• Title/Summary/Keyword: Correlation model

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위성 탑재체 구조물의 최적화 기반 모델 보정 (Optimization-based model correlation of satellite payload structure)

  • 윤도희
    • 항공우주시스템공학회지
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    • 제18권2호
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    • pp.104-116
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    • 2024
  • 인공위성은 발사체 모델과 연성하중해석을 수행하여 설계를 최종 검증하게 된다. 연성하중해석 결과의 정확도를 높이기 위해서는 유한요소모델 정확도가 매우 중요하며, 이를 위해 모델 보정은 필수적이다. 일반적으로 모델 보정은 재료 물성치와 두께 등을 하나씩 바꿔가며 수행하게 되는데, 이는 매우 많은 시간과 비용이 소요된다. 따라서 본 논문에서는 최적화 기법을 이용하여 탑재체 유한요소모델의 보정작업을 보다 효율적으로 수행하였다. 분산분석을 통해 중요 변수를 선정하고, 크리깅 대체 모델을 이용하여 해석과 최적화에 필요한 시간과 비용을 절감하였다. 본 논문에서 제안한 보정 방법은 진동 시험 결과만 있으면 적용할 수 있으며, 수치적인 계산 비용과 소요 시간을 대폭 줄일 수 있다는 점에서 효율성 측면에서 큰 장점이 있다.

유한요소 모델 검증 및 개선 (Correlation and Update of Finite Element Model)

  • 왕세명;고창성
    • 한국전산구조공학회:학술대회논문집
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    • 한국전산구조공학회 2000년도 봄 학술발표회논문집
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    • pp.195-204
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    • 2000
  • The finite element analysis (FEA) is widely used in modern structural dynamics because the performance of structure can be predicted in early stage. However, due to the difficulty in determination of various uncertain parameters, it is not easy to obtain a reliable finite element model. To overcome these difficulties, a updating program of FE model is developed by consisting of pretest, correlation and update. In correlation, it calculates modal assurance criteria, cross orthogonality, mixed orthogonality and coordinate modal assurance criteria. For the model updating, the continuum sensitivity analysis and design optimization tool(DOT) are used. The SENSUP program is developed for model updating giving physical parameter sensitivity. The developed program is applied to practical examples such as the BLDC spindle motor of HDD, and upper housing of induction motor. And the sensor placement for the square plate is compared using several methods.

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Adaptive Correlation Noise Model for DC Coefficients in Wyner-Ziv Video Coding

  • Qin, Hao;Song, Bin;Zhao, Yue;Liu, Haihua
    • ETRI Journal
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    • 제34권2호
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    • pp.190-198
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    • 2012
  • An adaptive correlation noise model (CNM) construction algorithm is proposed in this paper to increase the efficiency of parity bits for correcting errors of the side information in transform domain Wyner-Ziv (WZ) video coding. The proposed algorithm introduces two techniques to improve the accuracy of the CNM. First, it calculates the mean of direct current (DC) coefficients of the original WZ frame at the encoder and uses it to assist the decoder to calculate the CNM parameters. Second, by considering the statistical property of the transform domain correlation noise and the motion characteristic of the frame, the algorithm adaptively models the DC coefficients of the correlation noise with the Gaussian distribution for the low motion frames and the Laplacian distribution for the high motion frames, respectively. With these techniques, the proposed algorithm is able to make a more accurate approximation to the real distribution of the correlation noise at the expense of a very slight increment to the coding complexity. The simulation results show that the proposed algorithm can improve the average peak signal-to-noise ratio of the decoded WZ frames by 0.5 dB to 1.5 dB.

Correlated damage probabilities of bridges in seismic risk assessment of transportation networks: Case study, Tehran

  • Shahin Borzoo;Morteza Bastami;Afshin Fallah;Alireza Garakaninezhad;Morteza Abbasnejadfard
    • Earthquakes and Structures
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    • 제26권2호
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    • pp.87-96
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    • 2024
  • This paper proposes a logistic multinomial regression approach to model the spatial cross-correlation of damage probabilities among different damage states in an expanded transportation network. Utilizing Bayesian theory and the multinomial logistic model, we analyze the damage states and probabilities of bridges while incorporating damage correlation. This correlation is considered both between bridges in a network and within each bridge's damage states. The correlation model of damage probabilities is applied to the seismic assessment of a portion of Tehran's transportation network, encompassing 26 bridges. Additionally, we introduce extra daily traffic time (EDTT) as an operational parameter of the transportation network and employ the shortest path algorithm to determine the path between two nodes. Our results demonstrate that incorporating the correlation of damage probabilities reduces the travel time of the selected network. The average decrease in travel time for the correlated case compared to the uncorrelated case, using two selected EDTT models, is 53% and 71%, respectively.

