• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.57-67
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• 2018

The ensemble is a unified approach used for getting better performance by using multiple algorithms in machine learning. In this paper, we introduce boosting and bagging, which have been widely used in ensemble techniques, and design a method using support vector regression, radial basis function network, Gaussian process, and multilayer perceptron. In addition, our experiment was performed by adding a recurrent neural network and MOHID numerical model. The drifter data used for our experimental verification consist of 683 observations in seven regions. The performance of our ensemble technique is verified by comparison with four algorithms each. As verification, mean absolute error was adapted. The presented methods are based on ensemble models using bagging, boosting, and machine learning. The error rate was calculated by assigning the equal weight value and different weight value to each unit model in ensemble. The ensemble model using machine learning showed 61.7% improvement compared to the average of four machine learning technique.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.25-46
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• 2020

This paper presents ordinal probit semiparametric regression models using Bayesian Spectral Analysis Regression (BSAR) method. Ordinal probit regression is a way of modeling ordinal responses - usually more than two categories - by connecting the probability of falling into each category explained by a combination of available covariates using a probit (an inverse function of normal cumulative distribution function) link. The Bayesian probit model facilitates posterior sampling by bringing a latent variable following normal distribution, therefore, the responses are categorized by the cut-off points according to values of latent variables. In this paper, we extend the latent variable approach to a semiparametric model for the Bayesian ordinal probit regression with nonparametric functions using a spectral representation of Gaussian processes based BSAR method. The latent variable is decomposed into a parametric component and a nonparametric component with or without a shape constraint for modeling ordinal responses and predicting outcomes more flexibly. We illustrate the proposed methods with simulation studies in comparison with existing methods and real data analysis applied to a Korean National Health and Nutrition Examination Survey (KNHANES) 2016 for investigating nonparametric relationship between smoking behavior and coffee intake.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.111-122
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• 2015

This study purposed on introducing a new automated solution for detecting railway tracks and reconstructing track models from the mobile laser scanning data. The proposed solution completes following procedures; the study initiated with detecting a potential railway region, called Region Of Interest (ROI), and approximating the orientation of railway track trajectory with the raw data. At next, the knowledge-based detection of railway tracks was performed for localizing track candidates in the first strip. In here, a strip -referring the local track search region- is generated in the orthogonal direction to the orientation of track trajectory. Lastly, an initial track model generated over the candidate points, which were detected by GMM-EM (Gaussian Mixture Model-Expectation & Maximization) -based clustering strip- wisely grows to capture all track points of interest and thus converted into geometric track model in the tracking by detection framework. Therefore, the proposed railway track tracking process includes following key features; it is able to reduce the complexity in detecting track points by using a hypothetical track model. Also, it enhances the efficiency of track modeling process by simultaneously capturing track points and modeling tracks that resulted in the minimization of data processing time and cost. The proposed method was developed using the C++ program language and was evaluated by the LiDAR data, which was acquired from MMS over an urban railway track area with a complex railway scene as well.

• Journal of Digital Convergence
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• v.16 no.9
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• pp.413-419
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• 2018

Nowadays, smart phone became an important part of communication media and integrated into people's life. If callers rely on helmet-mounted display(HMD) augmented reality technique to add two-way user's facial expression, appearance, actions during the calling process, it will let callers have a visualized fantastic sensual experience. And through that method can break the limitations of vision, so research that technical problem can promote the development of visual arts, that is meaningful. This paper will choose and composite several existed technologies to set up two hypothesis, try to realize AR video calling. Through comparison and analysis to find those two hypothesis' problem, and create design solutions to solve problems. And use case study method to present two cases for prove my paper's result that is those two hypothesis can be realize in future. Use those technologies can bring more convenience and enjoyment to people's life. It can be predicted that AR video calling process can be successfully realized and will have unlimited development in future.

