• Korean Journal of Remote Sensing
• /
• v.34 no.6_3
• /
• pp.1445-1456
• /
• 2018

The purpose of this study is to compare the quality and characteristics of matching DEMs by using KOMPSAT-3 stereo pair capture in in-track and cross-track. For this purpose, two stereo pairs of KOMPSAT-3 were collected that were taken in the same area. The two stereo pairs have similar stereo geometry elements such as B/H, convergence angle. Sensor modeling for DEM production was performed with RFM affine calibration using multiple GCPs. The GCPs used in the study were extracted from the 0.25 m ortho-image and 5 meter DEM provided by NGII. In addition, matching DEMs were produced at the same resolution as the reference DEMs for a comparison analysis. As a result of the experiment, the horizontal and vertical errors at the CPs indicated an accuracy of 1 to 3 pixels. In addition, the shapes and accuracy of two DEMs produced in areas where the effects of natural or artificial surface land were low were almost similar.

• Structural Engineering and Mechanics
• /
• v.58 no.3
• /
• pp.459-473
• /
• 2016

Various vague and unstructured problems encountered the civil engineering/designers that persuaded by their experiences. One of these problems is the structural failure of the reinforced concrete (RC) building determination. Typically, using the traditional Limit state method is time consuming and complex in designing structures that are optimized in terms of one/many parameters. Recent research has revealed the Artificial Neural Networks potentiality in solving various real life problems. Thus, the current work employed the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifier to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. In order to evaluate the proposed method performance, a database of 257 multistoried buildings RC structures has been constructed by professional engineers, from which 150 RC structures were used. From the structural design, fifteen features have been extracted, where nine features of them have been selected to perform the classification process. Various performance measures have been calculated to evaluate the proposed model. The experimental results established satisfactory performance of the proposed model.

• 한국신재생에너지학회:학술대회논문집
• /
• 2007.06a
• /
• pp.515-518
• /
• 2007

Gas hydrates are ice-like crystalline compounds that form under low temperature and elevated pressure conditions. Recently, gas hydrates present a novel means for natural gas storage and transportation with potential applications in a wide variety of areas. An important property of hydrates that makes them attractive for use in gas storage and transportation is their very high gas-to-sol id ratio. In addition to the high gas content, gas hydrates are remarkably stable. The main barrier to development of gas hydrate technology is the lack of an effective mass production method of gas hydrate in solid form. In this study, some performance comparison among several cases classified by different volume sizes of solution were carried to identify the characteristics due to the volume increment. And it is found that one of the main reasons disturbing hydrate formation is related to the lack of cooling heat transfer due to the volume increase of the solution. So, three kinds of heat transfer plates which have different shapes and cross sectional areas were made and tested for the performance comparison following to the shape and area of each plate. Finally it is clarified that the heat transfer is one of the major factors effecting hydrate formation performance and the installation of heat transfer plate can enhance the formation performance especially not in terms of the quantity but the speed.

• Structural Engineering and Mechanics
• /
• v.53 no.5
• /
• pp.939-956
• /
• 2015

In this study, in order to propose an efficient model to predict the torque capacity of steel fiber reinforced concrete (SFRC) beams, the existing experimental data related to torsional response of beams is reviewed. It is observed that existing data neglects the effects of some parameters on the variation of torque capacity. Thus, an experimental research was also conducted to obtain the effects of neglected parameters. In the experimental study, a total of seventeen SFRC beams are tested against torsion. The parameters considered in the experiments are concrete compressive strength, steel fiber aspect ratio, volumetric ratio of steel fibers and longitudinal reinforcement ratio. The effect of each parameter is discussed in terms of torque versus unit angle of twist graphs. The data obtained from this experimental research is also combined with the data got from previous studies and employed in artificial neural network (ANN) analysis to estimate the ultimate torque capacity of SFRC beams. In addition to parameters considered in the experiments, aspect ratio of beam cross-section, yield strengths of both transverse and longitudinal reinforcements, and transverse reinforcement ratio are also defined as parameters in ANN analysis due to their significant effects observed in previous studies. Assessment of the accuracy of ANN analysis in estimating the ultimate torque capacity of SFRC beams is performed by comparing the analytical and experimental results. Comparisons are conducted in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of efficiency ($E_f$). The results of this study revealed that addition of steel fibers increases the ultimate torque capacity of reinforced concrete beams. It is also found that ANN is a powerful method and a feasible tool to estimate ultimate torque capacity of both normal and high strength concrete beams within the range of input parameters considered.

• Environmental Engineering Research
• /
• v.21 no.4
• /
• pp.333-340
• /
• 2016

An Artificial Neural Network including a Radial Basis Function (RBF) and a Time Delay Neural Network (TDNN) was used to predict total dissolved solid (TDS) in the river Zayanderud. Water quality parameters in the river for ten years, 2001-2010, were prepared from data monitored by the Isfahan Regional Water Authority. A factor analysis was applied to select the inputs of water quality parameters, which obtained total hardness, bicarbonate, chloride and calcium. Input data to the neural networks were pH, $Na^+$, $Mg^{2+}$, Carbonate ($CO{_3}^{-2}$), $HCO{_3}^{-1}$, $Cl^-$, $Ca^{2+}$ and Total hardness. For learning process 5-fold cross validation were applied. In the best situation, the TDNN contained 2 hidden layers of 15 neurons in each of the layers and the RBF had one hidden layer with 100 neurons. The Mean Squared Error and the Mean Bias Error for the TDNN during the training process were 0.0006 and 0.0603 and for the RBF neural network the mentioned errors were 0.0001 and 0.0006, respectively. In the RBF, the coefficient of determination ($R^2$) and the index of agreement (IA) between the observed data and predicted data were 0.997 and 0.999, respectively. In the TDNN, the $R^2$ and the IA between the actual and predicted data were 0.957 and 0.985, respectively. The results of sensitivity illustrated that $Ca^{2+}$ and $SO{_4}^{2-}$ parameters had the highest effect on the TDS prediction.

