• Korean Journal of Construction Engineering and Management
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• v.22 no.3
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• pp.21-30
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• 2021

This study proposes a 5-month rebar price prediction method using the recursive prediction method of deep learning. This approach predicts a long-term point in time by repeating the process of predicting all the characteristics of the input data and adding them to the original data and predicting the next point in time. The predicted average accuracy of the rebar prices for one to five months is approximately 97.24% in the manner presented in this study. Through the proposed method, it is expected that more accurate cost planning will be possible than the existing method by supplementing the systematicity of the price estimation method through human experience and judgment. In addition, it is expected that the method presented in this study can be utilized in studies that predict long-term prices using time series data including building materials other than rebar.

• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.125-128
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• 2003

CSK(Code Shift Keying) 방식은 전송 용량을 높이고, 잡음에 더욱 견고히 할 수 있다[1,2]. 전통적으로 다중 경로 페이딩, 예측 불가능한 위상과 진폭의 왜곡, 협대역 간섭 신호 및 예측 불가능한 시변 재밍 신호에 의한 간섭 등을 특징으로 하는 채널상에서는 DS-BPSK 방식이 사용되었지만 본 논문에서는 코드열의 반복적 특성을 적용한 CSK/SS(Code Shift Keying/Spread Spectrum) 방식을 제안하고 성능을 분석한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.505-512
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• 2010

Generally, the repeated traffic loading condition should be considered to predict the long-term deformation on road foundations or foundation systems. However, it is not easy to estimate long-term deformation on multi-layered system like roads and railways. For more quantitative analysis, mechanistic-empirical approach requires proper analytical tool, material's model, and material properties of foundation geomaterials under both traffic and environmental loadings. In this study, therefore, laboratory data from the long-term repeated load triaxial tests were used to predict accumulated deformation on pavement foundations and the results were analyzed based on the nonlinear models and stress state considered. All these results are presented and verified on laboratory based scale using the finite element analysis with the deformation characteristics of foundation geomaterials at various stress states.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.975-981
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• 2007

In this paper, we propose a spatially adaptive image restoration algorithm using local and statistics and estimated noise. The ratio of local mean, variance, and maximum values with different window size is used to constrain the solution space, and these parameters are computed at each iteration step using partially restored image. In addition, the additive noise estimated from partially restored image and the local constraints are used to determine a parameter for controlling the degree of local smoothness on the solution. The resulting iterative algorithm exhibits increased convergence speed when compared to the non-adaptive algorithm. In addition, a smooth solution with a controlled degree of smoothness is obtained without a prior knowledge about the noise. Experimental results demonstrate that the proposed algorithm requires the similar iteration number to converge, but there is the improvement of SNR more than 0.2 dB comparing to the previous approach.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.70-79
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• 2007

H.264/AVC has many repeated computation for motion estimation. Because of that, it takes much time to encode and it is very hard to implement into a real-time encoder. Many fast algorithms were proposed to reduce computation time but encoding quality couldn't be qualified. In this paper we proposed a new motion vector prediction method for efficient and fast full search H.264/AVC motion estimation. We proposed independent motion vector prediction and SAD share for motion estimation. Using our algorithm, motion estimation reduce calculation complexity 80% and less distortion of image (less PSNR drop) than previous full search scheme. We simulated our proposed method. Maximum Y PSNR drop is about 0.04 dB and average bit increasing is about 0.6%.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.205-207
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• 2020

This paper proposes a novel deep learning-based method to upsample a depth map. Most conventional methods estimate high-resolution depth map by modifying pixel value of given depth map using high-resolution color image and low-resolution depth map. However, these methods cause under- or over-shooting problems that restrict performance improvement. To overcome these problems, the proposed method iteratively performs grid warping scheme which shifts pixel values to restore blurred image for estimating high-resolution depth map. Experimental results show that the proposed method improves both quantitative and visual quality compared to the existing method.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10c
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• pp.658-660
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• 1999

