• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.391-399
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• 2012

Multipath remains a dominant source of ranging errors in GNSS (Global Navigation Satellite System). And it is generally considered undesirable in the context of GNSS, since the reception of multipath can make significant distortion to the shape of the correlation function. In this paper, therefore, the model of the distorted shape of the correlation function is formulated and a MBME (Model-Based Multipath Estimation) technique for GPS L1/L5 receivers is proposed in order to estimate the parameters of the indirect signal such as the amplitude and the delay. The MBME technique does not require the any hardware modifications and it can estimate the parameters for both the short and long-delay multipath. Especially, it would be the very effective technique for the short-delay multipath if the L5 signal is available. Finally, the feasibility of the proposed technique has been confirmed by simulation results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8C
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• pp.764-771
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• 2009

In this paper, we propose a fast multiple reference frame selection method using neighboring motion vectors and the correlation of inter-mode blocks in H.264 video coding. Using the property that an optimal reference frame has the statistical characteristics, the motion estimation of small Inter-block size is estimated from the motion estimation of the larger Inter-block size. Simulation results show that the proposal method decreased the computations about 91%. Without the sacrifice of coding performance, comparing to the H.264 multiple reference picture coding.

• Journal of the Society of Naval Architects of Korea
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• v.53 no.1
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• pp.76-83
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• 2016

The various model tests are carried out to estimate and verify a ship performance in the design stage. But in view of the cost, the model test should be applied to every project vessel is very inefficient. Therefore, other methods of predicting the maneuverability with confined data are required at the initial design stage. The purpose of this study is to estimate the hydrodynamic derivatives by using the multiple regression analysis and PMM test data. The characteristics of the stern shape which has an important effect on the maneuverability are applied to the regression analysis in this study. The correlation analysis is performed to select the proper hull form coefficients and stern shape factors used as the variables in the regression analysis. The comparative analysis of estimate results and model test results is conducted on two ships to investigate the effectiveness of the maneuvering hydrodynamic derivatives estimation applied the stern shape. Through the present study, it is verified that the estimation using the stern shape factors as the variables are valid when the stern shape factors are located in the center of the database.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.1065-1078
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• 2009

The shear strength has been managed as an important factor in soil mechanics. The shear strength estimation model was developed to evaluate the shear strength using only a few soil properties by the linear regression analysis model which is one of the statistical methods. The shear strength is divided into two part; one is the internal friction angle ($\Phi$) and the other is the cohesion (c). Therefore, some valid soil factors among the results of soil tests are selected through the correlation analysis using SPSS and then the model are formulated by the linear regression analysis based on the relationship between factors. Also, the developed model is compared with the result of direct shear test to prove the rationality of model. As the results of analysis about relationship between soil properties and shear strength, the internal friction angle is highly influenced by the void ratio and the dry unit weight and the cohesion is mainly influenced by the void ratio, the dry unit weight and the plastic index. Meanwhile, the shear strength estimated by the developed model is similar with that of the direct shear test. Therefore, the developed model may be used to estimate the shear strength of soils in the same condition of study area.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.119-124
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• 2015

Speech enhancement has been required as a preprocessor for a noise robust speech recognition system. Codebook-based Speech Enhancement (CBSE) is highly robust in nonstationary noise environments compared with conventional noise estimation algorithms. However, its performance is severely degraded for the codevector combinations that have lower correlation with the input signal since CBSE depends on the trained codebook information. To overcome this problem, only the reliable codevector combinations are selected to be used to remove the codevector combinations that have lower correlation with input signal. The proposed method produces the improved performance compared to the conventional CBSE in terms of Log-Spectral Distortion (LSD) and Perceptual Evaluation of Speech Quality (PESQ).

• Korean Journal of Remote Sensing
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• v.18 no.6
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• pp.319-326
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• 2002

Rainfall estimation was conducted based on TRMM-PR/VIES and GMS data. AWS rainfall data were used for various validation. General procedure is as follows; 1) Z-R relationship was made by the comparison of TRMM-PR and AWS data. 2) new algorithm was developed by the estimates from Z-R equation and TBB of VIRS. 3) rainfall was estimated through the substitution of GMS data for TBB of VIRS in the newly developed algorithm. Z-R relationship based on TRMM is $Z=303R^{0.72}$ with correlation coefficient 0.57. The newly developed algorithm is shown as correlation coefficient 0.67 and RMSE 17mm/hr. New algorithm shows the underestimating tendency in case of heavy rainfall event.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.67-74
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• 2019

