• Journal of the Optical Society of Korea
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• v.20 no.3
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• pp.381-388
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• 2016

The performance of a staring infrared imaging system can be characterized based on estimating the modulation transfer function (MTF). The slant edge method is a widely used MTF estimation method, which can effectively solve the aliasing problem caused by the discrete undersampling of the infrared focal plane array. However, the traditional slant edge method has some limitations such as the low precision of the edge angle extraction and using the approximate function to fit the edge spread function (ESF), which affects the accuracy of the MTF estimation. In this paper, we propose a modified slant edge method, including an edge angle extraction method that can improve the precision of the edge angle extraction and an ESF fitting algorithm which is based on the transfer function model of the imaging system, to enhance the accuracy of the MTF estimation. This modified slant edge method presents higher estimation accuracy and better immunity to noise and edge angle than other traditional methods, which is demonstrated by the simulation and application experiments operated in our study.

• Steel and Composite Structures
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• v.33 no.5
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• pp.729-746
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• 2019

The nonlinear behavior of single- and multi-story steel plate shear walls (SPSWs) strengthened with three different patterns of fiber reinforced polymer (FRP) laminates (including single-strip, multi-strip and fully FRP-strengthened models) is studied using the finite element analysis. In the research, the effects of orientation, width, thickness and type (glass or carbon) of FRP sheets as well as the system aspect ratio and height are investigated. Results show that, despite an increase in the system strength using FRP sheets, ductility of reinforced SPSWs is decreased due to the delay in the initiation of yielding in the infill wall, while their initial stiffness does not change significantly. The content/type/reinforcement pattern of FRPs does affect the nonlinear behavior characteristics and also the mode and pattern of failure. In the case of multi-strip and fully FRP-strengthened models, the use of FPR sheets almost along the direction of the infill wall tension fields can maximize the effectiveness of reinforcement. In the case of single-strip pattern, the effectiveness of reinforcement is decreased for larger aspect ratios. Moreover, a relatively simplified and approximate theoretical procedure for estimating the strength of SPSWs reinforced with different patterns of FRP laminates is presented and compared with the analytical results.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.85-94
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• 2004

As over 70% of the total life cycle cost (LCC) of a product is committed at the early design stage, designers are in an important position to substantially reduce the LCC of the products they design by giving due to life cycle implications of their design decisions. During early design stages, there may be competing concepts with dramatic differences. In addition, the detailed information is scarce and decisions must be made quickly. Thus, both the overhead in developing parametric LCC models fur a wide range of concepts, and the lack of detailed information make the application of traditional LCC models impractical. A different approach is needed, because a traditional LCC method is to be incorporated in the very early design stages. This paper explores an approximate method for providing the preliminary LCC, Learning algorithms trained to use the known characteristics of existing products might allow the LCC of new products to be approximated quickly during the conceptual design phase without the overhead of defining new LCC models. Artificial neural networks are trained to generalize product attributes and LCC data from pre-existing LCC studies. Then the product designers query the trained artificial model with new high-level product attribute data to quickly obtain an LCC for a new product concept. Foundations fur the learning LCC approach are established, and then an application is provided.

• Geomechanics and Engineering
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• v.19 no.4
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• pp.353-360
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• 2019

The elastic modulus is an important parameter to characterize the property of rock. It is common knowledge that the strengths of rocks are significantly different under tension and compression. However, little attention has been paid to the bi-modularity of rock. To validate whether the rock elastic moduli in tension and compression are the same, Brazilian disc, direct tension and compression tests were conducted. A horizontal laser displacement meter and a pair of vertical and transverse strain gauges were applied. Four types of materials were tested, including three types of rock materials and one type of steel material. A comprehensive comparison of the elastic moduli based on different experimental results was presented, and a tension-compression anisotropy model was proposed to explain the experimental results. The results from this study indicate that the rock elastic modulus is different under tension and compression. The ratio of the rock elastic moduli under compression and tension ranges from 2 to 4. The rock tensile moduli from the strain data and displacement data are approximate. The elastic moduli from the Brazilian disc test are consistent with those from the uniaxial tension and compression tests. The Brazilian disc test is a convenient method for estimating the tensile and compressive moduli of rock materials.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.419-421
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• 2009

The freezing point would be different as the atmospheric pressure and humidity change. So if we can measure the transparency of ice, it should be easy that we approximate a freezing process and estimate the density of ice. This paper presents the method for estimating the transparency of ice in images. First, ice images are mapped to the $CIEL^*a^*b^*$ color space, and we make a new index for the ice transparency by using the average of $L^*$ and RMS(Root Mean Square) Contrast. In this case, the new index is better than the other existing method, i.e, Weber contrast, and Michelson contrast.

