• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.73-76
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• 2000

We investigate the stepwise parallel processing of the serial search which can success the co]1 search at low Ec/Io. The single path Rayleigh fading channel which is worst-case channel model is considered. The typical 3-step cell search is used. The probabilities of detection, miss and false alarm for each step are used in closed forms based on the statistics of CDMA noncoherent demodulator output. The optimal power allocation to each channel and The optimal number of post-detection integrations for each step is obtained. Also, the cumulative probability distribution of the average eel] search time for serial search methods are compared.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.12
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• pp.1903-1909
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• 1989

In this paper, the mobile communication CH simulator is implemented in the baseband, using the Digital Signal Processor(TMS320C25), A/D and D/A converters. The Rayleigh CH is modeled by shaping the random noise source power spectrum. The statistical characteristics(Level Crossing Rate, Cumulative distribution Function, Probability Density Function) and the received fading signal's power's spectrum is observed when the doppler frequency is varied according to the variation of the vehicular velocity at the 222MHz band. And also the BER is measured when the baseband mobile CH simulator is applied to the GMSK(Gaussian Minimum Shift Keying` transmission rate: 16kbps, Bb T=0.25) modulator. The results shows the similar characteristics to be compared with the theoritically derived BER values of the discriminator type GMSK detection.

• Journal of Korea Water Resources Association
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• v.47 no.8
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• pp.671-684
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• 2014

Change point analysis is a efficient tool to understand the fundamental information in hydro-meteorological data such as rainfall, discharge, temperature etc. Especially, this fundamental information to change points to future rainfall data identified by reasonable detection skills can affect the prediction of flood and drought occurrence because well detected change points provide a key to resolve the non-stationary or inhomogeneous problem by climate change. Therefore, in this study, the comparative study to assess the performance of the 3 change point detection skills, cumulative sum (CUSUM) method, Bayesian change point (BCP) method, and segmentation by dynamic programming (DP) was performed. After assessment of the performance of the proposed detection skills using the 3 types of the synthetic series, the 2 reasonable detection skills were applied to the observed and future rainfall data at the 5 rainfall gauges in South Korea. Finally, it was suggested that BCP (with 0.9 posterior probability) could be best detection skill and DP could be reasonably recommended through the comparative study. Also it was suggested that BCP (with 0.9 posterior probability) and DP detection skills to find some change points could be reasonable at the North-eastern part in South Korea. In future, the results in this study can be efficiently used to resolve the non-stationary problems in hydrological modeling considering inhomogeneity or nonstationarity.

• Smart Structures and Systems
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• v.21 no.5
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• pp.705-713
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• 2018

High-speed rail (HSR) has been in operation and development in many countries worldwide. The explosive growth of HSR has posed great challenges for operation safety and ride comfort. Among various technological demands on high-speed trains, vibration is an inevitable problem caused by rail/wheel imperfections, vehicle dynamics, and aerodynamic instability. Ride comfort is a key factor in evaluating the operational performance of high-speed trains. In this study, online monitoring data have been acquired from an in-service high-speed train for condition assessment. The measured dynamic response signals at the floor level of a train cabin are processed by the Sperling operator, in which the ride comfort index sequence is used to identify the train's operation condition. In addition, a novel technique that incorporates salient features of Bayesian inference and time series analysis is proposed for outlier detection and change detection. The Bayesian forecasting approach enables the prediction of conditional probabilities. By integrating the Bayesian forecasting approach with time series analysis, one-step forecasting probability density functions (PDFs) can be obtained before proceeding to the next observation. The change detection is conducted by comparing the current model and the alternative model (whose mean value is shifted by a prescribed offset) to determine which one can well fit the actual observation. When the comparison results indicate that the alternative model performs better, then a potential change is detected. If the current observation is a potential outlier or change, Bayes factor and cumulative Bayes factor are derived for further identification. A significant change, if identified, implies that there is a great alteration in the train operation performance due to defects. In this study, two illustrative cases are provided to demonstrate the performance of the proposed method for condition assessment of high-speed trains.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.38 no.4
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• pp.1-10
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• 2001

The 3-step cell search has been considered for fast acquisition of the scrambling code unique to a cell in the W -CDMA system. In this paper, the performance of the cell search scheme is analyzed in Rayleigh fading channels. And the system parameters for cell search scheme and the design parameters for the receivers are examined. The probabilities of detection, miss and false alarm for each step are derived in closed forms based on the statistics of CDMA noncoherent demodulator output. Through the analysis, the effect of threshold setting and post detection integration for each step is investigated, and the optimal values of the power allocation for the synchronization channels are also considered. The number of post-detection integrations for each step is a design parameter for the receiver, and the optimum values may depend on not only the power allocation for each channel related to the cell search, but the false alarm penalty time. It is shown that optimal values could be determined through the analysis. Also, the cumulative probability distribution of the average cell search time is obtained.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.107-114
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• 2010

A guitar, a piano, and a violin are popular musical instruments for User Created Contents(UCC). However the patterns of audio signal generated by a guitar and a piano are too similar to differentiate. The difference between two musical instruments can be found by analyzing the frequency variation per each band near signal peaks. The distribution of probability on the existence of signal peaks based on Cumulative Histogram were applied to musical instrument recognition. Experiments with statistical models of the frequency variation per each band near signal peaks showed the 14% improvement of musical instrument recognition.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.2
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• pp.200-207
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• 2016