Bone density is one of the factors in the early failure of dental implants and doctors should make a preoperative assessment of jaw bone density using patient's CT data before dental implant surgery in order to find out whether the patient has osteoporosis and osteopenia. The main goal of this study was to propose a method that based on image processing techniques in order to provide accurate information about where to drill and place an abutment screw of implants in the jaw bone for doctors and reduce human activity for the estimation of the local cancellous bone density of mandible using CT data. The experiment was performed on a computed tomography data of the jaw bone of two different individuals. We assumed that the result of the estimation of jaw bone density depends on the angle of drilling and average HU (Hounsfield Unit) values were used to evaluate the quality of local cancellous bone density of mandible. As a result of this study, we have been developed a toolbox that can be used to estimate jaw bone density automatically and found a positive correlation between the angle of the drill and time complexity but a negative correlation between the diameter of the drill and time complexity.

• Wind and Structures
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• v.33 no.6
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• pp.423-435
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• 2021

Although existing algorithms can predict wind speed using historical observation data, for engineering feasibility, most use moment methods and probability density functions to estimate fitted parameters. However, extreme wind speed prediction accuracy for long-term return periods is not always dependent on how the optimized frequency distribution curves are obtained; long-term return periods emphasize general distribution effects rather than marginal distributions, which are closely related to potential extreme values. Moreover, there are different wind speed parent sample types; how to theoretically select the proper extreme value distribution is uncertain. The influence of different sampling time intervals has not been evaluated in the fitting process. To overcome these shortcomings, updated steps are introduced, involving parameter sensitivity analysis for different sampling time intervals. The extreme value prediction accuracy of unknown parent samples is also discussed. Probability analysis of mean wind is combined with estimation of the probability plot correlation coefficient and the maximum likelihood method; an iterative estimation algorithm is proposed. With the updated steps and comparison using a Monte Carlo simulation, a fitting policy suitable for different parent distributions is proposed; its feasibility is demonstrated in extreme wind speed evaluations at Longhua and Chuansha meteorological stations in Shanghai, China.

• Structural Engineering and Mechanics
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• v.89 no.3
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• pp.283-300
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• 2024

Machine learning (ML) models based on artificial neural network (ANN) and decision tree (DT) were developed for estimation of axial capacity of concrete columns reinforced with fiber reinforced polymer (FRP) bars. Between the design codes, the Canadian code provides better formulation compared to the Australian or American code. For empirical models based on elastic modulus of FRP, Hadhood et al. (2017) model performed best. Whereas for empirical models based on tensile strength of FRP, as well as all empirical models, Raza et al. (2021) was adjudged superior. However, compared to the empirical models, all ML models exhibited superior performance according to all five performance metrics considered. The performance of ANN and DT models were comparable in general. Under the present setup, inclusion of the transverse reinforcement information did not improve the accuracy of estimation with either ANN or DT. With selective use of inputs, and a much simpler ANN architecture (4-3-1) compared to that reported in literature (Raza et al. 2020: 6-11-11-1), marginal improvement in correlation could be achieved. The metrics for the best model from the study was a correlation of 0.94, absolute errors between 420 kN to 530 kN, and the range being 0.39 to 0.51 for relative errors. Though much superior performance could be obtained using ANN/DT models over empirical models, further work towards improving accuracy of the estimation is indicated before design of FRP reinforced concrete columns using ML may be considered for design codes.

• Journal of the Society of Naval Architects of Korea
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• v.50 no.6
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• pp.373-382
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• 2013

The weight of floating offshore plant, such as an FPSO(Floating, Production, Storage, and Off-loading unit) and an offshore wind turbine, is important for estimating the amount of production material and for determining the production method. Furthermore, the weight is a factor which affects in the building cost and production time of the floating offshore plant. Although the importance of the weight has long been recognized, the weight has been roughly estimated by using the existing design and production data, and designer's experience. To solve this problem, a simplified model for the weight estimation of the floating offshore plant using the statistical method was proposed in this study. To do this, various data for estimating the weight of the floating offshore plant were collected through the literature survey, and then the correlation analysis and the multiple regression analysis were performed to generate the simplified model for the weight estimation. Finally, to examine the applicability of the developed model, it was applied to examples of the weight estimation of an FPSO topsides and an offshore wind turbine. As a result, it was shown that the developed model can be applied the weight estimation process of the floating offshore plant at the early design stage.