• Journal of the Society of Naval Architects of Korea
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• v.40 no.5
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• pp.43-52
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• 2003

Two methods which estimate manoeuvring derivatives in the model of hydrodynamic force and moment acting on a manoeuvring ship using sea trial data were compared. One is the widely used parameter estimation method by using the Extended Kalman Filter (EKF), which estimates state variables of linearized state space model at every instant after dealing with the coefficients as the augmented state variables. The other one is the Estimation-Before-Modeling (EBM) technique, so called the two-step method. In the first step, hydrodynamic force of which dynamic model is assumed the third-order Gauss-Markov process is estimated along with motion variables by the EKF and the modified Bryson-Frazier smoother. Then, in the next step, manoeuvring derivatives are identified through the regression analysis. If the exact structure of hydrodynamic force could be known, which was an ideal case, the EKF method would be regarded as being more superior compared to the EBM technique. However the EBM technique was more robust than the EKF method from a realistic point of view where the assumed model structure was slightly different from the real one.

• ETRI Journal
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• v.39 no.5
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• pp.643-651
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• 2017

Human age estimation is one of the key factors in the field of Human-Robot Interaction/Human-Computer Interaction (HRI/HCI). Owing to the development of deep-learning technologies, age recognition has recently been attempted. In general, however, deep learning techniques require a large-scale database, and for age learning with variations, a conventional database is insufficient. For this reason, we propose an age estimation method using artificially generated data. Image data are artificially generated through 3D information, thus solving the problem of shortage of training data, and helping with the training of the deep-learning technique. Augmentation using 3D has advantages over 2D because it creates new images with more information. We use a deep architecture as a pre-trained model, and improve the estimation capacity using artificially augmented training images. The deep architecture can outperform traditional estimation methods, and the improved method showed increased reliability. We have achieved state-of-the-art performance using the proposed method in the Morph-II dataset and have proven that the proposed method can be used effectively using the Adience dataset.

• Computers and Concrete
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• v.11 no.4
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• pp.337-350
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• 2013

The sensitivity of compressive strength of no-slump concrete to its ingredient materials and proportions, necessitate the use of robust models to guarantee both estimation and generalization features. It was known that the problem of compressive strength prediction owes high degree of complexity and uncertainty due to the variable nature of materials, workmanship quality, etc. Moreover, using the chemical and mineral additives, superimposes the problem's complexity. Traditionally this property of concrete is predicted by conventional linear or nonlinear regression models. In general, these models comprise lower accuracy and in most cases they fail to meet the extrapolation accuracy and generalization requirements. Recently, artificial intelligence-based robust systems have been successfully implemented in this area. In this regard, this paper aims to investigate the use of optimized support vector machine (SVM) to predict the compressive strength of no-slump concrete and compare with optimized neural network (ANN). The results showed that after optimization process, both models are applicable for prediction purposes with similar high-qualities of estimation and generalization norms; however, it was indicated that optimization and modeling with SVM is very rapid than ANN models.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.7
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• pp.1-15
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• 2022

Combining the strength of both Mixed Data Sampling (MIDAS) Regression and realized variance measures, this paper seeks to investigate two objectives: (1) evaluate the post-sample performance of the proposed weekly Realized Variance-MIDAS (RVar-MIDAS) in one-week ahead volatility forecasting against the established Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the less explored but robust STES (Smooth Transition Exponential Smoothing) methods. (2) comparing forecast error performance between realized variance and squared residuals measures as a proxy for actual volatility. Data of seven private equity mutual fund indices (generated from 57 individual funds) from two different time periods (with and without financial crisis) are applied to 21 models. Robustness of the post-sample volatility forecasting of all models is validated by the Model Confidence Set (MCS) Procedures and revealed: (1) The weekly RVar-MIDAS model emerged as the best model, outperformed the robust DAILY-STES methods, and the weekly DAILY-GARCH models, particularly during a volatile period. (2) models with realized variance measured in estimation and as a proxy for actual volatility outperformed those using squared residual. This study contributes an empirical approach to one-week ahead volatility forecasting of mutual funds return, which is less explored in past literature on financial volatility forecasting compared to stocks volatility.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.3
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• pp.51-61
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• 2014

