Many case histories of re-liquefaction phenomena seem to support the idea that sand deposits, if they once have been liquefied, could be reliquefied again by a subsequent earthquake even though the earthquake is smaller than the previous one. The magnitude of the strains induced in the initial liquefaction has a significant influence on the resistance of the sample to re-liquefaction. The deposits undergoing liquefaction experience large shear strain during liquefaction. And this previous strain changes the microstructure into highly anisotropic structure such as columnlike structure and connected voids. This type of anisotropy is so unstable that it can reduce re-liquefaction resistance. It is blown that the extent of anisotropic structural change depends on the gradation characteristics of ground. The purpose of this study is to estimate the correlation between the gradation characteristics of the sand and the ratio of re-liquefaction resistance to liquefaction resistance. In this study, 1-g shaking table tests were carried out on five different kinds of sands. During the tests the values of excess pore pressure at various depths and surface settlements were measured. Re-liquefaction resistances were not affected by the initial void ratio and the effective confining pressures, and the deposits of all test sands which had once been liquefied were reliquefied in the cyclic loading number below 1 to 1.5. The ratio of re-liquefaction resistance to liquefaction resistance linearly decreased as $D_{10}/C_u$ increased, and was constant as about 0.2 above the value of $D_{10}/C_u$, 0.15 mm.
This study analyzed the geometric, compressive, cutting and friction properties of oriental melons in order to design a gripper capable of soft handling and a cutter for cutting oriental melon vine among the end effector of oriental melon as a preliminary step for developing the end effector of the robot capable of harvesting oriental melons in protected cultivation. As a result, the average length, diameter at the midpoint, weight, volume and roundness of the oriental melons were 108 mm, 70 mm, 188 g, 333 mL and 3.8 mm. Nonlinear regression analysis was performed on the equation $W=L^a{\times}D_2^b$ with variation of the length (L) and diameter (D2) of the weight (W) of the oriental melons. As a result, it was shown that there was a correlation between a of 2.0279 and b of -0.9998 as a constant value. The average diameter of the oriental melon vine was 3.8 mm, and most vines were distributed within a radius of 5 mm from the center. The average yield value, compressive strength and hardness of the oriental melons were $36.5N/cm^2$, $185.7N/cm^2$ and $636.7N/cm^2$, respectively. The average cutting force and shear strength of the oriental melon vines were $2.87{\times}10^{-2}\;N$ and $5.60N/cm^2$, respectively. The maximum friction coefficient of the oriental melons was rubber of 0.609, followed by aluminium of 0.393, stainless steel of 0.177 and teflon of 0.079. It was considered possible to apply it to the size of the gripper and cutter, turning radius, dynamics of drive motor and selection of materials and their quality in light of the position error and safety factor according to the movement when designing end effector based on the analyzed data.
In many solute transport studies, either flux or resident concentration has been used. Choice of the concentration mode was dependent on the monitoring device in solute displacement experiments. It has been accepted that no priority exists in the selection of concentration mode in the study of solute transport. It would be questionable, however, to accept the equivalency in the solute transport parameters between flux and resident concentrations in structured soils exhibiting preferential movement of solute. In this study, we investigate how they differ in the monitored breakthrough curves (BTCs) and transport parameters for a given boundary and flow condition by performing solute displacement experiments on a number of undisturbed soil columns. Both flux and resident concentrations have been simultaneously obtained by monitoring the effluent and resistance of the horizontally-positioned TDR probes. Two different solute transport models namely, convection-dispersion equation (CDE) and convective lognormal transfer function (CLT) models, were fitted to the observed breakthrough data in order to quantify the difference between two concentration modes. The study reveals that soil columns having relatively high flux densities exhibited great differences in the degree of peak concentration and travel time of peak between flux and resident concentrations. The peak concentration in flux mode was several times higher than that in resident one. Accordingly, the estimated parameters of flux mode differed greatly from those of resident mode and the difference was more pronounced in CDE than CLT model. Especially in CDE model, the parameters of flux mode were much higher than those of resident mode. This was mainly due to the bypassing of solute through soil macropores and failure of the equilibrium CDE model to adequate description of solute transport in studied soils. In the domain of the relationship between the ratio of hydrodynamic dispersion to molecular diffusion and the peclet number, both concentrations fall on a zone of predominant mechanical dispersion. However, it appears that more molecular diffusion contributes to the solute spreading in the matrix region than the macropore region due to the nonliearity present in the pore water velocity and dispersion coefficient relationship.
