• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.25 no.1
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• pp.18-23
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• 1989

An approach for robustness recovery of the observer-based control system is presented. The approach is developed by adding a loop with appropriate constant matrix to the observer-based closed-loop system. It will be shown that if there exists an added-loop matrix M satisfying F=MC for a feedback gain F and output matrix C, then the observer-based control systems have the same loop transfer functions as full-state feedback implementations, in other words, the former has the same relative stability and robustness as the latter.

• International Journal of Quality Innovation
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• v.6 no.1
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• pp.109-118
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• 2005

The parameter design is the most emphasized measure by researchers for a new products development. It is critical for makers to achieve simultaneously in both the time-to-market production and the quality enhancement. However, there are difficulties in practical application, such as (1) complexity and nonlinear relationships co-existed among the system's inputs, outputs and control parameters, (2) interactions occurred among parameters, (3) where the adjustment factors of Taguchi's two-phase optimization procedure cannot be sure to exist in practice, and (4) for some reasons, the data became lost or were never available. For these incomplete data, the Taguchi methods cannot treat them well. Neural networks have a learning capability of fault tolerance and model free characteristics. These characteristics support the neural networks as a competitive tool in processing multivariable input-output implementation. The successful fields include diagnostics, robotics, scheduling, decision-making, prediction, etc. This research is a case study of spherical annealing model. In the beginning, an original model is used to pre-fix a model of parameter design. Then neural networks are introduced to achieve another model. Study results showed both of them could perform the highest spherical level of quality.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5829-5834
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• 2012

Background: Breast cancer is the most common cancer in women worldwide. Although different metabolic factors have been implicated in breast cancer development, the relationship between hypertension and breast cancer has not been elucidated. Aim: To evaluate hypertension as a risk factor for breast cancer in Chilean women of low and middle socio-economic status. Methods: We conducted an age-matched (1:1) case-control study in 3 hospitals in Santiago, Chile. Breast cancer cases (n=170) were histopathologically confirmed. Controls had been classified as Breast Imaging Reporting and Data System I (negative) or II (benign findings) within 6 months of recruitment. Blood pressure was measured using a mercury sphygmomanometer and standardized procedures. We used 2 hypertension cut-off points: blood pressures of ${\geq}140/90$ mmHg and ${\geq}130/85$ mmHg. Fasting insulin and glucose levels were assessed, and anthropometric, sociodemographic, and behavioral information were collected. Odds ratios and 95% confidence intervals were estimated for the entire sample and restricted to postmenopausal women using multivariable conditional logistic regression models. Results: Hypertension (${\geq}140/90$ mmHg) was significantly higher in cases (37.1%) than controls (17.1%) for the entire sample and in postmenopausal pairs (44.0% compared to 23.8%). In crude and adjusted models, hypertensive women had a 4-fold increased risk of breast cancer (adjusted odds ratio: 4.2; 95% confidence interval: 1.8; 9.6) compared to non-hypertensive women in the entire sample. We found a similar association in the postmenopausal group (adjusted odds ratio: 2.8; 95% confidence interval: 1.1; 7.4). A significant effect was also observed when hypertension was defined as blood pressure of ${\geq}130/85$ mmHg. Conclusion: A significant association was found between hypertension and breast cancer over the entire sample and when restricted to postmenopausal women. Hypertension is highly prevalent in Latin America and may be a modifiable risk factor for breast cancer; therefore, a small association between hypertension and breast cancer may have broad implications.

• Korean Chemical Engineering Research
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• v.52 no.5
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• pp.592-602
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• 2014

In the Integrated Gasification Combined Cycle (IGCC), the stability of the gasifier has strong influences on the rest of the plant as it supplies the feed to the rest of the power generation system. In order to ensure a safe and stable operation of the entrained-flow gasifier and for protection of the gasifier wall from the high internal temperature, the solid slag layer thickness should be regulated tightly but its control is hampered by the lack of on-line measurement for it. In this study, a previously published dynamic simulation model of a Shell-type gasifier is reproduced and two different linear model predictive control strategies are simulated and compared for multivariable control of the entrained-flow gasifier. The first approach is to control a measured secondary variable as a surrogate to the unmeasured slag thickness. The control results of this approach depended strongly on the unmeasured disturbance type. In other words, the slag thickness could not be controlled tightly for a certain type of unmeasured disturbance. The second approach is to estimate the unmeasured slag thickness through the Kalman filter and to use the estimate to predict and control the slag thickness directly. Using the second approach, the slag thickness could be controlled well regardless of the type of unmeasured disturbances.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.87-92
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• 2007

Recently, developments of process monitoring system in order to detect and diagnose process abnormalities has got the spotlight in process systems engineering. Normal data obtained from processes provide available information of process characteristics to be used for modeling, monitoring, and control. Since modern chemical and environmental processes have high dimensionality, strong correlation, severe dynamics and nonlinearity, it is not easy to analyze a process through model-based approach. To overcome limitations of model-based approach, lots of system engineers and academic researchers have focused on statistical approach combined with multivariable analysis such as principal component analysis (PCA), partial least squares (PLS), and so on. Several multivariate analysis methods have been modified to apply it to a chemical process with specific characteristics such as dynamics, nonlinearity, and so on.This paper discusses about missing value estimation and sensor fault identification based on process variable reconstruction using dynamic PCA and canonical variate analysis.