Calculation of Effective Angular Correlation in the HPGe Spectroscopy of Co-60 $\gamma$-rays

  • Kim, In-Jung;Sun, Gwang-Min;Park, H. D.;Bae, Young-Dug
    • Nuclear Engineering and Technology
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    • 제34권1호
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    • pp.22-29
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    • 2002
  • The angular correlation effect was investigated for Co-60 ${\gamma}$-ray spectroscopy by using HPGe detector and the effective angular correlation was theoretically calculated by considering the finite detector solid angle. For the calculation of effective angular correlation, the detection efficiency as a function of ${\gamma}$-ray incident direction was obtained by using Monte Carlo method and the first interaction model. The results and the methods used in the calculation are discussed.

토양특성이 상수도관의 외부부식에 미치는 영향 평가 (Assessment of Soil Characteristics on External Corrosion of Water Pipes)

  • 배철호;김주환;박상영;김정현;홍성호;이경재
    • 상하수도학회지
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    • 제20권5호
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    • pp.737-745
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    • 2006
  • The goal of this study is to present an external pit corrosion rate($p_{ecr}$) model with considering both the age of pipe and the soil characteristics. The correlation of nonlinear exponential model among conventional empirical models was a little higher than other empirical models in the prediction of $p_{ecr}$ according to the age of pipe. However, there has been a limit to predict Peer with the model by using only a pipe age since installation as a variable. The soil analysis results from sixty nine samples showed that all of the samples were non corrosive in the assessment of ANSI/AWWA scoring system. The correlation of soil corrosion factors and $p_{ecr}$ was also low. The application result of linear and nonlinear regression models that soil characteristics only showed a low correlation with $p_{ecr}$ Proposed nonlinear regression model in this study, with considering both the age of pipe and the soil characteristics, showed a little higher correlation ($R^2=0.46$) than conventional model.

Derivation of Mechanistic Critical Heat Flux Model and Correlation for Water Based on Flow Excursion

  • Chang, Soon-Heung;Kim, Yun-Il;Baek, Won-Pil
    • 한국원자력학회:학술대회논문집
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    • 한국원자력학회 1996년도 춘계학술발표회논문집(2)
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    • pp.349-355
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    • 1996
  • In this study, the mechanistic critical heat flux (CHF) model and correlation for water are derived based on flow excursion (or Ledinegg instability) criterion and the simplified two-phase homogeneous model. The relationship between CHF for the water and the principal parameters such as mass flux heat of vaporization, heated length-to-diameter ratio, vapor-liquid density ratio and inlet subcooling is derived on the developed correlation. The developed CHF correlation predicts very well at the applicable ranges, 1 < P < 40 bar, 1, 300 < G 27, 00 kg/$m^2$s and inlet quality is less than -0.1. The overall mean ratio of predicted to experimental CHF value is 0.988 with standard deviation of 0.046.

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A Model for Correlation of Various Solvatochromic Parameters with Composition in Aqueous and Organic Binary Solvent Systems

  • Aziz, Habibi-Yangjeh
    • Bulletin of the Korean Chemical Society
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    • 제25권8호
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    • pp.1165-1170
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    • 2004
  • The applicability of the combined nearly ideal binary solvent/Redlich-Kister (CNIBS/R-K) equation for correlation of various solvatochromic parameters (SP) with composition is shown employing 84 experimental data sets for aqueous and organic binary solvent systems at temperatures ranging 15 to $75^{\circ}C$. The model provides a simple computational model to correlate/predict different SP values in various binary solvent systems. In proposed equations, $MPD_s$ (mean percentage deviations) are between 0.0500% and 6.9591% in mixtures of dimethyl sulfoxide with 2-methylpropan-2-ol and benzene with 2-methylpropan-2-ol, respectively. Correlation of the calculated and experimental values of various SP give an equation with an overall mean percentage deviation (OMPD) of 1.1900, $R^2$ = 0.99692, s.e = 0.01223 and F = 341925.51. Approximately 70% of the calculated SP values have IPD (individual percentage deviation) lower than one and it is possible to predict unmeasured SP values by using only eight experimental data.

다중 특징 기반 입자필터를 이용한 강건한 영상객체 추적 (Multiple Cues Based Particle Filter for Robust Tracking)

  • ;이칠우
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2012년도 추계학술발표대회
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    • pp.552-555
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    • 2012
  • The main goal of this paper is to develop a robust visual tracking algorithm with particle filtering. Visual Tracking with particle filter technique is not easy task due to cluttered environment, illumination changes. To deal with these problems, we develop an efficient observation model for target tracking with particle filter. We develop a robust phase correlation combined with motion information based observation model for particle filter framework. Phase correlation provides straight-forward estimation of rigid translational motion between two images, which is based on the well-known Fourier shift property. Phase correlation has the advantage that it is not affected by any intensity or contrast differences between two images. On the other hand, motion cue is also very well known technique and widely used due to its simplicity. Therefore, we apply the phase correlation integrated with motion information in particle filter framework for robust tracking. In experimental results, we show that tracking with multiple cues based model provides more reliable performance than single cue.

CNN-LSTM Coupled Model for Prediction of Waterworks Operation Data

  • Cao, Kerang;Kim, Hangyung;Hwang, Chulhyun;Jung, Hoekyung
    • Journal of Information Processing Systems
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    • 제14권6호
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    • pp.1508-1520
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    • 2018
  • In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.