• The Journal of Korea Robotics Society
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• v.5 no.1
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• pp.70-76
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• 2010

This paper addresses a design issue of "model complexity and performance trade-off" in the application of bandwidth extension (BWE) methods to the intra-frame predictivevector quantization problem of wideband speech. It discusses model-based linear and non-linear prediction methods and presents a comparative study of them in terms of prediction gain. Through experimentation, the general trend of saturation in performance (with the increase in model complexity) is observed. However, specifically, it is also observed that there is no significant difference between HMM and GMM-based BWE functions.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.27.4-27
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• 2001

A stabilizing control method for linear systems with model uncertainties and hard input constraints is developed, which does not require on-line optimizations. This work is motivated by the constrained robust MPC(CRMPC) approach [3] which adopts the dual mode prediction strategy (i.e. free control moves and invariant set) and minimizes a worst case performance criterion. Based on the observation that, a feasible control sequence for a particular state can be found as a linear combination of feasible sequences for other states, we suggest a stabilizing control algorithm providing sub-optimal and feasible control sequences using pre-computed optimal sequences for some canonical states. The on-line computation of the proposed method reduces to simple matrix multiplication.

• 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.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.19 no.3
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• pp.51-56
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• 2000

Is this paper, we studied on the automatic speech control system in real-time windows environment using voice recognition. The applied reference pattern is the variable DMS model which is proposed to fasten execution speed and the one-stage DP algorithm using this model is used for recognition algorithm. The recognition vocabulary set is composed of control command words which are frequently used in windows environment. In this paper, an automatic speech period detection algorithm which is for on-line voice processing in windows environment is implemented. The variable DMS model which applies variable number of section in consideration of duration of the input signal is proposed. Sometimes, unnecessary recognition target word are generated. therefore model is reconstructed in on-line to handle this efficiently. The Perceptual Linear Predictive analysis method which generate feature vector from extracted feature of voice is applied. According to the experiment result, but recognition speech is fastened in the proposed model because of small loud of calculation. The multi-speaker-independent recognition rate and the multi-speaker-dependent recognition rate is 99.08% and 99.39% respectively. In the noisy environment the recognition rate is 96.25%.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• Ocean Systems Engineering
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• v.14 no.1
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• pp.17-52
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• 2024

In this paper, the authors explore the modeling and control of a point absorber wave energy converter, which is connected to the electric grid via a power converter that is based on a linear permanent magnet synchronous generator (LPMSG). The device utilizes a buoyant mechanism to convert the energy of ocean waves into electrical power, and the LPMSG-based power converter is utilized to change the variable frequency and voltage output from the wave energy converter to a fixed frequency and voltage suitable for the electric grid. The article concentrates on the creation of a predictive control system that regulates the speed, voltage, and current of the LPMSG, and the modeling of the system to simulate its behavior and optimize its design. The predictive model control is created to guarantee maximum energy output and stable grid connection, using Matlab Simulink to validate the proposed strategy, including control side generator and predictive current grid-side converter loops.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.669-681
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• 2006

In this work, a new method of design adaptive controllers for SISO systems based on multiple models and switching is presented. The controller selects the model from a given set, according to a switching rule based on output prediction errors. The goal is to design, at each sample instant, a predictive control law that ensures the robust stability of the closed-loop system and achieves the best performance for the current operating point. At each sample the proposed control scheme identifies a set of linear models that best characterizes the dynamics of the current operating region. Then, it carries out an automatic reconfiguration of the controller to achieve the best possible performance whilst providing a guarantee of robust closed-loop stability. The results are illustrated by simulations a nonlinear continuous and stirred tank reactor.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.60-66
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• 1996

Model Based Predictive Control(MBPC) has been widely used in predictive control since 80's. GPC[1] which is the superset of many MBPC strategies a popular method, but GPC has some weakness, such as insufficient stability analysis, non-applicability to internally unstable systems. However, CRHPC[2] proposed in 1991 overcomes the above limitations. So we chose RHPC based on CRHPC for electric furnace control. An electric furnace which has nonlinear properties and large time delay is difficult to control by linear controller because it needs nearly perfect modelling and optimal gain in case of PID. As a result, those controls are very time-consuming. In this paper, we applied RHPC with equality constraint to electric furnace. The reults of experiments also include the case of RHPC with monotonic weighting improving the transient response and including unmodelled dynamics. So, This paper proved the practical aspect of RHPC for real processes.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.289-295
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• 2002

In this paper, a linear matrix inequality(LMI)-based robust control method, which combines model predictive control(MPC) with the feedback linearization(FL), is presented for constrained nonlinear systems with parameter uncertainty. The design procedures consist of the following 3 steps: Polytopic description of nonlinear system with a parameter uncertainty via FL, Mapping of actual input constraint by FL into constraint on new input of linearized system, Optimization of the constrained MPC problem based on LMI. To verify the performance and usefulness of the control method proposed in this paper, some simulations with application to a flexible single link manipulator are performed.