• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.1-10
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• 2022

Attempts to interpret human perspectives using computer vision have been developed in various fields. In this paper, we propose a method for evaluating the walking environment through semantic segmentation results of images from road images. First, the Kakao Map API was used to collect road images, and four-way images were collected from about 50,000 points in JeonJu. 20% of the collected images build datasets through crowdsourcing-based paired comparisons, and train various regression models using paired comparison data. In order to derive the walkability score of the image data, the ranking score is calculated using the Trueskill algorithm, which is a ranking algorithm, and the walkability and analysis using various regression models are performed using the constructed data. Through this study, it is shown that the walkability of Jeonju can be evaluated and scores can be derived through the correlation between pixel distribution classification information rather than human vision.

• The Journal of the Acoustical Society of Korea
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• v.30 no.1
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• pp.56-62
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• 2011

In the present study, the phase velocities of guided ultrasonic waves such as the first arriving signal (FAS) and the slow guided wave (SGW) propagating along the long axis on the 12 tubular cortical bone samples in vitro were measured and their correlations with the cortical thickness were investigated. The phase velocities of the FAS and the SGW were measured by using the axial transmission method in air with a pair of unfocused ultrasonic transducers with a diameter of 12.7 mm and a center frequency of 200 kHz. The phase velocity of the FAS measured at 200 kHz exhibited a very high negative correlation with the cortical thickness and that of the SGW arriving after the FAS showed a high positive correlation with the cortical thickness. The simple and multiple linear regression models with the phase velocities of the FAS and the SGW as independent variables and the cortical thickness as a dependent variable revealed that the coefficient of determination of the multiple linear regression model was higher than those of the simple linear regression models. The phase velocities of the FAS and the SGW measured at 200 kHz on the 12 tubular cortical bone samples were, respectively, consistent with those of the S0 and the A0 Lamb modes calculated at 200 kHz on the cortical bone plate.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.6
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• pp.530-538
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• 2014

The weight estimation of floating offshore structures such as FPSO, TLP, semi-Submersibles, Floating Offshore Wind Turbines etc. in the preliminary design, is one of important measures of both construction cost and basic performance. Through both literature investigation and internet search, the weight data of floating offshore structures such as FPSO and TLP was collected. In this study, the weight estimation model was suggested for FPSO. The weight estimation model using non-linear regression analysis was established by fixing independent variables based on this data and the multiple regression analysis was introduced into the weight estimation model. Its reliability was within 4% of error rate.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.181-194
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• 2017

This study aims to develop a regression model for forest volume estimation using field-collected forest inventory information and airborne LiDAR data. The response variable of the model is forest stem volume, was measured by random sampling from each individual plot of the 30 circular sample plots collected in Bonghwa-gun, Gyeong sangbuk-do, while the predictor variables for the model are Height Percentiles(HP) and Height Bin(HB), which are metrics extracted from raw LiDAR data. In order to find the most appropriate model, the candidate models are constructed from simple linear regression, quadratic polynomial regression and multiple regression analysis and the cross-validation tests were conducted for verification purposes. As a result, $R^2$ of the multiple regression models of $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}$ among the estimated models was the highest at 0.509, and the PRESS statistic of the simple linear regression model of $HP_{25}$ was the lowest at 122.352. $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}-based$ models, thus, are comparatively considered more appropriate for Korean forests with complicated vertical structures.

• Journal of the Korean Society of Propulsion Engineers
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• v.20 no.2
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• pp.56-66
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• 2016

Robustness is essential for model based FDI (Fault Detection and Isolation) and it is inevitable to have modeling errors and sensor signal noises during the process of FDI. This study suggests an improved method by applying NARX (Nonlinear Auto Regressive eXogenous) model and Kalman estimator in order to cope with problems caused by linear model errors and sensor signal noises in the process of fault diagnoses. Fault decision is made by the probability of the trend of gradually accumulated errors applying Fuzzy logic, which are robust to instantaneous sensor signal noises. Reliability of fault diagnosis is verified under various fault simulations.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.135-147
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• 2017

We, for the first time, estimated daily and monthly surface nitrogen dioxide ($NO_2$) volume mixing ratio (VMR) using three regression models with $NO_2$ tropospheric vertical column density (OMIT-rop $NO_2$ VCD) data obtained from Ozone Monitoring Instrument (OMI) in Seoul in South Korea at OMI overpass time (13:45 local time). First linear regression model (M1) is a linear regression equation between OMI-Trop $NO_2$ VCD and in situ $NO_2$ VMR, whereas second linear regression model (M2) incorporates boundary layer height (BLH), temperature, and pressure obtained from Atmospheric Infrared Sounder (AIRS) and OMI-Trop $NO_2$ VCD. Last models (M3M & M3D) are a multiple linear regression equations which include OMI-Trop $NO_2$ VCD, BLH and various meteorological data. In this study, we determined three types of regression models for the training period between 2009 and 2011, and the performance of those regression models was evaluated via comparison with the surface $NO_2$ VMR data obtained from in situ measurements (in situ $NO_2$ VMR) in 2012. The monthly mean surface $NO_2$ VMRs estimated by M3M showed good agreements with those of in situ measurements(avg. R = 0.77). In terms of the daily (13:45LT) $NO_2$ estimation, the highest correlations were found between the daily surface $NO_2$ VMRs estimated by M3D and in-situ $NO_2$ VMRs (avg. R = 0.55). The estimated surface $NO_2$ VMRs by three modelstend to be underestimated. We also discussed the performance of these empirical modelsfor surface $NO_2$ VMR estimation with respect to otherstatistical data such asroot mean square error (RMSE), mean bias, mean absolute error (MAE), and percent difference. This present study shows a possibility of estimating surface $NO_2$ VMR using the satellite measurement.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.38-39
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• 2019

