• Journal of the Society of Disaster Information
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• v.13 no.4
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• pp.491-498
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• 2017

The primary objective of this research is to develop a rutting performance prediction model of flexible pavement. Extensive laboratory testings were conducted to achieve the objective. A new test method employing repetitive axial loading with confinement was also adopted to estimate the rutting performance of asphalt concrete in the research. The rutting prediction model employes a layer-strain theory. The required rutting coefficients for the prediction model were determined through the laboratory rutting characterizations of the asphalt concrete layer materials. Within the limits of this study, a laboratory rutting prediction model of flexible pavement using repetitive axial loading test was presented. It is noted that the developed rutting prediction model simulates propery the behaviors of flexible pavement layer materials.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.43-49
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• 2020

This paper proposes a lossless image compression method based on arithmetic coding using block-adaptive Context Tree Weighting. The CTW method predicts and compresses the input data bit by bit. Also, it can achieve a desirable coding distribution for tree sources with an unknown model and unknown parameters. This paper suggests the method to enhance the compression rate about image data, especially aerial and satellite images that require lossless compression. The value of aerial and satellite images is significant. Also, the size of their images is huger than common images. But, existed methods have difficulties to compress these data. For these reasons, this paper shows the experiment to prove a higher compression rate when using the CTW method with divided images than when using the same method with non-divided images. The experimental results indicate that the proposed method is more effective when compressing the divided images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.11
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• pp.2543-2551
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• 1997

This paper proposes a compression technique of image data, a variable length PN code and channel models which are required in CDMA communication system. It also analyzes their performances. Original images is compressed by 2-D DCT and its coefficients are quantized by optimal quantizer at compression rate 0.84bit/pel. Channel model 1 and 2 which are composed of 5 and 4 channels respectively are employed to be used in CDMA. Such a situation forces us to empoly variable length PN code, such as Chebyshev map for spread spectrum system. When average PN code length of model 1 and 2 is 44.4 and 26.7 chips respectively, the received image through these models under Gaussian noise with variance 1.75 is visually of the same quality as the transmitting image. Thus, the model 2 appears to be better in channel efficiency, comparing with channel model 1 and channel model which uses fixed length PN code.

• Journal of the Korea Concrete Institute
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• v.14 no.2
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• pp.137-147
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• 2002

Maturity models involving curing temperature and curing ages have been widely used to predict concrete strength, which can accurately estimate concrete strength. However, they may not consider physical quantities such as the characteristics of hydrates and the capillary porosity of microstructures associated with strength development. In order to find out the effects of both factors on a strength increment, the hydration model and the estimation method of the amount of capillary porosity were established, and the compressive strength test of concrete nth various water/cement ratios was carried out considering two test parameters, curing temperature and curing age. In this study, by analyzing the experimental results, a strength estimation model for early-age concrete that can consider the microstructural characteristics such as hydrates and capillary porosity was proposed. Measured compressive strengths were compared with estimated strengths and good agreements were obtained. Consequently, the proposed strength model can estimate compressive strength of concrete with curing age and curing temperature within an acceptable error.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.27-27
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• 2021

