• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.448-448
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• 2018

In this study, the regression models (Load ESTimator and eight-parameter model) were evaluated to estimate instantaneous pollutant loads under various criteria and optimization methods. As shown in the results, LOADEST commonly used in interpolating pollutant loads could not necessarily provide the best results with the automatic selected regression model. It is inferred that the various regression models in LOADEST need to be considered to find the best solution based on the characteristics of watersheds applied. The recently developed eight-parameter model integrated with Genetic Algorithm (GA) and Gradient Descent Method (GDM) were also compared with LOADEST indicating that the eight-parameter model performed better than LOADEST, but it showed different behaviors in calibration and validation. The eight-parameter model with GDM could reproduce the nitrogen loads properly outside of calibration period (validation). Furthermore, the accuracy and precision of model estimations were evaluated using various criteria (e.g., $R^2$ and gradient and constant of linear regression line). The results showed higher precisions with the $R^2$ values closed to 1.0 in LOADEST and better accuracy with the constants (in linear regression line) closed to 0.0 in the eight-parameter model with GDM. In hence, based on these finding we recommend that users need to evaluate the regression models under various criteria and calibration methods to provide the more accurate and precise results for pollutant load estimations.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.4
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• pp.153-162
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• 2019

Recently, it is necessary to develop a technology for quantitatively evaluating railway vibration to prevent civil complaints about orbital structures caused by railway noise and normal operation of ultra-precise equipment of orbital industrial complexes. The existing analytical method requires a very complicated dynamic response model, and it is difficult to secure the reliability of the result due to the inaccuracy of the demand model. Therefore, in this paper, we propose a railway vibration evaluation algorithm and system that deduce the vibration value generated from railway operation by using Linear Regression and Gradient Descent technique based on actual measurement railway vibration database that classifies factors affecting railway vibration. The prediction results obtained by the proposed algorithm show higher efficiency and accuracy than the existing analytical methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.334-340
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• 2002

This paper proposes a self-organizing fuzzy modeling which can create a new hyperplane-shaped cluster by applying multiple regression to input/output data with relatively large fuzzy entropy, add the new cluster to fuzzy rule base and adjust parameters of the fuzzy model in repetition. Tn the coarse tuning, weighted recursive least squared algorithm and fuzzy C-regression model clustering are used and in the fine tuning, gradient descent algorithm is used to adjust parameters of the fuzzy model precisely And learning rates are optimized by utilizing meiosis-genetic algorithm. To check the effectiveness and feasibility of the suggested algorithm, four representative examples for system identification are examined and the performance of the identified fuzzy model is demonstrated in comparison with that of the conventional fuzzy models.

• Structural Engineering and Mechanics
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• v.45 no.1
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• pp.53-67
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• 2013

This study aimed to develop a model to accurately predict the acceleration of structural systems during an earthquake. The acceleration and applied force of a structure were measured at current time step and the velocity and displacement were estimated through linear integration. These data were used as input to predict the structural acceleration at next time step. The computation tool used was the Volterra/Wiener neural network (VWNN) which contained the mathematical model to predict the acceleration. For alleviating problems of relatively large-dimensional and nonlinear systems, the VWNN model was utilized as the signal processing tool, including the Taylor series components in the input nodes of the neural network. The number of the intermediate layer nodes in the neural network model, containing the training and simulation stage, was evaluated and optimized. Discussions on the influences of the gradient descent with adaptive learning rate algorithm and the Levenberg-Marquardt algorithm, both for determining the network weights, on prediction errors were provided. During the simulation stage, different earthquake excitations were tested with the optimized settings acquired from the training stage to find out which of the algorithms would result in the smallest error, to determine a proper simulation model.

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.443-458
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• 2014

An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.4
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• pp.805-814
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• 1998

An efficient image processing algorithm is proposed to recognize both the height and width modulated barcodes which are rotated and printed at an arbitrary position. The main feature of this algorithm is to utilize the gradient information of a rotated barcode with a Sobel operator. The barcode area is extracted using the gradient information, and the barcode is decoded from the binary image of the extracted area. Theis algorithm is successfully applied to the 4 state and width modulated barcodes. It takes 0.86 secoden to process a letter, and the recognition rate reaches above 98% under various testing conditions. Since both the width and height modulated barcodes are processed with the proposed algorithm, it can be applied to postal service automation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1918-1924
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• 2010

This In this paper, we propose an efficient algorithm for motion estimation of the image sequences with luminance fluctuation. For such sequences, conventional motion estimation methods based on the difference of pixel values usually produce the erroneous motion information. The proposed algorithm defines the luminance fluctuation as a linear model with gain and offset parameter, and extracts motion information using gradient and phase of the corresponding local region within consecutive frames. Therefor the method is robust to the luminance change of the frames. We test our algorithm for the ground truth sequence with artificially added luminance change and motion, and real sequences corrupted by the flicker. The results shows that the proposed algorithm outperforms the conventional methods.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3703-3709
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• 1999

We propose a new approach to reconstruct the surface shape of an object from a shaded image. We use genetic algorithm instead of gradient descent algorithm which is apt to take to local minima and also proposes genetic representation and suitable genetic operators for manipulating 2-D image. And for more effective execution, we suggest hierarchical process to reconstruct minutely the surface of an object after coarse and global reconstruction. A modified Lambertian illumination model including the distance factor was herein adopted to get more reasonable result and an experiment was performed with synthesized and real images to demonstrate the devised method, of which results show the usefulness of our method.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.152-153
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• 2008

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.5
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• pp.610-620
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• 1999

Recently, numerous modifications of low Reynolds number $k-{\epsilon}$ model have boon carried out with the aid of DNS data. However, the previous models made in this way are too intricate to be used practically. To overcome this shortcoming, a new low Reynolds number $k-{\epsilon}$ model has boon developed by considering the distribution of turbulent properties near the wall. This study proposes the revised a turbulence model for prediction of turbulent flow with adverse pressure gradient and separation. Nondimensional distance $y^+$ in damping functions is changed to $y^*$ and some terms modeled for one dimensional flow in $\epsilon$ equations are expanded into two or three dimensional form. Predicted results by the revised model show an acceptable agreement with DNS data and experimental results. However, for a turbulent flow with severe adverse pressure gradient, an additive term reflecting an adverse pressure gradient effect will have to be considered.