• The korean journal of orthodontics
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• v.43 no.4
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• pp.193-200
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• 2013

Objective: To evaluate the psychosocial impact of malocclusion, determine its relationship with the severity of malocclusion, and assess the influence of gender and social class on this relationship in adolescents. Methods: A random sample of 627 Spanish adolescents aged 12 - 15 years underwent intraoral examinations by 3 calibrated examiners (intraexaminer and interexaminer kappa > 0.85) at their schools. Psychosocial impact was measured through a self-rated Psychosocial Impact of Dental Aesthetics Questionnaire (PIDAQ). The severity of malocclusion was measured by the Index of Orthodontic Treatment Need (IOTN). Gender and social class were also recorded. Results: The total PIDAQ score and those of its 4 subscales, social impact, psychological impact, aesthetic concern, and dental self-confidence, presented significant differences ($p{\leq}0.05$ by analysis of variance) and linear relationships with the IOTN grades ($p{\leq}0.05$ by linear regression). Stepwise linear regression models showed that the IOTN dental health component was a predictive variable of the total and subscale PIDAQ scores. Neither gender nor social class was an independent predictive variable of this relationship, except the linear model for psychological impact, where gender was a predictive variable. The occlusal conditions responsible for higher PIDAQ scores were increased overjet, impeded eruption, tooth displacement, and increased overbite. Conclusions: Malocclusion has a psychological impact in adolescents and this impact increases with the severity of malocclusion. Social class may not influence this association, but the psychological impact seems to be greater among girls.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.186-192
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• 2011

In this paper, we propose a new procedure for improvement of the linear predictive coding. To reduce the error power incurred by the coding, we interchanged the order of the two procedures of windowing on the signal and linear prediction. This scheme corresponds to LPC extraction with windowed autocorrelation. The proposed method requires more calculational time because it necessitates matrix inversion on more parameters than the conventional technique where an efficient Levinson-Durbin recursive procedure is applicable with smaller parameters. Experimental test over various speech phonemes showed, however, that our procedure yields about 5 % less power distortion compared to the conventional technique. Consequently, the proposed method in this paper is thought to be preferable to the conventional technique as far as the fidelity is concerned. In a separate study of speaker-dependent speech recognition test for 50 isolated words pronounced by 40 people, our approach yielded better performance too.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.33-39
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• 2019

In this paper, we propose a parametric model for analyzing the motion information obtained from the acceleration sensors to measure the activity of the human body. The motion of the upper body and the lower body does not occur at the same time, and the motion analysis method using a single motion sensor involves a lot of errors. In this study, the 3-axis accelerometer is attached to the arms and legs, the body's activity data are measured, the momentum of the arms and legs are calculated for each channel, and the linear predictive coefficient is obtained for each channel. The periodicity of the upper body and the lower body is determined by analyzing the correlation between the channels. The linear predictive coefficient and the periodic value are used as data to measure the type of exercise and the amount of exercise. In the proposed method, we measured four types of movements such as walking, stair climbing, slow hill climbing, and fast hill descending. In order to verify the usefulness of the parameters, the recognition results are presented using the linear predictive coefficient and the periodic value for each motion as the neural network input.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.15-22
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• 2004

Some recent advances in stability and optimality for the nonlinear receding horizon control (NRHC) or the nonlinear model predictive control (NMPC) are assessed. The NRHCs with terminal conditions are surveyed in terms of a terminal state equality constraint, a terminal cost, and a terminal constraint set. Other NRHCs without terminal conditions are surveyed in terms of a control Lyapunov function (CLF) and cost monotonicity. Additional approaches such as output feedback, fuzzy, and neural network are introduced. This paper excludes the results for linear receding horizon controls and concentrates only on the analytical results of NRHCs, not including applications of NRHCs. Stability and optimality are focused on rather than robustness.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.197-203
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• 1997

In this paper, we present a GP-PID(Generalized Predictive PID) controller which has the same structure as a generalized predictive control with steady-state weighting. The proposed controller can perform better than the conventional PID controller because it includes intrinsic delay-time compensator. The PID tuning parameters and delay-time compensator are calculated by equating the two degree of freedom PID to a linear form of GPC. The proposed controller is combined with a supervisor for safe start and self-tuning. GP-PID controller has been tested for various numerical models and an experimental stirred tank heater. As a result, it was observed that the proposed controller shows a satisfactory performance for variable delay as well as stochastic disturbance.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1133-1145
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• 2015

A new neural network (NN) predictive controller (NNPC) algorithm has been developed and tested in the computer simulation of active control of a nonlinear structure. In the present method an NN is used as a predictor. This NN has been trained to predict the future response of the structure to determine the control forces. These control forces are calculated by minimizing the difference between the predicted and desired responses via a numerical minimization algorithm. Since the NNPC is very time consuming and not suitable for real-time control, it is then used to train an NN controller. To consider the effectiveness of the controller on probability of damage, fragility curves are generated. The approach is validated by using simulated response of a 3 story nonlinear benchmark building excited by several historical earthquake records. The simulation results are then compared with a linear quadratic Gaussian (LQG) active controller. The results indicate that the proposed algorithm is completely effective in relative displacement reduction.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.37 no.1 s.109
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• pp.61-66
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• 2005

The grade change operations In paper mills exhibit inherent nonlinear dynamic characteristics. For this reason, the conventional model predictive control techniques based on linear process models are not adequate for the grade change operations. In this paper, a bilinear model for the nonlinear grade change processes was presented first and optimal input variables were calculated by using one-step-ahead predictive control method. Numerical simulations showed that the control performance lied within acceptable range and that the bilinear model predictive control scheme was highly promising control strategy for the grade change operations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1360-1365
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• 2011

Electro-hydraulic servo motor is used to a lot of in the field of industrial equipment which requires one of the control functions among pressure, flow, and power output. In this paper, linear discrete reference model of the electro-hydraulic servo motor system are made for 1-step ahead predictive control. The parameters of electro-hydraulic servo motor system are estimated using the recursive least square method. 1-step ahead predictive model output of electro-hydraulic servo motor system corresponded to reference model output in spite of estimated parameters are not meet real parameters. Control performance affections are studied due to the forgetting factors variation.

• Journal of Power Electronics
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• v.17 no.4
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• pp.983-990
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• 2017

This paper proposes a model predictive control based on the discrete Lyapunov function to improve the performance of power electronic converters. The proposed control technique, based on the finite control set model predictive control (FCS-MPC), defines a cost function for the control law which is determined under the Lyapunov stability theorem with a control error compensation. The steady state and dynamic performance of the proposed control strategy has been tested under a single phase AC/DC voltage source rectifier (S-VSR). Experimental results demonstrate that the proposed control strategy not only offers global stability and good robustness but also leads to a high quality sinusoidal current with a reasonably low total harmonic distortion (THD) and a fast dynamic response under linear loads.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.53-59
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• 2022

This paper describes an integrated control of torque vectoring and rear wheel steering using model predictive control. The control objective is to minimize the yaw rate and body side slip angle errors with chattering alleviation. The proposed model predictive controller is devised using a linear parameter-varying (LPV) vehicle model with real time estimation of the varying model parameters. The proposed controller has been investigated via computer simulations. In the simulation results, the performance of the proposed controller has been compared with uncontrolled cases. The simulation results show that the proposed algorithm can improve the lateral stability and handling performance.