• Journal of Korean Society of Steel Construction
• /
• v.19 no.6
• /
• pp.695-702
• /
• 2007

The Thermal Prestressing Method for continuous composite girder bridges is a new design and construction method developed to induce initial composite stresses in the concrete slab at negative bending regions. Due to the induced initial stresses, prevention of tensile cracks at the concrete slab, reduction of steel girder section, and reduction of reinforcing bars are possible. Thus, the construction efficiency can be improved and the construction can be made more economical. The method for determining the optimum heating region of the thermal prestressing method has not been established although such method is essential for improving the efficiency of the design process. The trial-and-error method used in previous studies is far from efficient, and a more rational method for computing optimal heating region is required. In this study, an efficient method for determining the optimum heating region in using the thermal prestressing method was developed based on the neural network algorithm, which is widely adopted to pattern recognition, optimization, diagnosis, and estimation problems in various fields. Back-propagation algorithm, commonly used as a learning algorithm in neural network problems, was used for the training of the neural network. Through case studies of two-span and three-span continuous composite girder bridges using the developed procedure, the optimal heating regions were obtained.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.10
• /
• pp.1840-1848
• /
• 2017

Linear wireless sensor networks typically construct a network topology with a high reliability through sequential 1:1 mapping among sensor nodes, so that they are used in various surveillance applications of major national infrastructures. Most existing techniques for identifying sensor nodes in those networks are using GPS, AOA, and RSSI mechanisms. However, GPS or AOA based node identification techniques affect the size or production cost of the nodes so that it is not easy to construct practical sensor networks. RSSI based techniques may have a high deviation regrading location identification according to propagation environments and equipment quality so that complexity of error correction algorithm may increase. We propose a timestamps based sequential localization algorithm that uses transmit and receive timestamps in a message between sensor nodes without using GPS, AOA, and RSSI techniques. The algorithms for distance measurement between each node are expected to measure distance within up to 1 meter in case of an crystal oscillator of 300MHz or more.

• Journal of Korea Water Resources Association
• /
• v.39 no.10 s.171
• /
• pp.823-832
• /
• 2006

Precipitation is the most important component to the study of water and energy cycle in hydrology. In this study we investigate rainfall retrieval uncertainty from different sources of remotely sensed precipitation field and then probable error propagation in the simulation of hydrologic variables especially, runoff on different vegetation cover. Two remotely sensed rainfall retrievals (space-borne IR-only and ground radar rainfall) are explored and compared visually and statistically. Then, an offline Community Land Model (CLM) is forced with in situ meteorological data to simulate the amount of runoff and determine their impact on model predictions. A fundamental assumption made in this study is that CLM can adequately represent the physical land surface processes. Results show there are big differences between different sources of precipitation fields in terms of the magnitude and temporal variability. The study provides some intuitions on the uncertainty of hydrologic prediction via the interaction between the land surface and near atmosphere fluxes in the modelling approach. Eventually it will contribute to the understanding of water resources redistribution to the climate change in Korean Peninsula.

• Geophysics and Geophysical Exploration
• /
• v.14 no.3
• /
• pp.220-226
• /
• 2011

In the elastic wave equations, both horizontal and vertical displacements are defined. Since we can measure both the horizontal and vertical displacements in field acquisition, these displacements compose a displacement vector. In this study, we propose a frequency-domain elastic waveform inversion technique taking advantage of the magnitudes of displacement vectors to define objective function. When we apply this displacement-vector objective function to the frequency-domain waveform inversion, the inversion process naturally incorporates the back-propagation algorithm. Through the inversion examples with the Marmousi model and the SEG/EAGE salt model, we could note that the RMS error of the solution obtained by our algorithm decreased more stably than that of the conventional method. Particularly, the density of the Marmousi model and the low-velocity sub-salt zone of the SEG/EAGE salt model were successfully recovered. Since the gradient direction obtained from the proposed objective function is numerically unstable, we need additional study to stabilize the gradient direction. In order to perform the waveform inversion using the displacementvector objective function, it is necessary to acquire multi-component data. Hence, more rigorous study should be continued for the multi-component land acquisition or OBC (Ocean Bottom Cable) multi-component survey.

• Journal of Korea Water Resources Association
• /
• v.31 no.1
• /
• pp.13-25
• /
• 1998

It is necessary to develop methodologies for the application of artificial neural network into hydrologic rainfall-runoff process, although there is so much applicability by using the functions of associative memory based on recognition for the relationships between causes and effects and the excellent fitting capacity for the nonlinear phenomenon. In this study, some problems are presented in the application procedures of artificial neural networks and the simulation of runoff hydrograph experiences are reviewed with nonlinear functional approximator by artificial neural network for rainfall-runoff relationships in a watershed. which is regarded as hydrdologic black box model. The neural network models are constructed by organizing input and output patterns with the deserved rainfall and runoff data in Pyoungchang river basin under the assumption that the rainfall data is the input pattern and runoff hydrograph is the output patterns. Analyzed with the results. it is possible to simulate the runoff hydrograph with processing element of artificial neural network with any hydrologic concepts and the weight among processing elements are well-adapted as model parameters with the assumed model structure during learning process. Based upon these results. it is expected that neural network theory can be utilized as an efficient approach to simulate runoff hydrograph and identify the relationship between rainfall and runoff as hydrosystems which is necessary to develop and manage water resources.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.2
• /
• pp.465-482
• /
• 1997

