• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.11-16
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• 2014

In this paper, the two-wheels robot using a predictive controller to maintain the balance of the posture control in real time have been examined. A reaction wheel pendulum control method is adopted to maintain the balance while the bicycle robot is driving. The objective of this research was to design and implement a self-balancing algorithm using the dsPIC30F4013 embedded processor. To calculate the attitude in ARS using 2 axis gyro(roll, pitch) and 3 axis accelerometers (x, y, z). In this study, the disturbance of the posture for the asymmetrical propose to overcome the predictive controller which was a problem in the control of a remote system by introducing the two wheels of the robot controller and the linear prediction of the system controller combines the simulation was performed. Also, the robust characteristic for realizing the goal of designing a loop filter too robust controller is designed so that satisfactory stability of the control system to improve stability of the system to minimize degradation of performance was confirmed.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.46-56
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• 1999

A predictive controller is designed based upon stochastic methods for compensation time-delay effect on a system which has inherent time-delay caused by the spatial separation between controllers and actuators. The predictive controller estimates current outputs through linear prediction methods and probability functions utilizing previous outputs, and minimizes the malicious phenomena caused by the time-delay in precision control systems. To demonstrate effectiveness of this control methodology, we applied this algorithm for the control of a tele-operated DC servomotor. The experimental results show that this predictive controller is superior to the PID controller in terms of convergence-characteristics, and show that this controller expands the maximum allowable time-delay for a system maintaining the stability. Since the proposed predictor does not require models of plants - it requires only stochastic information for outputs --, it is a general scheme which can be applied for the control of systems which are difficult to model or the compensator of PID control.

• Journal of Community Nutrition
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• v.6 no.3
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• pp.121-124
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• 2004

Objective : The paper aims to provide predictive coefficients via BIA for the assessment of body composition in children and adolescents to serve clinical as well as research purposes. Methods : Body composition via dual-energy x­ray absorptiometry (DXA) and bioelectric impedance as well as other anthropometric index were derived from meaurements on 1026 children and adolescents aged from 6 to 18 years from Beijing City. The best subset regression and principle component analysis were adopted to build the predictive coefficients with the logarithm of body composition via DXA as response variable. Results : Condition index ${\varphi}$ of fat-free mass multiple linear regression achieves 113.49 and 91.18 for males and females respectively, demonstrating severe multicollinearity among anthropometric indexes in children and adolescents. BIA predictive coefficients base on the best subset regression and principle component analysis boast a content predictive value for lean mass ($r^2$ = 0.9697 and 0.9664 for boys and girls respectively, p < 0.0001) and for Fat$\%$ ($r^2$ = 0.7705 and 0.6959 for boys and girls respectively, p < 0.0001). Conclusions : BIA method is applicable for the prediction of body composition for children and adolescents.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.457-466
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• 2005

In this paper, we propose a wavelet neural network(WNN) based predictive control method for path tracking of mobile robots with multi-input and multi-output. In our control method, we use a WNN as a state predictor which combines the capability of artificial neural networks in learning processes and the capability of wavelet decomposition. A WNN predictor is tuned to minimize errors between the WNN outputs and the states of mobile robot using the gradient descent rule. And control signals, linear velocity and angular velocity, are calculated to minimize the predefined cost function using errors between the reference states and the predicted states. Through a computer simulation for the tracking performance according to varied track, we demonstrate the efficiency and the feasibility of our predictive control system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.11
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• pp.1628-1633
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• 2017

The purpose of this study was to identify the difference and correlation in elbow joint maximal flexion strength according to measurement methods and characteristics of muscular contraction, and to develop the predictive equation of elbow joint maximal flexion strength for the optimal exercise intensity setting and accurate measurement. Subjects were 30 male university students. Elbow joint maximal flexion strength of isokinetic contraction, isometric contraction at $75^{\circ}$ elbow joint flexion position, isotonic concentric 1RM, manual muscle strength (MMT) were measured with isokinetic dynamometer, dumbbell, and manual muscle tester. Pearson's r, linear regression equation, and multiple regression equation between variables were calculated. As a result, the highest value was isometric contraction. The second highest value was MMT. The third highest value was isokinetic contraction. 1RM was the lowest. Predictive equations of elbow joint maximal flexion strength between isometric and isokinetic contraction, between isometric contraction and 1RM, among isometric contraction, 1RM, and body weight were developed. In conclusion, 1RM and isokinetic elbow joint maximal flexion strength could be seemed to underestimate the practical elbow joint maximal flexion strength. And it is suggested that the developed predictive equations in this study should be useful in criteria- and goal-setting for resistant exercise and sports rehabilitation after elbow joint injury.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.181-200
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• 2020

This study used the OLS model to estimate the determinants affecting the tenure of a home and then compared the predictive power of each model with SVM, Decision Tree, Random Forest, Gradient Boosting, XGBooest and LightGBM. There is a difference from the preceding study in that the Stacking model, one of the ensemble models, can be used as a base model to establish a more predictable model to identify the volume of housing transactions in the housing market. OLS analysis showed that sales profits, housing prices, the number of household members, and the type of residential housing (detached housing, apartments) affected the period of housing ownership, and compared the predictability of the machine learning model with RMSE, the results showed that the machine learning model had higher predictability. Afterwards, the predictive power was compared by applying each machine learning after rebuilding the data with the influencing variables, and the analysis showed the best predictive power of Random Forest. In addition, the most predictable Random Forest, Decision Tree, Gradient Boosting, and XGBooost models were applied as individual models, and the Stacking model was constructed using Linear, Ridge, and Lasso models as meta models. As a result of the analysis, the RMSE value in the Ridge model was the lowest at 0.5181, thus building the highest predictive model.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.

• 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%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.307-310
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• 2003

In the maritime container terminal, LMTT(Linear Motor-based Transfer Technology) is horizontal transfer system for the yard automation, which has been proposed to take the place of AGV(Automated Guided Vehicle). The system is based on PMLSM (Permanent Magnetic Linear Synchronous Motor) that is consists of stator modules on the rail and shuttle car (mover). Because of large variant of mover's weight by loading and unloading containers, the difference of each characteristic of stator modules, and a stator module's trouble etc., LMCPS (Linear Motor Conveyance Positioning System) is considered as that the system is changed its model suddenly and variously. In this paper, we will introduce the soft-computing method of a multi-step prediction control for LMCPS using DR-FNN (Dynamically-constructed Recurrent Fuzzy Neural Network). The proposed control system is used two networks for multi-step prediction. Consequently, the system has an ability to adapt for external disturbance, cogging force, force ripple, and sudden changes of itself.

• International Journal of Highway Engineering
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• v.16 no.5
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• pp.173-182
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• 2014

PURPOSES : The purpose of the study is to a) explore the operating speed of trucks on rural highways affected by road geometry, and thereby b) develop a predictive model for the operating speed of trucks on rural highways. METHODS : Considering that most of the existing studies have focused on cars, the current study aimed to predict the operating speed of trucks by conducting linear regression analysis on the speed data of trucks operating on the linear-curved-linear portions of the road as a single set. RESULTS : The operating speed in the plane curve portion increased with the length of the curve, and decreased with a lower vertical grade and a smaller curve radius. In the straight plane portion, the operating speed increased with a larger curve radius(upstream), and decreased with an increase in the change of the vertical grade, depending on the length of the vertical curve. CONCLUSIONS : This study developed estimation models of truck for operational speed and evaluated the degree of safety for horizontal and vertical alignments simultaneous. In order to represent whole area of the rural highway, the models should be ew-analyzed with vast data related with road alignment factor in the near future.