In this paper, we deal with developing a cost estimation relationships (CER) for Korean maneuverable weapons systems using historical production cost. To develop the CER, we collected the historical data of the production cost of four tanks and five armored vehicles. We also analyzed the Required Operational Capability (ROC) of the weapons systems and chose cost drivers that can compare operational capabilities of the weapons systems We used Forward selection, Backward selection, Stepwise Regression and $R^2$ selection as the cost drivers which have the greatest influence with the dependent variables. And we used Principle Component Regression, Robust Regression and Weighted Regression to deal with multicollinearity and outlier among the data to develop a more appropriate CER. As a result, we were able to develop a production cost CER for Korean maneuverable weapons systems that have the lowest cost errors. Thus, this research is meaningful in terms of developing a CER based on Korean original cost data without foreign data and these methods will contribute to developing a Korean cost analysis program in the future.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.25-34
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• 2008

By comparing two images, which are captured with the same scene at different time, we can detect a set of outliers, such as occluding objects due to moving vehicles. To reduce the influence from the different intensity properties of the images, an intensity compensation scheme, which is based on the polynomial regression model, is employed. For an accurate detection of outliers alleviating the influence from a set of outliers, a simple technique that reruns the regression is employed. In this paper, an algorithm that iteratively reruns the regression is theoretically analyzed by observing the convergence property of the estimates of the noise variance. Using a correction constant for the estimate of the noise variance is proposed. The correction enables the detection algorithm robust to the choice of thresholds for selecting outliers. Numerical analysis using both synthetic and Teal images are also shown in this paper to show the robust performance of the detection algorithm.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.345-355
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• 1998

The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data are employed. In this paper two types of robust backpropagation algorithms are discussed both from a theoretical point of view and in the case studies of nonlinear regression function estimation and handwritten Korean character recognition. For future research we suggest Bayesian learning approach to neural networks and compare it with two robust backpropagation algorithms.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.305-317
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• 2006

Outliers adversely affect model identification, parameter estimation, and forecast in time series data. In particular, when outliers consist of a patch of additive outliers, the current outlier detection procedures suffer from the masking and swamping effects which make them inefficient. In this paper, we propose new outlier detection procedure based on high breakdown estimators, called as the dual robust filtering. Empirical and simulation studies in the autoregressive model with orders p show that the proposed procedure is effective.

• Journal of Biosystems Engineering
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• v.40 no.3
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• pp.296-304
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• 2015

Purpose: The balance between miscanthus production and its cost effectiveness depends greatly on its moisture content during post processing. The objective of this research was to measure the moisture content using a non-destructive and non-contact methodology for in situ applications. Methods: The moisture content of comminuted miscanthus was controlled using a closed chamber, a humidifier, a precision weigher, and a real-time monitoring software developed in this research. A CMOS sensor equipped with $50{\times}$ magnifier lens was used to capture magnified images of the conditioned materials with moisture content level from 5 to 30%. The hypothesis is that when light is incident on the comminuted particles in an inclined manner, higher moisture content results in light being reflected with a higher intensity. Results: A linear regression analysis for an initiative hypothesis based on general histogram analysis yielded insufficient correlations with low significance level (<0.31) for the determination coefficient. A significant relationship (94% confidence level) was determined at level 108 in a reverse accumulative histogram proposed based on a revised hypothesis. A linear regression model with the value at level 108 in the reverse accumulative histogram for a magnified image as the independent variable and the moisture content of comminuted miscanthus as the dependent variable was proposed as the estimation model. The calibrated linear regression model with a slope of 92.054 and an offset of 32.752 yielded 0.94 for the determination coefficient (RMSE = 0.2%). The validation test showed a significant relationship at the 74% confidence level with RMSE 6.4% (n = 36). Conclusions: To compensate the inconsistent significance between calibration and validation, an estimation model robust against various systematic interferences is necessary. The economic efficiency of miscanthus, which is a promising energy resource, can be improved by the real-time measurement of its crucial material properties.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.295-298
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• 1996

In distillation column control, secondary measurements such as temperatures and flows are widely used in order to infer product composition. This paper addresses the design of static estimators using the secondary measurements for estimating the product compositions of the multicomponent distillation columns. Based on the unified framework for the estimator problems, the relationships among several typical static estimators are discussed including the effect of the measured inputs. Design guidelines for the composition estimator using PLS regression are also presented. The estimator based on the guidelines is robust to sensor noise and has a good predictive power.