• Journal of the Korean Crystal Growth and Crystal Technology
• /
• v.19 no.4
• /
• pp.208-213
• /
• 2009

Artificial lightweight aggregates were produced by using bottom ashes and dredged soils from coal power plant. The amount of glassy phases on the aggregate surfaces, specific gravities, absorption rates, and observations of cross-sectional surfaces were compared according to the compositions, sintering temperatures, and the amount of coating. It is concluded that surface modification by 10 % $CaCO_3$ coating on the aggregate surfaces enhances the properties of aggregates as follows: Specific gravities were controlled by depressing formation of large pores in the aggregates. Sticking phenomena among aggregates during the sintering process was drastically decreased by reducing glassy phases on the aggregate surfaces. Pumping problems during the application of ready-mix concretes containing lightweight aggregates having high value of absorption rates could be solved by reducing the absorption rate.

• Steel and Composite Structures
• /
• v.17 no.3
• /
• pp.215-236
• /
• 2014

The flexural behaviour of steel beams significantly affects the structural performance of the steel frame structures. In particular, the flexural overstrength (namely the ratio between the maximum bending moment and the plastic bending strength) that steel beams may experience is the key parameter affecting the seismic design of non-dissipative members in moment resisting frames. The aim of this study is to present a new formulation of flexural overstrength factor for steel beams by means of artificial neural network (NN). To achieve this purpose, a total of 141 experimental data samples from available literature have been collected in order to cover different cross-sectional typologies, namely I-H sections, rectangular and square hollow sections (RHS-SHS). Thus, two different data sets for I-H and RHS-SHS steel beams were formed. Nine critical prediction parameters were selected for the former while eight parameters were considered for the latter. These input variables used for the development of the prediction models are representative of the geometric properties of the sections, the mechanical properties of the material and the shear length of the steel beams. The prediction performance of the proposed NN model was also compared with the results obtained using an existing formulation derived from the gene expression modeling. The analysis of the results indicated that the proposed formulation provided a more reliable and accurate prediction capability of beam overstrength.

• Journal of Korean Society of Forest Science
• /
• v.88 no.4
• /
• pp.485-489
• /
• 1999

The germination percentages of Quercus hybrid seeds produced in 1994 using stored pollens were low. The survival rate and height growth within 2 years differed according to crossing combinations. The survival rate and growth of seedlings from hybrid seeds produced in 1991 and 1993 were investigated. The survival rates of five-year-old and three-year-old seedlings were from 71 to 100% and from 33 to 100%, respectively. The survival rates differed according to cross combinations. The mortality of seedlings increased in proportion to the increase of percentages of dwarf seedlings. The growth of five-year-old seedlings, of which mother tree was Q. aliena, was the best. The growth of seedlings produced by artificial crossing tended to be worse than that of natural crossing.

• Journal of Korea Water Resources Association
• /
• v.49 no.10
• /
• pp.877-885
• /
• 2016

In this study, the Standard Precipitation Index(SPI), meteorological drought index, was used to evaluate the temporal and spatial assessment of drought forecasting results for all cross Korea. For the drought forecasting, the Multi Layer Perceptron-Artificial Neural Network (MLP-ANN) was selected and the drought forecasting was performed according to different forecasting lead time for SPI (3) and SPI (6). The precipitation data observed in 59 gaging stations of Korea Meteorological Adminstration (KMA) from 1976~2015. For the performance evaluation of the drought forecasting, the binary classification confusion matrix, such as evaluating the status of drought occurrence based on threshold, was constituted. Then Receiver Operating Characteristics (ROC) score and F score according to conditional probability are computed. As a result of ROC analysis on forecasting performance, drought forecasting performance, of applying the MLP-ANN model, shows satisfactory forecasting results. Consequently, two-month and five-month leading forecasts were possible for SPI (3) and SPI (6), respectively.

• Journal of Wetlands Research
• /
• v.15 no.3
• /
• pp.301-315
• /
• 2013

The purpose of this study is to investigate characteristics of hydromorpological structures that affect landscape preference and visual characteristics on the sections of the designated streams where have dynamic ecological characteristics. We evaluated the ecological status of the streams utilizing LAWA to assess hydromorpological structures of streams. We also investigated preference and visual characteristics of stream landscapes through Semantic Differential Scale(SD scale). The differences of visual images according to the characteristics of hydromorpological structures in the sites were analyzed by descriptive statistics, One-way ANOVA, and t-test. As a result, this study showed that sections represented as "good" ecological status are shown to be harmonious, beautiful, natural, and clean comparing to sections represented as "poor" ecological status. The hydromorpological structures that have significant impacts on the visual characteristics are considered as riparian vegetation, cross-sectional shape, and the artificial structures. Results of this study can help guide the stream restoration of the damaged stream to improving ecological function and positive landscape.