Journal of Korean Society of Coastal and Ocean Engineers
/
v.31
no.1
/
pp.28-39
/
2019
In the design process of counter measures against the beach erosion, information like the main sediment transport mode and yearly net amount of longshore and cross shore transport is of great engineering value. In this rationale, we numerically analyzed the yearly sediment budget of the Mang-Bang beach which is suffering from erosion problem. For the case of cross sediment transport, Bailard's model (1981) having its roots on the Bagnold's energy model (1963) is utilized. In doing so, longshore sediment transport rate is estimated based on the assumption that longshore transport rate is determined by the available wave energy influx toward the beach. Velocity moments required for the application of Bailard's model (1981) is deduced from numerical simulation of the nonlinear shoaling process over the Mang-Bang beach of the 71 wave conditions carefully chosen from the wave records. As a wave driver, we used the consistent frequency Boussinesq Eq. by Frelich and Guza (1984). Numerical results show that contrary to the Bailard's study (1981), Irribaren NO. has non negligible influence on the velocity moments. We also proceeds to numerically simulate the yearly sediment budget of Mang-Bang beach. Numerical results show that for ${\beta}=41.6^{\circ}$, the mean orientation of Mang-Bang beach, north-westwardly moving longshore sediment is prevailing over the south-eastwardly moving sediment, the yearly amount of which is simulated to reach its maxima at $125,000m^3/m$. And the null pint where north-westwardly moving longshore sediment is balanced by the south-eastwardly moving longshore sediment is located at ${\beta}=47^{\circ}$. For the case of cross shore sediment, the sediment is gradually moving toward the shore from the April to mid October, whereas these trends are reversed by sporadically occurring energetic wind waves at the end of October and March. We also complete the littoral drift rose of the Mang-Bang beach, which shows that even though the shore line is temporarily retreated, and as a result, the orientation of Mang-Bang beach is larger than the orientation of null pont, south-eastwardly moving longshore sediment is prevailing. In a case that the orientation of Mang-Bang beach is smaller than the orientation of null pont, north-westwardly moving longshore sediment is prevailing. And these trend imply that the Mang-Bang beach is stable one, which has the self restoring capability once exposed to erosion.
Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.
Considering the recent trent toward the development of multiple-use of forest trees, investigations for comprehensive information on these young stands of Hinoki cypress are necessary for rational forest management. From this point of view, 83 sample trees were selected and cut down from 23-ear old stands of Hinoki cypress at Changsung-gun, Chonnam-do. Various stem growth factors of felled trees were measured and canonical correlaton analysis, principal component analysis and factor analysis were applied to investigate the stem growth characteristics, relationships among stem growth factors, and to get potential information and comprehensive information. The results are as follows ; Canonical correlation coefficient between stem volume and quality growth factor was 0.9877. Coefficient of canonical variates showed that DBH among diameter growth factors and height among height growth factors had important effects on stem volume. From the analysis of relationship between stem-volume and canonical variates, which were linearly combined DBH with height as one set, DBH had greater influence on volume growth than height. The 1st-2nd principal components here adopted to fit the effective value of 85% from the pincipal component analysis for 12 stem growth factors. The result showed that the 1st-2nd principal component had cumulative contribution rate of 88.10%. The 1st and the 2nd principal components were interpreted as "size factor" and "shape factor", respectively. From summed proportion of the efficient principal component fur each variate, information of variates except crown diameter, clear length and form height explained more than 87%. Two common factors were set by the eigen value obtained from SMC (squared multiple correlation) of diagonal elements of canonical matrix. There were 2 latent factors, $f_1$ and $f_2$. The former way interpreted as nature of diameter growth system. In inherent phenomenon of 12 growth factor, communalities except clear length and crown diameter had great explanatory poorer of 78.62-98.30%. Eighty three sample trees could he classified into 5 stem types as follows ; medium type within a radius of ${\pm}1$ standard deviation of factor scores, uniformity type in diameter and height growth in the 1st quadrant, slim type in the 2nd quadrant, dwarfish type in the 3rd quadrant, and fall-holed type in the 4 th quadrant.