셀단위 무선망에서 핸드오프 하는 호에 대하여 QoS(Quality of Service)를 보장하기 위하여 지금까지 고정적 채널 할당방식, 분산된 호수락제어 방식 그리고 최대치 에측 채널할당 방식등이 제안되어 왔다. 그중 최대치 예측 채널 할당 방식은 핸드오프하는 호의 패턴을 예상하여 새로운 호에 대한 호수락 결정을 할 수 있는 방식으로 제안하였으나 불필요하게 많은 패턴에 대하여는 적합하지 않은데도 그 예상확률을 똑같이 적용하고 있다. 이곳에서는 핸드오프하는 호가 일정한 이동 패턴을 가지고 셀간을 이동하는 경우를 고려하여 일정한 횟수 이상의 반복적인 패턴에 대하여만 그 예상 확률을 적용함으로써 핸드오프 하는 호에 대하여 좀 더 신뢰성 있게 채널을 할당하는 방식을 제시하였다.

• Korean Journal of Construction Engineering and Management
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• v.21 no.6
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• pp.38-45
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• 2020

This study presents a systematic procedure for developing a short-term prediction deep learning model of rebar price using bidirectional LSTM, Random Search, data combination, Dropout. In general, users intuitively determine these values, making it time-consuming and repetitive attempts to explore results with good predictive performance, and the results found by these attempts cannot be guaranteed to be excellent. With the proposed approach presented in this study, the average accuracy of short-term price forecasts is approximately 98.32%. In addition, this approach could be used as basic data to produce good predictive results in a study that predicts prices with time series data based on statistics, including building materials other than rebars.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.946-948
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• 2005

H.264/AVC는 SoC/IEC MPEG와 ITU-T Video Coding Experts Group에서 함께 발표한 비디오 코딩을 위한 가장 최근의 표준이다. 기존의 표준들 보다 적은 비트로 높은 압축률과 좋은 화질을 제공하고 있다. 그러나 1/4 화소 움직임 예측과 보상의 지원과 7가지의 가변블록에 대한 움직임예측과 블록모드별 RD(Rate-Distortion)를 수행하고 CAVLC등 H.264/AVC 표준에서 채택한 여러 가지 비디오 압축방식으로 인해 그 복잡도가 훨씬 증가하였다. 이 논문에서 H.264/AVC의 복호화기에서 복잡도의 약 $40\%$ 이상을 차지하는 움직임보상 모듈을 효율적으로 수행하고 최적화하기 위한 방법을 제안한다. 예측된 모션벡터에 따라 창조하는 프레임에서 매크로블록을 만들어 내는 움직임 보상 과정을 수행하는 데 있어서 접근 지연시간이 큰 외부 메모리 창조를 선인출 메커니즘을 이용하여 미리 예측하여 수행함으로써 전체 수행시간을 줄이는 기법을 적용하였다. 이를 통하여 가변길이 복호화 모듈과 움직임 보상모듈을 수정하여 반복적으로 읽고, 쓰기를 수행해야 하는 횟수를 줄였다. 실험 결과 본 논문에서 제안하는 방법을 이용하여 복호화 과정을 수행했을 때 PSNR(Peak Signal to Noise Ratio)의 손실은 전혀 없으면서 복호화기의 전체 실행시간을 약 $5\%$ 향상시키고, 핵심 모듈인 움직임 보상과정에서 약 $20\%$ 정도 실행시간을 향상시키는 등 높은 성능 향상을 보였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.105-107
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• 2013

단일 반송파 시스템은 다중경로를 가지는 무선채널을 통과할 때 심볼 간 간섭(Inter-symbol Interference, ISI)에 의한 영향을 크게 받으며 이를 보상하기 위한 등화방식은 그동안 많이 연구되어왔다. 본 논문에서는 단일 반송파 시스템에서 심볼 간 간섭의 영향을 보상하기 위해서 잡음 예측기와 결합된 이중 피드백 구조의 등화 방식을 제안한다. 또한, 제안된 등화 방식이 기존 반복 결정 궤환 등화기에 비해 향상된 성능을 가지는 것을 이론적 분석과 모의실험을 통해서 분석한다.