• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.698-707
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• 2017

This paper deals with predictive control methods of mobile robots for reference trajectory tracking control. Predictive control methods using predictive model are known as effective schemes that minimize the future errors between the reference trajectories and system states; however, the amount of real-time computation for the predictive control are huge so that their applications were limited to slow dynamic systems such as chemical processing plants. Lately with high computing power due to advanced computer technologies, the predictive control methods have been applied to fast systems such as mobile robots. These predictive controllers have some control parameters related to control performance. But these parameters have not been optimized. In this paper we employed the genetic algorithm to optimize the control parameters of the predictive controller for mobile robots. The improved performances of the proposed control method are demonstrated by the computer simulation studies.

• Advances in Computational Design
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• v.7 no.2
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• pp.113-128
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• 2022

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.3
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• pp.43-58
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• 2008

In this paper, we have presented a dynamic, predictive location update scheme that takes into account each user's mobility patterns. A user's past movement history is used to create two-dimensional transition probability matrix which makes use of two directional consecutive location areas. A mobile terminal utilizes the transition probability to develop a predictive path which consists of several predictive nodes and then the location update is saved as long as a mobile user follows the predictive path. Using continuous-time Markov chain, cost functions of location update and paging are derived and it is shown that the number of predictive nodes can be determined optimally. To evaluate the proposed scheme, simulations are designed and the numerical analysis is carried out. The numerical analysis features user's mobility patterns and regularity, call arrival rates, and cost ratio of location update to paging. Results show that the proposed scheme gives lower total location management cost, compared to the other location update schemes.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.4
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• pp.179-186
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• 2015

In this study, the actual energy consumption of the secondary side District Heating System (DHS) with different hot water supply temperature control methods is compared. The two methods are Outdoor Temperature Reset Control and Outdoor Temperature Predictive Control. While Outdoor Temperature Reset Control has been widely used for energy savings of the secondary side system, the results show that the Outdoor Temperature Predictive Control method saves more energy. In general, the Outdoor Temperature Predictive Control method lowers the supply temperature of hot water, and it reduces standby losses and increases the overall heat transfer value of heated spaces due to more flow into the space. During actual energy consumption monitoring, the Outdoor Temperature predictive Control method saves about 6.6% of energy when compared to the Outdoor Temperature Reset Control method. Also, it is found that at partial load condition, such as during daytime, the fluctuation of hot water supply temperature with Outdoor Temperature Reset Control is more severe than that with Outdoor Temperature Predictive Control. Thus, it proves that Outdoor Temperature Predictive Control is more stable even at partial load conditions.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.227-242
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• 2015

This paper proposes a weighting factor optimization method in predictive control algorithm for torque ripple reduction in an induction motor fed by an indirect matrix converter (IMC). In this paper, the torque ripple behavior is analyzed to validate the proposed weighting factor optimization method in the predictive control platform and shows the effectiveness of the system. Therefore, an optimization method is adopted here to calculate the optimum weighting factor corresponds to minimum torque ripple and is compared with the results of conventional weighting factor based predictive control algorithm. The predictive control algorithm selects the optimum switching state that minimizes a cost function based on optimized weighting factor to actuate the indirect matrix converter. The conventional and introduced weighting factor optimization method in predictive control algorithm are validated through simulations and experimental validation in DS1104 R&D controller platform and show the potential control, tracking of variables with their respective references and consequently reduces the torque ripple.

• Journal of Power Electronics
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• v.11 no.1
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• pp.64-73
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• 2011

The stability of PWM rectifiers with a deadbeat current controller is seriously influenced by computation time delays and low-pass filters inserted into the current-sampling circuit. Predictive current control is often adopted to solve this problem. However, grid current predictive precision is affected by many factors such as grid voltage estimated errors, plant model mismatches, dead time and so on. In addition, the predictive current error aggravates the grid current distortion. To improve the grid current predictive precision, an improved deadbeat current controller with a repetitive-control-based observer to predict the grid current is proposed in this paper. The design principle of the proposed observer is given and its stability is discussed. The predictive performance of the observer is also analyzed in the frequency domain. It is shown that the grid predictive error can be decreased with the proposed method in the related bode diagrams. Experimental results show that the proposed method can minimize the current predictive error, improve the current loop robustness and reduce the grid current THD of PWM rectifiers.