Environmental problems caused by GHG emitted by various industries are emerging around the world, and accordingly, relevant regulations are being applied by countries around the world. Korea is operating a carbon credit system that trades GHG in industry for money, which is expected to be applied to the construction industry. In addition, construction equipment using fossil fuels accounts for the largest portion of $CO_2$ emissions in the construction industry, and the importance of $CO_2$ reduction and prediction is increasing. However, there is a lack of data on the directly measured $CO_2$ emissions of construction equipment and there is no accurate methodology for measuring methods. Therefore, in this study, independent variables were derived based on the $CO_2$ emission data. In addition, multiple linear regression is performed for each independent variable to derive a predictive model of carbon dioxide emission by work type of construction equipment. It is expected that the construction process plan based on environmental factors in the construction industry can be established in the future.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.10a
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• pp.170-173
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• 1998

본 연구에서는 시뮬레이터나 그와 유사한 가상현실환경(Virtual Reality Environment ; VRE)에서 일어날 수 있는 Simulator Sickness가 어떤 사람들에게 쉽게 발생하는지를 예측하기 위하여 다중선형회귀(Multiple linear regression) 방정식으로 예측회귀모형을 제시하였다. 이 회귀모형에서의 종속변수는 김도희 외(1998)에 의해 개발된 RSSQ의 종합점수이고, 독립변수는 실제운전경력에 1을 더한 값에 나이를 곱한 값, 과거 멀미를 경험한 정도, 1주일 평균 동화상 시간, 현재의 건강상태로 되어져 있다. 이 회귀모형의 R2값은 약 0.52로 Kolasinski(1996)의 모델보다 설명력이 18% 증가하였고, 부수적인 별도의 실험을 하지 않고도 간단한 개인 신상에 관한 간단한 자료만으로도 훨씬 좋은 결과를 예측할 수 있게 되었다. 따라서 시뮬레이터나 가상현실에서 일어나는 Simulator Sickness가 어떠한 사람에게 걸리기가 쉬운지를 쉽게 예측할 수 있게 되었고, 이러한 사람들에게는 시뮬레이터나 가상현실의 이용을 자제시키거나 주의를 주어 특별관리 함으로써 시뮬레이터나 가상현실을 운영하는데 많은 도움을 줄 수 있을 것이다.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.5
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• pp.502-508
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• 2012

Quantitative ultrasound technologies for osteoporosis diagnosis measure ultrasonic parameters such as speed of sound(SOS) and normalized broadband ultrasound attenuation(nBUA) in the calcaneus (heel bone). In the present study, the dependences of SOS and nBUA on bone mineral density in the proximal femur with high risk of fracture were investigated by using 20 trabecular bone samples extracted from bovine femurs. SOS and nBUA in the femoral trabecular bone samples were measured by using a transverse transmission method with one matched pair of ultrasonic transducers with a center frequency of 1.0 MHz. SOS and nBUA measured in the 20 trabecular bone samples exhibited high Pearson's correlation coefficients (r) of r = 0.83 and 0.72 with apparent bone density, respectively. The multiple regression analysis with SOS and nBUA as independent variables and apparent bone density as a dependent variable showed that the correlation coefficient r = 0.85 of the multiple linear regression model was higher than those of the simple linear regression model with either parameter SOS or nBUA as an independent variable. These high linear correlations between the ultrasonic parameters and the bone density suggest that the ultrasonic parameters measured in the femur can be useful for predicting the femoral bone mineral density.

• The Journal of the Acoustical Society of Korea
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• v.21 no.1
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• pp.11-18
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• 2002

In this paper, a gain-adapted speech recognition method in noise is developed in the time domain. Noise is assumed to be colored. To cope with the notable nonstationary nature of speech signals such as fricative, glides, liquids, and transition region between phones, the nonstationary autoregressive (NAR) hidden Markov model (HMM) is used. The nonstationary AR process is represented by using polynomial functions with a linear combination of M known basis functions. When only noisy signals are available, the estimation problem of noise inevitably arises. By using multiple Kalman filters, the estimation of noise model and gain contour of speech is performed. Noise estimation of the proposed method can eliminate noise from noisy speech to get an enhanced speech signal. Compared to the conventional ARHMM with noise estimation, our proposed NAR-HMM with noise estimation improves the recognition performance about 2-3%.