• Journal of the Korean Geotechnical Society
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• v.23 no.12
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• pp.83-94
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• 2007

Piled raft foundations have been highlighted as an economical design concept of pile foundations in recent years. However, piled raft foundations have not been widely used in Korea due to the difficulty in estimating the complex interaction effects among rafts, piles and soils. The authors developed an effective numerical program to analyze the behavior of piled raft foundations for practical design purposes and presented it briefly in this paper. The developed numerical program simulates the raft as a flexible plate consisting of finite elements with eight nodes and the raft is supported by a series of elastic springs representing subsoils and piles. This study imported another model to simulate pile groups considering non-linear behavior and interaction effects. The apparent stiffnesses of the soils and piles were estimated by iterative calculations to satisfy the compatibility between those two components and the behavior of piled raft foundations can be predicted using these stiffnesses. For the verification of the program, the analysis results about some example problems were compared with those of rigorous three dimensional finite element analysis and other approximate analysis methods. It was found that the program can analyze non-linear behaviors and interaction effects efficiently in multi-layered soils and has sufficient capabilities for application to practical analysis and design of piled raft foundations.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.815-826
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• 2012

The underestimating trend using existing ordinary regression (OR) based trend analysis has been a well-known problem. The existing OR method based on least squares approximate the conditional mean of the response variable given certain values of the time t, and the usual assumption of the OR method is normality, that is the distribution of data are not dissimilar form a normal distribution. In this regard, this study proposed a quantile regression that aims at estimating either the conditional median or other quantiles of the response variable. This study assess trend in annual daily maximum rainfall series over 64 weather stations through both in OR and QR approach. The QR method indicates that 47 stations out of 67 weather stations are a strong upward trend at 5% significance level while OR method identifies a significant trend only at 13 stations. This is mainly because the OR method is estimating the condition mean of the response variable. Unlike the OR method, the QR method allows us flexibly to detect the trends since the OR is designed to estimate conditional quantiles of the response variable. The proposed QR method can be effectively applied to estimate hydrologic trend for either non-normal data or skewed data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.4
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• pp.705-716
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• 2017

As many environmental pollution problems have arisen, various studies related to the environmental evaluation have been carried out in the construction industry. However, there is no methodology for estimating the environmental load quickly for design alternatives of civil facilities in the design phase. This study aim to establish criteria of works information and designed parts which can efficiently estimate environmental loads of PSC beam bridge based on standard quantity at the early design phase. For this purpose, a detailed environmental loads database was constructed by performing Life Cycle Assessment (LCA) based on detailed design data of 25 bridges. In addition, major work with high impact on environmental load were selected, and the analysis of characteristics of environmental load according to the required materials and 8 impact categories were conducted. As a result, the superstructure accounted for 42.91%. In the superstructure, remicon of the material base and PSC beam work occupied 53.13% and 31.25%. In the substructure, remicon, rebar, and cement, which are material base, accounted for more than 93%. It is expected that this major work and material information for each part of bridge can be utilized in the construction of the model, which can estimate the approximate environmental load, reflecting the characteristics of the structure in the design phase.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.210-218
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• 2002

This paper proposes an efficient separation method of the composite images by using independent component analysis(ICA) based on neural networks of the approximate learning algorithm. The Proposed learning algorithm is the fixed point(FP) algorithm based on Secant method which can be approximately computed by only the values of function for estimating the root of objective function for optimizing entropy. The secant method is an alternative of the Newton method which is essential to differentiate the function for estimating the root. It can achieve a superior property of the FP algorithm for ICA due to simplify the composite computation of differential process. The proposed algorithm has been applied to the composite signals and image generated by random mixing matrix in the 4 signal of 500-sample and the 10 images of $512{\times}512-pixel$, respectively The simulation results show that the proposed algorithm has better performance of the learning speed and the separation than those using the conventional algorithm based method. It also solved the training performances depending on initial points setting and the nonrealistic learning time for separating the large size image by using the conventional algorithm.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.65-70
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• 1991

A hybrid model which uses a probabilistic model and a MLP(multi layer perceptron) model for pattern recognition of EMG(electromyogram) signals is proposed in this paper. MLP model has problems which do not guarantee global minima of error due to learning method and have different approximation grade to bayesian probabilities due to different amounts and quality of training data, the number of hidden layers and hidden nodes, etc. Especially in the case of new test data which exclude design samples, the latter problem produces quite different results. The error probability of probabilistic model is closely related to the estimation error of the parameters used in the model and fidelity of assumtion. Generally, it is impossible to introduce the bayesian classifier to the probabilistic model of EMG signals because of unknown priori probabilities and is estimated by MLE(maximum likelihood estimate). In this paper we propose the method which get the MAP(maximum a posteriori probability) in the probabilistic model by estimating the priori probability distribution which minimize the error probability using the MLP. This method minimize the error probability of the probabilistic model as long as the realization of the MLP is optimal and approximate the minimum of error probability of each class of both models selectively. Alocating the reference coordinate of EMG signal to the outside of the body make it easy to suit to the applications which it is difficult to define and seperate using internal body coordinate. Simulation results show the benefit of the proposed model compared to use the MLP and the probabilistic model seperately.