세굴은 유체와 유사의 상호작용으로 발생하는 중요한 자연 현상 중 하나로, 구조 및 지반 붕괴, 홍수, 생태계 파괴 등의 문제를 야기할 수 있다. 이러한 세굴 현상을 예측하기 위해 많은 수치적 연구가 진행되어왔지만, 대부분의 연구가 기존 격자기반방법인 유한체적법 (FVM)과 개별요소법 (DEM)이 연성된 모델을 이용하였고, 이는 격자 의존도로 인한 정확도와 효율성의 문제점을 보였다. 해결책으로 입자기반 유체해석 방법인 약압축성 SPH (WCSPH)와 개별요소법의 결합모델을 이용한 모의가 연구되어 왔지만, 단순 밀도차를 활용한 유체해석방법이 압력의 불안정성을 야기하여 유사의 운동에도 영향을 주는 결과를 보였다. 또한, 개별요소법의 특성상 모의 입자의 크기를 실제 실험 입자의 크기와 동일하게 설정하면서 입자수가 지나치게 증가해 계산의 효율성이 현저히 낮아지게 되었고, 이로 인해 실제 자연 지형에 적용하는데 어려움을 보여주었다. 본 연구에서는 향상된 세굴 수치모의해석을 위해 반복법을 통해 안정적인 유체 압력을 계산하는 비압축성 SPH (ISPH)와 개별요소법을 연성한 ISPH-DEM 모델을 사용하였다. 또한, 계산속도 향상을 위해 하나의 입자가 다수의 작은 입자의 움직임을 대표하는 Coarse-grained 방법을 적용하여 기존 모델을 개선하였다. 개선된 모델을 NFLOW ISPH PURPLE 소프트웨어를 이용하여 세굴 현상을 수치 모의하였고 실험 결과와 검증을 진행한 결과, 세굴의 깊이, 너비, 형상 등을 비교하였을 때 약 10% 이내의 오차를 보였고, Coarse-grained 방법을 통한 입자 수 감소로 최소 13배 증가된 해석 속도를 보였다. 이를 통해 본 연구에서 제시된 모델이 실제 자연 지형에서의 적용가능성을 확인할 수 있었다.

• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.115-116
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• 2009

A new model which is able to understand the mechanical behavior is developed, based on investigating the theoretical background for design compressive strength in strut-and-tie model. A proposed model is an alternative method for engineers through analyzing the merits and demerits of the conventional models

• Journal of the Korean Geotechnical Society
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• v.16 no.3
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• pp.67-75
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• 2000

본 연구에서는 지반의 비등방성을 고려한 점탄소성 bounding surface 모델의 정확성을 검증하고 모델정수의 영향을 고찰하였다. 이를 위하여 모델을 컴퓨터 프로그래밍 하였으며 실내시험을 실시하였다. 실내시험으로는 표준압밀시험, 등방/비등방 압밀 삼축압축시험, 크리프 시험 등이 실시되었다. 연구결과, 컴퓨터 프로그램을 이용한 해석결과와 실내시험 결과는 잘 부합되었으며, 탄소성 모델정수의 영향은 크지 않았으나 점소성 모델정수의 영향은 해석결과에 큰 영향을 미치는 것으로 고찰되었다.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.05
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• pp.43-48
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• 1994

본 논문은 반복 수축 변환의 프랙탈(fractal) 이론에 근거한 심전도 데이터 압축에 관한 연구이다. 심전도 데이터에 반복 함수계(Iterated Function System : IFS) 모델을 적용하여 신호 자체의 자기 유사성(self-similarity)을 반복 수축 변환으로 표현하고, 그 매개변수만을 저장한다. 재구성시는 변환 매개변수를 반복 적용하여 원래의 신호에 근사되어지는 값을 얻게 된다. 심전도 데이타는 부분적으로 자기 유사성을 갖는다고 보고, 부분 자기-유사 프랙탈 모델(piecewise self-affine fractal model)로 표현될 수 있다. 이 모델은 신호를 특정 구간들로 나누어 각 구간들에 대해 최적 프랙탈 보간(fractal interpolation)을 구하고 그 중 오차가 가장 작은 매개변수만을 추출하여 저장한다. 이 방법을 심전도 데이타에 적용한 결과 특정 압축율에 대해 아주 적은 재생오차 (percent root-mean-square difference : PRD)를 얻을 수 있었다.

• Polymer(Korea)
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• v.24 no.5
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• pp.690-700
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• 2000

In this study, an elastomer-assistered compression molding process was investigated by experiments as well as modeling for the long-fiber reinforced thermoset composites. The consolidation pressure generated by fixed-volume and variable-volume conditions was thermodynamically derived for both elastomer and curing prepregs, and was compared with the pressure measured during curing of epoxy matrix. Exhibiting non-linear viscoelastic characteristics in the compressive stress-strain tests, the measured stress was well compared with a modifed KWW (Kohlrausch-Williame-Watts) equation, which is based on the Maxwell viscoelastic model. Using the developed model equations, the consolidation pressure generated by the elastomer was successfully predicted for the compression molding process of thermoset composite materials in tile closed mold system.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.619-630
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• 2022

The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.