• KSTLE International Journal
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• v.6 no.1
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• pp.1-7
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• 2005

Wear model for predicting the vehaviour of a depth is considered in this paper. It is deduced from the energy and volume based wear models such as the Archard equation and the workrate model. A new parameter of the equivalent depth ($D_e$= wear volume /worn area) is considered for the wear model of a depth prediction. A concenpt of a dissipated shear energy density is accommodated for in the suggested models. It is found that $D_e$ can distinguish the worn area shape. A cubic of $D_e$($D_e^3$) gives a better linear regression with the volume than that of the maximmum depth $D_{max}e$($D_{max}^3$) does. Both $D_{max}$ and $D_e$ are used for the presently suggested depth-based wear model. As a result, a wear depth profile can be simulated by a model using $D_{max}$. Wear resistance from the concern of an overall depth can be identified by the wear coefficient of the model using $D_e$.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.368-372
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• 2009

Pin-hole model has been widely used as a robust tool for easily understanding how to obtain a stereo image and how to present the depth-cue to an observer in stereoscopy. However, most of the processes to analyze depth cue in stereoscopy are performed that a stereo image is taken by camera model practically but depth cue of the image is analyzed by pin-hole model. Therefore, the result of depth cues by the process to be uncorrected. The reason of the uncorrected depth cue is led to the image distances of camera model due to variable focused object distances, and it makes a depth distortion. In this paper, we tried to show the contradiction such as occurring depth distortion in the process which the pin-hole model is used to analyze depth cue despite practical camera model is used in stereoscopy, and we presents the method to overcome the contradiction.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.4
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• pp.362-368
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• 2006

A pot experiment was conducted to investigate the effects of soil burial depth on seedling emergences of rice (Oryza sativa) and Echinochloa spp. and to model such effects for mathematical prediction of seedling emergences. When the Gompertz curve was fitted at each soil depth, the parameter C decreased in a logistic form with increasing soil depth, while the parameter M increased in an exponential form and the parameter B appeared to be constant. The Gompertz curve was combined by incorporating the logistic model for the parameter C, the exponential model for the parameter M, and the constant for the parameter B. This combined model well described seedling emergence of rice and Echinochloa species as influenced by soil burial depth and predicted seedling emergence at a given time after sowing and a soil burial depth. Thus, the combined model can be used to simulate seedling emergence of crop sown in different soil depths and weeds present in various soil depths.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.141-150
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• 2013

This study proposes technology using Active Shape Model to track the object separating it by depth-sensors. Unlike the common visual camera, the depth-sensor is not affected by the intensity of illumination, and therefore a more robust object can be extracted. The proposed algorithm removes the horizontal component from the information of the initial depth map and separates the object using the vertical component. In addition, it is also a more efficient morphology, and labeling to perform image correction and object extraction. By applying Active Shape Model to the information of an extracted object, it can track the object more robustly. Active Shape Model has a robust feature-to-object occlusion phenomenon. In comparison to visual camera-based object tracking algorithms, the proposed technology, using the existing depth of the sensor, is more efficient and robust at object tracking. Experimental results, show that the proposed ASM-based algorithm using depth sensor can robustly track objects in real-time.

• Computers and Concrete
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• v.26 no.6
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• pp.497-504
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• 2020

Concrete can lose its alkalinity by concrete carbonation causing steel corrosion. Thus, the determination of the carbonation depth is necessary. An empirical model is proposed in this research to predict the carbonation depth of concrete using Gene expression programming (GEP). The GEP model was trained and validated using a large and reliable database collected from the literature. The model was developed using the six parameters that predominantly control the carbonation depth of concrete including carbon dioxide CO2 concentration, relative humidity, water-to-cement ratio, maximum aggregate size, aggregate to binder ratio and carbonation period. The model was statistically evaluated and then compared to the Jiang et al. model. A parametric study was finally performed to check the proposed GEP model's sensitivity to the selected input parameters.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.93-98
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• 2001

This study was performed to analyse the rainfall and the rainfall-runoff characteristics of a rural watershed. The Sangwha basin($105.9km^{2}$) in the Geum river system was selected for this study. The arithmetic mean method, the Thiessen's weighing method, and the isohyetal method were used to analyse areal rainfall distribution and the Huff's quartile method was used to analyse temporal rainfall distribution. In addition, daily runoff analyses were peformed using the DAWAST and tank model. In the model calibration, the data from June through November, 1999 were used. In the model calibration, the observed runoff depth was 513.7mm and runoff rate was 45.2%, and the DAWAST model simulated runoff depth was 608.6mm and runoff rate was 53.5%, and the tank model runoff depth was 596.5mm and runoff rate was 52.5%, respectively. In the model test, the data from June through November, 2000 were used. In the model test, the observed runoff depth was 1032.3mm and runoff rate was 72.5%, and the DAWAST model simulated runoff depth was 871.6mm and runoff rate was 61.3%, and the tank model runoff depth was 825.4mm and runoff rate was 58%, respectively. The DAWAST and tank model's $R^{2}$ and RMSE were 0.85, 3.61mm, and 0.85, 2.77mm in 1999, and 0.83, 5.73mm, and 0.87, 5.39mm in 2000, respectively. Both models predicted low flow runoff better than flood runoff.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.35-41
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• 2022