• Journal of the Korea Society for Simulation
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• v.17 no.3
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• pp.19-26
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• 2008

In this paper, we propose a dynamic prediction algorithm to predict the bond price using actual data set of treasure note (T-Note). The proposed algorithm is based on term structure model of the interest rates, which takes place in various financial modelling, such as the standard Gaussian Wiener process. To obtain cumulative distribution functions (CDFs) of actual data for the interest rate measurement used, we use the natural cubic spline (NCS) method, which is generally used as numerical methods for interpolation. Then we also use the random number generation scheme (RNGS) to calculate the pricing of bond through the obtained CDF. In empirical computer simulations, we show that the lower values of precision in the proposed prediction algorithm corresponds to sharper estimates. It is very reasonable on prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.178-203
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• 2018

In this paper, authors have been evaluated the Frame Error Rate (FER) performance of IEEE 802.11 a/g/p standard 5 GHz frequency band WLAN over Rayleigh and Rician distributed fading channels in presence of Additive White Gaussian Noise (AWGN). Orthogonal Frequency Division Multiplexing (OFDM) based transceiver is implemented by using real-time signal processing frameworks (IEEE 802.11 Blocks) in GNU Radio Companion (GRC) and Ettus USRP N200 is used to process the symbol over the wireless radio channel. The FER is calculated for each sub-carrier conventional modulation schemes used by OFDM such as BPSK, QPSK, 16, 64-QAM with different punctuated coding rates. More precise SNR is computed by modifying the SNR calculation process of YANS and NIST error rate model to estimate more accurate FER. Here, real-time signal constellations, OFDM signal spectrums etc. are also observed to find the effect of multipath propagation of signals through flat and frequency selective fading channels. To reduce the error rate due to the multipath fading effect and Doppler shifting, channel estimation (CE) and equalization techniques such as Least Square (LS) and training based adaptive Least Mean Square (LMS) algorithm are applied in the receiver. The simulation work is practically verified at GRC by turning into a pair of Software Define Radio (SDR) as a simultaneous transceiver.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.10
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• pp.798-805
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• 2018

This study introduces an adaptive design of experiment (DoE) approach for the hypersonic shock-tunnel testing. A series of experiments are conducted to model the pitch moment coefficient of a cone as the function of the angle of attack and the pitch rate. An algorithm to construct the trajectory of the test model from the images obtained by the high-speed camera is developed to effectively analyze multiple time series experimental data. An adaptive DoE procedure to determine the experimental point based on the analysis results of the past experiments using the algorithm is proposed.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.56-62
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• 2001

In this paper, we adopt a missing data theory to speech recognition. It can be used in order to maintain high performance of speech recognizer when the missing data occurs. In general, hidden Markov model (HMM) is used as a stochastic classifier for speech recognition task. Acoustic events are represented by continuous probability density function in continuous density HMM(CDHMM). The missing data theory has an advantage that can be easily applicable to this CDHMM. A marginalization method is used for processing missing data because it has small complexity and is easy to apply to automatic speech recognition (ASR). Also, a spectral subtraction is used for detecting missing data. If the difference between the energy of speech and that of background noise is below given threshold value, we determine that missing has occurred. We propose a new method that examines the reliability of detected missing data using voicing probability. The voicing probability is used to find voiced frames. It is used to process the missing data in voiced region that has more redundant information than consonants. The experimental results showed that our method improves performance than baseline system that uses spectral subtraction method only. In 452 words isolated word recognition experiment, the proposed method using the voicing probability reduced the average word error rate by 12％ in a typical noise situation.

• Journal of Korean Society of Disaster and Security
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• v.6 no.2
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• pp.23-30
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• 2013

A dynamic analysis of random vibration processes is concerned with the first excursion probability based on first passage time during some specified lifetime or duration of the excitation. This study is concerned with the estimation of first-passage probability for hazard fluctuate wind velocity in the major cities reflecting the recent meteorological with largest data samples (yearly 2003-2012). The basic wind speeds were standardized homogeneously to the surface roughness category C, and to 10m above the ground surface. In this paper, the hazard fluctuate wind velocities are treated as a time-independent (stationary) random process and Gaussian random processes. The first excursion probability were calculated from Poisson model based on the independent event of level crossing & two-state Markov model based on the envelopes of level crossing.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.