• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.350-355
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• 1997

The problem of mathematical model for an unknown system by measureing its input-output data pairs is generally referred to as state estimates. The state estimation problem is often of importance in its own right since we may want to know the value of the states. For instance, in navigation, we may take noisy positional fixes using satelite or radar navigation, and the estimator can use these measurements to provide accurate estimates of current position, hedaing, and velocity. And the state estimates can also be used for control purposes. Then it is very important to know the state of plant. In this paper, the theory of the minimization of a loss function was used to design the fuzzy system. Here, the used teory is Least Square Esimation method. This parametrization has the Linear in the parameters charcteristic that allows standard parameter estimation technique to be used to estimate the parameters of the fuzzy system. The combination of the fuzzy system and the estimation m thod then performs as a nonlinear estimator. If several fuzzy label are defined for the input variables at the antecedent part, the fuzzy system then behaves as a collection of nonlinear estimators where different regions of rules have different parameters. In simulation results, the fuzzy model controlled a difference in the structure between the actual plant and the fuzzy estimator. It is also proved that the fuzzy system is equivalent to its transformed system. therefore we was able to get the state space equation of system with the estimated paramater.

• Design & Manufacturing
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• v.14 no.3
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• pp.1-7
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• 2020

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.7
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• pp.378-388
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• 2000

In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed.

• International Journal of Contents
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• v.10 no.3
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• pp.41-46
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• 2014

A cause-effect graph considers only the desired external behavior of a system by identifying input-output parameter relationships in the specification. When testing a software system with cause-effect graphs, it is important to derive a moderate number of tests while avoiding loss in fault detection ability. Pairwise testing is known to be effective in determining errors while considering only a small portion of the input space. In this paper, we present a new testing technique that generates pairwise tests from a cause-effect graph. We use a Boolean Satisbiability (SAT) solver to generate pairwise tests from a cause-effect graph. The Alloy language is used for encoding the cause-effect graphs and its SAT solver is applied to generate the pairwise tests. Using a SAT solver allows us to effectively manage constraints over the input parameters and facilitates the generation of pairwise tests, even in the situations where other techniques fail to satisfy full pairwise coverage.

• Microbiology and Biotechnology Letters
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• v.21 no.4
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• pp.354-365
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• 1993

A novel control method with automatic tuning of PID controller parameters has been developed for efficient regulation of dissolved oxygen concentration in fed-batch fermentations of Escherichia coli. Agitation speed and oxygen partial pressure in the inlet gas stream were chosen to be the manipulated variables. A heuristic reasoning allowed improved tuning decisions from the supervision of control performance indices and it coule obviate the needs for process assumptions or disturbance patterns. The control input consisted of feedback and feedforword parts. The feedback part was determined by PID control and the feedforward part is determined from the feed rate. The proportional gain was updated on-line by a set of heuristics rules based on the supervision of three performance indices. These indices were output error covariance, the average value of output error, and input covariance, which were calculated on-line using a moving window. The integral and derivative time constants were determined from the period of output response. The specific growth rate was maintained at a low level to avoid acetic acid accumulation and thus to achieve a high cell density. The specific growthe rate was estimated from the carbon dioxide evolution rate. In fed-batch fermentation, the simutaneous control of dissolved oxygen concentration (at 0.2; fraction of saturated value) and specific growth rate (at 0.25$hr^{-1}$) was satisfactory for the entire culture period in spite of the changes in the feed rate and the switching of control input.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.246-246
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• 2023

Multi-purpose dams are operated accounting for both physical and socioeconomic factors. This study aims to evaluate the utility of a deep learning algorithm-based model for three multi-purpose dam operation (Seomjin River dam, Juam dam, and Juam Control dam) in Seomjin River. In this study, the Gated Recurrent Unit (GRU) algorithm is applied to predict hourly water level of the dam reservoirs over 2002-2021. The hyper-parameters are optimized by the Bayesian optimization algorithm to enhance the prediction skill of the GRU model. The GRU models are set by the following cases: single dam input - single dam output (S-S), multi-dam input - single dam output (M-S), and multi-dam input - multi-dam output (M-M). Results show that the S-S cases with the local dam information have the highest accuracy above 0.8 of NSE. Results from the M-S and M-M model cases confirm that upstream dam information can bring important information for downstream dam operation prediction. The S-S models are simulated with altered outflows (-40% to +40%) to generate the simulated water level of the dam reservoir as alternative dam operational scenarios. The alternative S-S model simulations show physically inconsistent results, indicating that our deep learning algorithm-based model is not explainable for multi-purpose dam operation patterns. To better understand this limitation, we further analyze the relationship between observed water level and outflow of each dam. Results show that complexity in outflow-water level relationship causes the limited predictability of the GRU algorithm-based model. This study highlights the importance of socioeconomic factors from hidden multi-purpose dam operation processes on not only physical processes-based modeling but also aritificial intelligence modeling.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.4
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• pp.109-121
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• 1996

This paper presents how the input forces along the prismatic joints of a parallel manipulator are transmitted to the upper platform. In order to consider force transmission and moment transmission seperately the Jacobian matrix for parallel manipulators is splitted into two parts. Magnitudes of input forces on the six actuators at a given manipulator configuration which generate maximum/minimum output force with no moment generated on the platform are obtained through the singular value decomposition of a matrix involving the Jacobian. Similarly the directions of the input forces to obtain only the rotation of the platform have been analyzed. Using the singular values a simple equation for the volume of ellipsoid which is a good tool for manipulability measure is provided. Obtained results could be useful in determinimg design parameters like radius of plaform, angles between joints, etc. Simulations are porvided.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.436-444
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• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.