• Journal of Korean Critical Care Nursing
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• v.12 no.2
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• pp.26-38
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• 2019

Purpose : The aims of this study were to identify the incidence of pressure ulcers and to compare the predictive validities of pressure ulcer risk assessment scales among trauma patients. Methods : This was a prospective observational study. A total of 155 patients admitted to a trauma intensive care unit in a university hospital were enrolled. The predictive validity of the Braden, Cubbin & Jackson, and Waterlow scales were assessed based on the sensitivity, specificity, positive and negative predictive values, and area under the receiver operating characteristic curve (AUC). Results : Of the patients, 14 (9.0%) subsequently developed pressure ulcers. The sensitivity, specificity, positive predictive values, and negative predictive values were 78.6%, 75.9%, 24.4%, and 97.3%, respectively, for the Braden scale (cut-off point of 12); 85.7%, 68.8%, 21.4%, and 98.0%, respectively, for the Cubbin & Jackson scale (cut-off point of 26); and 71.4%, 87.2%, 35.7%, and 96.9%, respectively, for the Waterlow scale (cut-off point of 18). The AUCs were 0.88 (Waterlow), 0.86 (Braden), and 0.85 (Cubbin & Jackson). Conclusion : The findings indicate that the predictive validity values of the Waterlow, Braden, and Cubbin & Jackson scales were similarly high. However, further studies need to also consider clinical usefulness of the scales.

• Journal of the Korean Society of Physical Medicine
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• v.19 no.3
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• pp.47-54
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• 2024

PURPOSE: This study aimed to identify the potential clinically predictive indicators of the age-related loss of skeletal muscle mass (ALSMM) in middle-aged women. METHODS: The data from a cross-sectional study involving 2,066 community-dwelling female participants aged 40 to 49 years were analyzed. Complex sampling analyses were used to ensure a nationally representative analysis, incorporating the individual weights provided by KNHANES. This approach accounted for the stratified, clustered, and multistage probability sampling design of the survey. The participants were screened for ALSMM, and various potential predictive indicators were assessed, including age, height, weight, body mass index, waist circumference, skeletal muscle mass index, smoking and drinking status, systolic and diastolic blood pressure, fasting glucose levels, triglyceride levels, and cholesterol levels. RESULTS: Significant potential predictive indicators for ALSMM included height, weight, body mass index, waist circumference, skeletal muscle mass index, and fasting glucose (p < .05). The systolic blood pressure, diastolic blood pressure, triglyceride levels triglyceride, and drinking and smoking status were found to be non-significant variables (p > .05). CONCLUSION: The study identified the potential predictive indicators for ALSMM among community-dwelling middle-aged women. These findings enhance the current understanding of ALSMM and highlight the potential predictive indicators associated with its development in middle-aged women.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.235-247
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• 1993

The predictive density function of a potential future observation and its first four moments are obtained in this paper to study the effects of a non-normal prior of the unknown mean of a normal population. The derived predictive density function is modified to study changes in utility curves, used to choose the optimum treatment from a given set of treatments, at a given level of stimulus due to slight deviations from normality of the prior distribution. Numerical illustrations are provided to exhibit some effectsl.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.155.5-155
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• 2001

The robust predictive control law which use the bound estimation is proposed for uncertain robot manipulators. Since the control design of a real manipulator system may often be made on the basis of the imperfect knowledge about model, it´s an important tend to design a robust control law that will guarantee the desired performance of the manipulator under uncertain elements. In the preceeding work, the robust predictive control law was proposed. In this work, we propose a class of robust predictive control of manipulators with the bound estimate technique and fe stability based on Lyapunov function is presented.