For many rears, traffic accident statistics are the most direct measure of safety for a signalized intersection. However it takes more than 2 or 3 yearn to collect certain accident data for adequate sample sizes. And the accident data itself is unreliable because of the difference between accident data recorded and accident that is actually occurred. Therefore, it is rather difficult to evaluate safety for a intersection by using accident data. For these reasons, traffic conflict technique(TCT) was developed as a buick and accurate counter-measure of safety for a intersection. However, the collected conflict data is not always reliable because there is absence of clear criteria for conflict. This study developed objective and accurate conflict criteria, which is shown below based on traffic engineering theory. Frist, the rear-end conflict is regarded, when the following vehicle takes evasive maneuver against the first vehicle within a certain distance, according to car-following theory. Second, lane-change conflict is regarded when the following vehicle takes evasive maneuver against first vehicle which is changing its lane within the minimum stopping distance of the following vehicle. Third, cross and opposing-left turn conflicts are regarded when the vehicle which receives green sign takes evasive maneuver against the vehicle which lost its right-of-way crossing a intersection. As a result of correlation analysis between conflict and accident, it is verified that the suggested conflict criteria in this study ave applicable. And it is proven that estimating safety evaluation for a intersection with conflict data is possible, according to the regression analysis preformed between accident and conflict, EPDO accident and conflict. Adopting the conflict criteria suggested in this study would be both quick and accurate method for diagnosing safety and operational deficiencies and for evaluation improvements at intersections. Further research is required to refine the suggested conflict criteria to extend its application. In addition, it is necessary to develope other types of conflict criteria, not included in this study, in later study.
Park, Jun Sang;Seo, Yun Am;Kim, Kyu Rang;Ha, Jong-Chul
Korean Journal of Agricultural and Forest Meteorology
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v.20
no.3
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pp.262-276
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2018
Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.
$SiO_2$ is one of the most abundant constituents of the Earth's crust and mantle. Probing its electronic structures at high pressures is essential to understand their elastic and thermodynamic properties in the Earth's interior. The in situ high-pressure x-ray Raman scattering (XRS) experiment has been effective in providing detailed bonding transitions of the low-z materials under extreme compression. However, the relationship between the local atomic structures and XRS features at high pressure has not been fully established. The ab initio calculations have been used to overcome such experimental difficulties. Here we report the partial density of states (PDOS) of O atoms and the O K-edge XRS spectra of ${\alpha}-quartz$, ${\alpha}-cristobalite$, and $CaCl_2$-type $SiO_2$ phases calculated using ab initio calculations based on the full-potential linearized augmented plane wave (FP-LAPW) method. The unoccupied O PDOSs of the $CaCl_2$-type $SiO_2$ calculated with and without applying the core-hole effects present significantly distinctive features. The unoccupied O p states of the ${\alpha}-quartz$, ${\alpha}-cristobalite$ and $CaCl_2$-type $SiO_2$ calculated with considering the core-hole effect present similar features to their calculated O K-edge XRS spectra. This confirms that characteristic features in the O K-edge XRS stem from the electronic transition from 1s to unoccupied 2p states. The current results indicate that the core-hole effects should be taken in to consideration to calculate the precise O K-edge XRS features of the $SiO_2$ polymorphs at high pressure. Furthermore, we also calculated O K-edge XRS spectrum for $CaCl_2$-type $SiO_2$ at ~63 GPa. As the experimental spectra for these high pressure phases are not currently available, the current results for the $CaCl_2$-type $SiO_2$ provide useful prospect to predict in situ high-pressure XRS spectra.
Fresh edible sweet corns demand relatively short period to harvest fresh ears, which can allow farmers to make a choice sweet corns for various cropping systems. For this reason, we were to find the optimum planting date of late-planted sweet corns to sell fresh ears in the autumn linked to cropping system with winter crops, investigating yield and properties of marketable fresh ears and growth traits of sweet corns (cv. 'Godangok' and cv. 'Guseulok') depending on planting dates such as 10 July, 20 July, and 30 July in Suwon 2012 and 2013, respectively. The 20 July-planted sweet corns showed the most fresh ear yield. However, the 10 July-planted and the 30 July-planted had 32% less yield caused by consecutive rainfall from 10 July through 20 July, and 15% less yield due to low air temperature during ripening than the 20 July-planted, respectively. The 10 and 20 July-planted sweet corns had average 140g of a fresh ear weight and 15% heavier ear than the 30 July-planted. For the July-planted sweet corns, silking days after planting ($r=-0.80^{**}$), and harvesting days after silking ($r=-0.97^{**}$) and planting ($r=-0.91^{**}$) were highly negatively correlated with daily mean air temperature during the period, resulting in it takes 1,100 growing degree days (GDD) to harvest fresh ears from the July-planted sweet corns. The fresh ears of the 20 July-planted sweet corns are able to be harvested by early October. Therefore it will be a good choice for the cropping system based on winter vegetable cash crops such as temperate garlic and onion with medium or late maturity. Among three planting dates 20 July-planted sweet corns had the best field performance in every year considering fresh ear yield, ear size, and stability to grow.
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