To add new groundwater standard, 7 candidate materials (copper, zinc, selenium, manganese, iron, chromium, aluminum) were calculated by CROWN (Chemical Ranking Of groundwater pollutaNts). Copper and zinc were selected as groundwater candidates through the process and monitored total 430 samples for 2 years with 113 groundwater sampling sites. In this study, geological distribution characteristics (igneous rock, metamorphic rock, sedimentary rock) of copper and zinc were evaluated and the geological background levels obtained by a cumulative probability distribution and pre-selection methods were compared. In the results, the highest average concentrations of the copper and zinc were observed both in the igneous rock. The detection concentration ranges of copper and zinc in 430 groundwater samples were $0.002{\sim}0.931mg\;L^{-1}$, and $0.002{\sim}32.293mg\;L^{-1}$, respectively. In addition, detection concentration ranges of copper and zinc were $0.002{\sim}0.931mg\;L^{-1}$, $0.002{\sim}32.293mg\;L^{-1}$ in the igneous rock, $0.002{\sim}0.134mg\;L^{-1}$, $0.004{\sim}7.038mg\;L^{-1}$ in the metamorphic rock and $0.002{\sim}0.008mg\;L^{-1}$, $0.003{\sim}3.948mg\;L^{-1}$ in the sedimentary rock, respectively. As a result of the background concentrations with two methods, zinc concentrations with the pre-selected method are comparatively higher than that of the others with the cumulative distribution.

• Journal of the Korea Society for Simulation
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• v.23 no.1
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• pp.33-42
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• 2014

In this paper we analyzed the search patterns for the anti-submarine warfare (ASW) surface ships cooperating with ASW helicopters. For this purpose, we modeled evasive motion of a submarine with a probabilistic method. And maneuvers and search actions of ships and helicopters participating in the anti-submarine search mission are designed. And for each simulation scenario, the case where a ship and a helicopter searches a submarine independently according to its optimized search pattern is compared with the case where the search platforms participate in the ASW mission cooperatively. Based on the simulation results, we proposed the reconfigured search patterns that help cooperative ASW surface ships increase the total cumulative detection probability (CDP).

• Smart Structures and Systems
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• v.31 no.1
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• pp.57-68
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• 2023

Bridge hangers, such as those in suspension and cable-stayed bridges, suffer from cumulative fatigue damage caused by dynamic loads (e.g., cyclic traffic and wind loads) in their service condition. Thus, the identification of damage to hangers is important in preserving the service life of the bridge structure. This study develops a new method for condition assessment of bridge hangers. The tension force of the bridge and the damages in the element level can be identified using the Bayesian optimization method. To improve the number of observed data, the additional mass method is combined the Bayesian optimization method. Numerical studies are presented to verify the accuracy and efficiency of the proposed method. The influence of different acquisition functions, which include expected improvement (EI), probability-of-improvement (PI), lower confidence bound (LCB), and expected improvement per second (EIPC), on the identification of damage to the bridge hanger is studied. Results show that the errors identified by the EI acquisition function are smaller than those identified by the other acquisition functions. The identification of the damage to the bridge hanger with various types of boundary conditions and different levels of measurement noise are also studied. Results show that both the severity of the damage and the tension force can be identified via the proposed method, thereby verifying the robustness of the proposed method. Compared to the genetic algorithm (GA), particle swarm optimization (PSO), and nonlinear least-square method (NLS), the Bayesian optimization (BO) performs best in identifying the structural damage and tension force.

• Atmosphere
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• v.29 no.3
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• pp.257-267
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• 2019

This study is to assess the applicability of the Extreme Forecast Index (EFI) algorithm of the ECMWF seasonal forecast system to the Global Seasonal Forecasting System version 5 (GloSea5), operational seasonal forecast system of the Korea Meteorological Administration (KMA). The EFI is based on the difference between Cumulative Distribution Function (CDF) curves of the model's climate data and the current ensemble forecast distribution, which is essential to diagnose the predictability in the extreme cases. To investigate its applicability, the experiment was conducted during the heat-wave cases (the year of 1994 and 2003) and compared GloSea5 hindcast data based EFI with anomaly data of ERA-Interim. The data also used to determine quantitative estimates of Probability Of Detection (POD), False Alarm Ratio (FAR), and spatial pattern correlation. The results showed that the area of ERA-Interim indicating above 4-degree temperature corresponded to the area of EFI 0.8 and above. POD showed high ratio (0.7 and 0.9, respectively), when ERA-Interim anomaly data were the highest (on Jul. 11, 1994 (> $5^{\circ}C$) and Aug. 8, 2003 (> $7^{\circ}C$), respectively). The spatial pattern showed a high correlation in the range of 0.5~0.9. However, the correlation decreased as the lead time increased. Furthermore, the case of Korea heat wave in 2018 was conducted using GloSea5 forecast data to validate EFI showed successful prediction for two to three weeks lead time. As a result, the EFI forecasts can be used to predict the probability that an extreme weather event of interest might occur. Overall, we expected these results to be available for extreme weather forecasting.