• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.62-62
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• 2000

An auto-tuning PID controller which is adequate for temperature control is developed based on relay-control and pole-placement Using the critical frequency which is obtained from relay-control parameters of assumed model are identified. Pole/zero-placement PID controller is designed for the identified model. The desired pole/zeros are determined so that the closed-loop has overshoot free step response. The developed auto-tuning PID controller was successfully applied to the temperature control of RTP.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2167-2170
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• 2002

In this paper, model-based tuning rules of the PID controller are proposed incorporating with genetic algorithms. Three sets of optimal PID parameters for set-point tracking are obtained based on the first-order time delay model and a genetic algorithm as a optimization tool which minimizes performance indices(IAE, ISE and ITAE). Then tuning rules are derived using the tuned parameter sets, potential rule models and a genetic algorithm. Simulation is carried out to verify the effectiveness of the proposed rules.

• 한국조명전기설비학회지:조명전기설비
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• 제9권6호
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• pp.71-77
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• 1995

This paper deals with design method of learning fuzzy controller for control of an unknown nonlinear plant using the self-tuning algorithm of fuzzy inference rules. In this method the fuzzy identification model obtained that the joined identification model of nonlinear part and linear identification model of linear part by fuzzy inference systems. This fuzzy identification model ordered self-tuning by Decent method so as to be servile to nonlinear plant. A the end, designed learning fuzzy controller of fuzzy identification model have learning structure to model reference adaptive system. The simulation results show that th suggested identification and learning control schemes are practically feasible and effective.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2114-2116
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• 2001

Model reduction from high order systems to low order systems in frequency domain is considered four point (${\angle}$G(jw)=0, - ${\pi}/2$, ${\pi}$, and -3${\pi}$/2) instead of two point (${\angle}$G(jw) = - ${\pi}$/2,- ${\pi}$) of existing method in Nyquist curve. The Performances of reduced order model by proposed approach is similar to original model. In this paper, we proposed a new tuning algorithm for PID controller using model reduction in frequency domain. Simulations for some examples with varies dynamic characteristics are provided to show the effectiveness of the proposed tuning algorithm for PID controller using model reduction.

• Quantitative Bio-Science
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• 제37권2호
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• pp.133-141
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• 2018

Graphical lasso is one of the most popular methods to estimate a sparse precision matrix, which is an inverse of a covariance matrix. The objective function of graphical lasso imposes an ${\ell}_1$-penalty on the (vectorized) precision matrix, where a tuning parameter controls the strength of the penalization. The selection of the tuning parameter is practically and theoretically important since the performance of the estimation depends on an appropriate choice of tuning parameter. While information criteria (e.g. AIC, BIC, or extended BIC) have been widely used, they require an asymptotically unbiased estimator to select optimal tuning parameter. Thus, the biasedness of the ${\ell}_1$-regularized estimate in the graphical lasso may lead to a suboptimal tuning. In this paper, we propose a two-staged bias-correction procedure for the graphical lasso, where the first stage runs the usual graphical lasso and the second stage reruns the procedure with an additional constraint that zero estimates at the first stage remain zero. Our simulation and real data example show that the proposed bias correction improved on both edge recovery and estimation error compared to the single-staged graphical lasso.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.196-202
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• 2024

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer's favorable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model's overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, and finally the F1 score. In this study hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.701-706
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• 1998

A design method of rule -based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of " IF..., THEN,," statements. using the theories of optimization and linguistic fuzzy implication rules. The improved complex method, which is a powerful auto-tuning algorithm, is used for tuning of parameters of the premise membership functions in consideration of the overall structure of fuzzy rules. The optimized objective function, including the weighting factors, is auto-tuned for better performance of fuzzy model using training data and testing data. According to the adjustment of each weighting factor of training and testing data, we can construct the optimal fuzzy model from the objective function. The least square method is utilized for the identification of optimum consequence parameters. Gas furance and a sewage treatment proce s are used to evaluate the performance of the proposed rule-based fuzzy modeling.

• 한국컴퓨터정보학회논문지
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• 제24권1호
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• pp.137-143
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• 2019

As the performance of computers increases, the use of deep learning, which has faced technical limitations in the past, is becoming more diverse. In many fields, deep learning has contributed to the creation of added value and used on the bases of more data as the application become more divers. The process for obtaining a better performance model will require a longer time than before, and therefore it will be necessary to find an optimal model that shows the best performance more quickly. In the artificial neural network modeling a tuning process that changes various elements of the neural network model is used to improve the model performance. Except Gride Search and Manual Search, which are widely used as tuning methods, most methodologies have been developed focusing on heuristic algorithms. The heuristic algorithm can get the results in a short time, but the results are likely to be the local optimal solution. Obtaining a global optimal solution eliminates the possibility of a local optimal solution. Although the Brute Force Method is commonly used to find the global optimal solution, it is not applicable because of an infinite number of hyper parameter combinations. In this paper, we use a statistical technique to reduce the number of possible cases, so that we can find the global optimal solution.

• Journal of Advanced Marine Engineering and Technology
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• 제38권10호
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• pp.1297-1302
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• 2014

This paper presents modern optimization methods for determining the optimal parameters of proportional-integral-derivative (PID) controller for coupled tank systems. The main objective is to obtain a fast and stable control system for coupled tank systems by tuning of the PID controller using the Particle Swarm Optimization algorithm. The result is compared in terms of system transient characteristics in time domain. The obtained results using the Particle Swarm Optimization algorithm are also compared to conventional PID tuning method like the Ziegler-Nichols tuning method, the Cohen-Coon method and IMC (Internal Model Control). The simulation results have been simulated by MATLAB and show that tuning the PID controller using the Particle Swarm Optimization (PSO) algorithm provides a fast and stable control system with low overshoot, fast rise time and settling time.

• 대한전기학회논문지:전력기술부문A
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• 제51권2호
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• pp.69-75
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• 2002

AVR parameter tuning for voltage control of power system generators has generally been performed with the analytic methods and the simulation methods, which mostly depend on off-line linear mathematical models of excitation control system. However, due to the nonlinear nature of excitation control system, excitation control system performance of the tuned Parameters using the above conventional tuning methods may not be appropriate for some operating conditions. This paper presents an AVR parameter tuning method using actual on-line data of the excitation control system with the parameter optimization technique. As this method utilizes on-line operating data of the target excitation control system not the mathematical model of the system, it can overcome the limitation of model uncertainty Problems in conventional method, and it can tune the AVR parameter set which gives desired performance at the operating conditions. For the verification of proposed tuning method, two case studies with scaled excitation systems and the real-time power system simulator are presented.