Heavy snow damage can be prevented in advance with an appropriate security system. To develop the security system, we developed a model that predicts snow depth after a few hours when the snow depth is observed, and utilized it to calculate a failure probability with various types of greenhouses and observed snow depth data. We compared the Markov chain model and Bayesian long short-term memory models with varying input data. Markov chain model showed the worst performance, and the models that used only past snow depth data outperformed the models that used other weather data with snow depth (temperature, humidity, wind speed). Also, the models that utilized 1-hour past data outperformed the models that utilized 3-hour data and 6-hour data. Finally, the Bayesian LSTM model that uses 1-hour snow depth data was selected to predict snow depth. We compared the selected model and the shifting method, which uses present data as future data without prediction, and the model outperformed the shifting method when predicting data after 11-24 hours.

• Atmosphere
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• v.22 no.1
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• pp.117-128
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• 2012

Does snow depth initialization have a quantitative impact on sub-seasonal to seasonal prediction skill? To answer this question, a snow depth initialization technique for seasonal forecast system has been implemented and the impact of the initialization on the seasonal forecast of surface air temperature during the wintertime is examined. Since the snow depth observation can not be directly used in the model simulation due to the large systematic bias and much smaller model variability, an anomaly rescaling method to the snow depth initialization is applied. Snow depth in the model is initialized by adding a rescaled snow depth observation anomaly to the model snow depth climatology. A suite of seasonal forecast is performed for each year in recent 12 years (1999-2010) with and without the snow depth initialization to evaluate the performance of the developed technique. The results show that the seasonal forecast of surface air temperature over East Asian region sensitively depends on the initial snow depth anomaly over the region. However, the sensitivity shows large differences for different timing of the initialization and forecast lead time. Especially, the snow depth anomaly initialized in the late winter (Mar. 1) is the most effective in modulating the surface air temperature anomaly after one month. The real predictability gained by the snow depth initialization is also examined from the comparison with observation. The gain of the real predictability is generally small except for the forecasting experiment in the early winter (Nov. 1), which shows some skillful forecasts. Implications of these results and future directions for further development are discussed.

• Computers and Concrete
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• v.31 no.3
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• pp.241-252
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• 2023

Corrosion of rebar is one of the major deteriorating mechanisms that affect the durability of reinforced concrete (RC) structures. The increase in CO₂ concentration in the atmosphere leads to early carbonation and deterioration due to corrosion in RC structures. In the present study, an attempt has been made to modify the existing carbonation depth prediction empirical model. The modified empirical model is verified from the carbonation data collected from selected RC structures of CSIR-SERC campus, Chennai and carbonation data available from the reported literature on in-situ RC structures. Attempt also made to study the carbonation depth in the laboratory specimens using oxygen permeability index (OPI) test. The carbonation depth measured from RC structures and laboratory specimens are compared with estimated carbonation depth obtained from OPI test data. The modified empirical model shows good correlation with measured carbonation depth from the identified RC structures and the reported RC structures from the literature. The carbonation depth estimated from OPI values for both in-situ and laboratory specimens show lesser percentage of error compared to measured carbonation depth. From the present investigation it can be said that the OPI test is the suitable test method for both new and existing RC structures and laboratory RC specimens.

• Journal of the Ergonomics Society of Korea
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• v.22 no.1
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• pp.17-29
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• 2003

This study was initiated to develop a methodology for devising Korean virtual models for apparel shopping at internet shopping site. The data base for this study was the Korean National Anthropometric Survey in 1997. The subjects were 493 women and 626 men in 20's. The researchers also measured 88 males and females in age 20's to suggest back and front depth proportions which are not available from the survey. The virtual models' figure types were classified by the heights, drop value and bust(chest) girth. It was evaluated whether it is needed to separate figure type with bust(chest) girth. The body sizes of virtual models were suggested for side view model and front view model in 13cm height. Four female virtual models were suggested for front view and side view. Eight male virtual models were suggested for front view and side view. Each virtual model's height, breadth and depth proportions were calculated. Shoulder breadth. Bust(chest) breadth, waist breadth, hip breadth and proportions were calculated for front view model. The bust(chest) depth, waist depth, abdomen depth, hip depth and proportions were calculated for side view model. Height proportions were suggested for female and male virtual models.