• International Journal of Computer Science & Network Security
• /
• v.24 no.2
• /
• pp.150-154
• /
• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• Proceedings of the KIEE Conference
• /
• 2001.07e
• /
• pp.141-145
• /
• 2001

In this paper, model reference PID genetic controller was proposed in order to overcome the difficulty of reflecting control performance required in the overall control system and defects of the adaptation performance in the PID genetic controller. The proposed controller comprised Inner feedback loop consisting of the PID controller and plant, and outer loop consisting of an genetic algorithm which was designed for tuning a parameter of the controller. A reference model was used for design criteria of a PID controller which characterizes and quantizes the control performance required in the overall control system. Tuning parameter of the controller is performed by the genetic algorithm. The performance of proposed algorithm was verified through experiment for the DC servo motor.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2011.10a
• /
• pp.191-195
• /
• 2011

The airframe ground vibration tests were conducted on the KC-100 aircraft according to the regulation requirement, KAS 23.629(a)(2) and the modal characteristics for the target modes were measured. To make FE model tuning, a design sensitivity approach with engineering judgment was implemented using MSC/Nastran and Attune, a genetic algorithm based parameter optimization software. Based on the comparison between initial prediction and test results, design variables such as beam cross-sectional properties and spring stiffnesses were devised. As the results, the correlation of the FE model to the GVT results was made appropriately, meeting the goal of matching the target frequencies within 5%.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.13-14
• /
• 2008

This paper describes the optimal PSS parameter design for the PSS of EX2000 excitation system. The suggested tuning technique uses the model-based PSS tuning method which have three steps: generation system modeling, determination of PSS parameters, and on-site test. Using this method, the PSS parameters of EX2000 system in Dangjin T/P #4 was designed and verified by linear analysis program, PSS/E, and EMTDC/PSCAD.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.114-114
• /
• 2000

In this paper a self-tuning modified skyhook control for the semi-active suspension systems is investigated. The damping force generation mechanism is modeled We consider a 2 DOF time-varying quarter car model that permits parameter variations of the sprung mass and suspension spring coefficient. The modified skyhook control algorithm proposed in this paper requires only the measurement of body acceleration. The absolute velocity of the sprung mass and the relative velocity of the suspension deflection are estimated by using integral filters, according to parameter variations. The skyhook gains are designed in such a way that the body acceleration and the dynamic tire force are optimized. An ECU prototype will be discussed

• Journal of Electrical Engineering and Technology
• /
• v.3 no.1
• /
• pp.101-107
• /
• 2008

We introduce an optimization of fuzzy set-fuzzy systems based on IG (Information Granules). The proposed fuzzy model implements system structure and parameter identification by means of IG and GAs. The concept of information granulation was coped with to enhance the abilities of structural optimization of the fuzzy model. Granulation of information realized with C-Means clustering helps determine the initial parameters of the fuzzy model such as the initial apexes of the membership functions in the premise part and the initial values of polynomial functions in the consequence part of the fuzzy rules. The initial parameters are adjusted effectively with the help of the GAs and the standard least square method. To optimally identify the structure and the parameters of the fuzzy model we exploit GAs with successive tuning method to simultaneously search the structure and the parameters within one individual. We also consider the variant generation-based evolution to adjust the rate of identification of the structure and the parameters in successive tuning method. The proposed model is evaluated with the performance of the conventional fuzzy model.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.20 no.6
• /
• pp.498-508
• /
• 2015

In the simulation of photovoltaic (PV) power conditioning systems, PSIM is a widely accepted circuit simulation platform because of its fast speed and C-code support. PSIM provides two kinds of generic PV panel models: functional model and physical model. Whereas the functional model simulates PV in the standard test condition (STC) only, the physical model can emulate changing PV characteristics under varying temperatures and irradiation conditions and is thus more suitable for system simulation. However, the physical model requires complicated parameters from users, and thus it is prone to errors and is difficult to use. In this study, a new PSIM model for PV is presented to solve these problems. The proposed model utilizes manufacturers' datasheet values specified under STC only and excludes user-defined information from input parameters. To achieve good accuracy even in varying environmental conditions, single-diode model parameters are successively tuned to a time-varying virtual datasheet. Comparison with a conventional physical model shows that the proposed model provides more accurate simulation according to error analysis based on the EN50530 standard.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.701-706
• /
• 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.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.1 s.173
• /
• pp.7-15
• /
• 2000

This paper suggests an auto-tuning I'll) control algorithm that uses the advantage of PID controller and improves the system performance. The PID gains being designed by th- conventional method are tuned through the plant parameter estimation. The Extended Kalman Filter is used for the estimation. It works as an observer and noise filter. Moreover, as the plant state and the uncertain parameter could be estimated simultaneously, the proposed algorithm is very useful in the tracking control of a system with uncertain parameter. The auto-tuning I'll) controller could maintain the system performance in the case that the plant parameters are uncertain or varying. The proposed control algorithm requires a correct estimation of the plant parameter. The controller stability and the performance is considered through the stability criteria and a servo motor model. The Kalman filter estimates the most sensitive plant parameter, which is determined by the sensitivity analysis.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.206-211
• /
• 1993

This paper presents a new identification method which utilizes fuzzy inference in parameter identification. The proposed system has an additional control loop where a real plant is replaced by a plant model. The control system to be designed is to satisfy the following specifications: 1) It has zero steady-state error. 2) It has adequate damping characteristics. 3) 1),2) satisfied, it has a shortest rise-time. Fuzzy rules describe the relationship between comparison results of the features and magnitude of modification in the model parameter values. This method is effective in auto-tuning because the response of the closed loop is verified. The proposed method is tested in simulation for several plants with high-order lags and dead-times.