• Journal of the Korea Institute of Building Construction
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• 제22권6호
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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.

• Journal of the Computational Structural Engineering Institute of Korea
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• 제36권1호
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• pp.9-18
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• 2023

Predicting the compressive strength of high-performance concrete (HPC) is challenging because of the use of additional cementitious materials; thus, the development of improved predictive models is essential. The purpose of this study was to develop an HPC compressive-strength prediction model using an ensemble machine-learning method of combined bagging and stacking techniques. The result is a new ensemble technique that integrates the existing ensemble methods of bagging and stacking to solve the problems of a single machine-learning model and improve the prediction performance of the model. The nonlinear regression, support vector machine, artificial neural network, and Gaussian process regression approaches were used as single machine-learning methods and bagging and stacking techniques as ensemble machine-learning methods. As a result, the model of the proposed method showed improved accuracy results compared with single machine-learning models, an individual bagging technique model, and a stacking technique model. This was confirmed through a comparison of four representative performance indicators, verifying the effectiveness of the method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• 제30권8호
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• pp.56-64
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• 2002

In this work, a smart skin of multi-layer structure is designed and manufactured. Through the compression test, the characteristic of smart skin behavior was examined. We have predicted stress of each layer and the first failed layer of the smart skin structure by using MSC/NASTRAN. The finite element model was verified by comparing measured data from the compression test and result from the geometrically linear/non-linear analysis. The finite element model was used for obtaining design data from the parametric study. It was confirmed that shear moduli of honeycomb core affect the buckling load of smart skin where shear deformation was considerable.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제12권1호
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• pp.45-54
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• 2011

This paper discuss about structure modification method of the reciprocating compressor to reduce its vibration and noise in small refrigeration system. The structure modification is applied using analytic FE models and then applying suggested Component Mode Synthesis(CMS) algorithms. The efficient CMS algorithms to a compressor's fixed base design problem are analytically tried and verified from some experiments.

• The Journal of the Acoustical Society of Korea
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• 제19권3호
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• pp.11-19
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• 2000

This research deals with the implementation procedures of a chorus processing DSP for karaoke system. It is necessary to compress the chorus data to store as many choruses as we can. We apply MPEG-1 audio algorithm to compress the chorus data. And the chorus system must be accompanied with the karaoke that can change the key and the tempo. So the chorus DSP must be able to change the key and tempo of the chorus data. We apply SOLA (Synchronized Overlap and Add) to do it. We designed the chorus DSP that can compress the chorus, change the key and tempo. And we verified the chorus DSP logic using FPGA. The used FPGA are two FLEX10K100s made by ALTERA. Finally we make the ASIC chip of chorus DSP and verify its operation.

• Journal of KIISE:Computing Practices and Letters
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• 제9권5호
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• pp.509-520
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• 2003

In this paper, we developed a new and robust algorithm for a practical and very efficient MPEG-4 video coding. The MPEG-4 video group has developed the video Verification Model(VM) which evolved through time by means of core experiments. And in the standardization process, MS-FDAM was developed based on the standard document of ISO/IEC 14496-2 and VM as a reference MPEG-4 coding system. But MS -FDAM has drawbacks in practical MPEG-4 coding and it does not have the VOP extraction functionality. In this research, we implemented a preprocessing system for a real-time input and the VOP extraction for a practical content-based MPEG-4 video coding and also implemented the motion detection to achieve the high compression rate of 180:1.

• The Transactions of the Korea Information Processing Society
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• 제5권6호
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• pp.1431-1440
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• 1998

Video data are retrieved and stored in various compressed forms according to their characteristics, In this paper, we present a generic data model that captures the structure of a video document and that provides a means for indexing a video stream, Using this model, we design and implement CVIMS (the MPEG-2 Compressed Video Information Management System) to store and retrieve video documents, CVIMS extracts I-frames from MPEG-2 files, selects key-frames from the I -frames, and stores in database the index information such as thumbnails, captions, and picture descriptors of the key-frames, And also, CVIMS retrieves MPEG- 2 video data using the thumbnails of key-frames and v31ious labels of queries.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• 제9권2호
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• pp.139-149
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• 1991

In general, Digital Elevation Models(DEM) are constructed in a grid format for this form is advantageous to capture data automatically and is easy to manipulate. But, grid DEM requires vast volumes of data to represent terrain features finely and accurately as its data is sampled in a regular space. This paper aims at constructing compact DEM by selecting the significant points from grid DEM which affect well the terrain features. For the purpose, the significant points is detected by the high-pass filtering using Laplacian operator and gradient operator. The results of this study show that the Laplacian operator is more efficient than the gradient operator in selecting the significant points for compact DEM.

• Broadcasting and Media Magazine
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• 제15권4호
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• pp.45-54
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• 2010

최근 ISO/IEC와 ITU는 공동협력팀(Joint Collaborative Team on Video Coding-JCT-VC)을 구성하여 HEVC(High Efficiency Video Coding)라 불리는 새로운 비디오 압축 표준 기술을 개발하고 있다. JCT-VC의 목표 중 하나는 H.264/AVC 압축률의 2배를 향상하는 것으로 최근 HEVC 테스트 모델(HEVC Test Model - HM)을 확정했다. HM의 여러 기술 중에서 확장 블록 구조 (large block structure) 기술은 CTB(Coded Tree Block)와 TU(Transform Unit), PU(Partition Unit)로 구성된다. CTB와 TU는 압축 단위와 변환 기술을 확장한 반복적인 문법구조(recursive syntax structure)이며, PU는 H.264/AVC과 동일한형태를 띈다. 확장 블록 구조는CTB, PU, TU의 여러 조합에 의해 다양한 모드를 지원하여 압축 성능은 높아졌지만 HM 부호화기의 복잡도는 증가한다. 본 논문에서는 HM에 채택된 확장블록구조 및 변환 기술에 대해 설명한 후, TMuC 및 HM의 테스트 영상을 이용하여 다양한 최대 CTB 및 TU 크기의 압축성능 및 선택비율을 분석한다.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 한국에너지공학회 1997년도 추계학술발표회 논문집
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• pp.99-104
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• 1997

기존의 천연가스 가스터빈 시스템을 IGCC 발전소에 적용함에 있어 야기되는 탈설계점효과를 고려할 수 있는 압축기 성능곡선의 예측방법을 제안하였다. 압축기 성능해석방법으로는 익렬요소방법에 전압력손실, 유동편차각 모델들을 결합하여 사용하였으며, 본 방법에 의한 예측결과와 실제 압축기 성능실험결과를 비교하였다. 예측결과가 다양한 압축기 운전조건에 대해 시험결과와 비교적 잘 일치하였으며, 이를 통해 본 예측방법이 IGCC 공정설계 및 성능평가시 가스터빈 탈설계점 효과를 분석할 수 있는 기본 모듈로 사용될 수 있을 것이다.