In this paper, we have studied on cinstructing code-recognition shstem by neural network according to a image process taking the DOT classification code stamped on tire surface.It happened to a few problems that characters distorted in edge by diffused reflection and two adjacent characters take the same label,even very sen- sitive to illumination ofr recognition the stamped them on tire.Thus,this paper would propose the algorithm for tire code under being cinscious of these properties and prove the algorithm drrciency with a simulation.Also,we have suggerted the MCBP network composing of multi-linked recognizers of dffcient identify the DOT code being tire classification code.The MCBP network extracts the projection balue for classifying each character's rdgion after taking out the prjection of each chracter's region on X,Y axis,processes each chracters by taking 7$\times$8 normalization.We have improved error rate 3% through the MCBP network and post-process comparing the DOT code Database. This approach has a accomplished that learming time get's improvenent at 60% and recognition rate has become to 95% from 90% than BckPropagation with including post- processing it has attained greate rates of entire of tire recoggnition at 98%.

• The Transactions of the Korea Information Processing Society
• /
• v.3 no.3
• /
• pp.439-448
• /
• 1996

Extraction and selection of the informative features play a central role in pattern recognition. This paper describes a modified back-propagation algorithm that performs selection of the informative features and trains a neural network simultaneously. The algorithm is mainly composed of three repetitive steps : training, connection pruning, and input unit elimination. Afer initial training, the connections that have small magnitude are first pruned. Any unit that has a small number of connections to the hidden units is deleted,which is equivalent to excluding the feature corresponding to that unit.If the error increases,the network is retraned,again followed by connection pruning and input unit elimination.As a result,the algorithm selects the most im-portant features in the measurement space without a transformation to another space.Also,the selected features are the most-informative ones for the classification,because feature selection is tightly coupled with the classifi-cation performance.This algorithm helps avoid measurement of redundant or less informative features,which may be expensive.Furthermore,the final network does not include redundant parameters,i.e.,weights and biases,that may cause degradation of classification performance.In applications,the algorithm preserves the most informative features and significantly reduces the dimension of the feature vectors whiout performance degradation.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.23 no.8
• /
• pp.2010-2024
• /
• 1998

In video coding, it is important to improve the average picture quality as well as to maintain cosistent picture quality between consecutive pictures. In this paper, we propose a distortion-rate estimation method for MPEG-2 video and a forward rate control method, using the proposed estimation result, to be able to obtain the improved and consistent picture quality of CBR (Constant Bit Rate) encoded MPEG-2 video. The proposed distortion-rate estimation enable us to predict the distortion and the bits generated from an encoded picture at a given quantization step size and vice versa. The most attactive features of proposed distortion-rate estimation are its accuracy and low computational complexity enough to be applied to the practical video coding. In addition, the proposed rate control first determined a quantization parameter per frame by following procedure: distortion-rate estimation, target bit allocation, distortion constraint and VBV(Video Buffer Verification) constraint. And then this quantization parameter is applied to the encoding so that improved and consisten picture quality can be obtained. Furthermore the proposed rate control method can solve the error propagation problem caused by scene change or anchor picture degradation by using the B-picture skipping and the guarantee of the minimum bit allocation for the anchor picture. Experimental results, comparing the proposed forward rate control method with TM5 method, show that the proposed method makes more improed and consistent picture quality than TM5.

• Journal of Korea Water Resources Association
• /
• v.51 no.11
• /
• pp.1021-1029
• /
• 2018

A primary objective of this study is to develop a drought forecasting technique based on groundwater which can be exploit for water supply under drought stress. For this purpose, we explored the lagged relationships between regionalized SGI (standardized groundwater level index) and SPI (standardized precipitation index) in view of the drought propagation. A regional prediction model was constructed using a NARX (nonlinear autoregressive exogenous) artificial neural network model which can effectively capture nonlinear relationships with the lagged independent variable. During the training phase, model performance in terms of correlation coefficient was found to be satisfactory with the correlation coefficient over 0.7. Moreover, the model performance was described by root mean squared error (RMSE). It can be concluded that the proposed approach is able to provide a reliable SGI forecasts along with rainfall forecasts provided by the Korea Meteorological Administration.

• Journal of the Korea Concrete Institute
• /
• v.14 no.4
• /
• pp.457-466
• /
• 2002

An artificial neural network was applied to predict compressive strength, slump value and mix proportion of a concrete. Standard mixed tables were trained and estimated, and the results were compared with those of the experiments. To consider variabilities of material properties, the standard mixed fables from two companies of Ready Mixed Concrete were used. And they were trained with the neural network. In this paper, standard back propagation network was used. The mix proportion factors such as water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate and air entraining admixture were used. For the arrangement on the approval of prediction of mix proportion factor, the standard compressive strength of $180kgf/cm^2{\sim}300kgf/cm^2$, and target slump value of 8 cm, 15 cm were used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of $210kgf/cm^2{\sim}240kgf/cm^2$, and target slump value of 12 cm and 15 cm wore used because these ranges are most frequently used. In results, in the prediction of mix proportion factor, for all of the water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate, air entraining admixture, the predicted values and the values of standard mixed tables were almost the same within the target error of 0.10 and 0.05, regardless of two companies. And in the prediction of compressive strength and slump value, the predicted values were converged well to the values of standard mixed fables within the target error of 0.10, 0.05, 0.001. Finally artificial neural network is successfully applied to the prediction of concrete mixture and compressive strength.