• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2711-2715
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• 2012

Background: Bisphenol A (BPA), an endocrine disrupting chemical, has been suspected to pose carcinogenic risks. However, likely mechanisms are obscure and there are difficulties to estimating its real significance for cancer development. Methods: We therefore studied BPA-induced proteomic alterations in immune organs of ICR mice offspring that were prenatally exposed to BPA (15 and 300 mg/L of drinking water). We performed 2D-gel analyses of samples, considering differences in spleen, exposure levels, sex, and ages. Results: From proteomic analyses, we found various proteins were up- or down-regulated by BPA. Among them, SET, a putative oncogene and inhibitor of phosphatase 2A, was significantly down-regulated in a BPA dose-dependent manner. We also confirmed down-regulation of SET in western blot and real time PCR analyses. From gene network analysis, SET is predicted to communicate with other genes including CYP17, which is involved in biosynthesis and metabolism of sex-hormones. Conclusions: This study provided evidence that SET can be applied as a new biomarker for prenatal BPA exposure and suggests a potential new mechanism of action in that BPA may disrupt CYP17 via SET.

• International Journal of Quality Innovation
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• v.5 no.1
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• pp.52-67
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• 2004

Successful implementation of statistical process control techniques requires for operational definitions and precise measurements. Nevertheless, very often analysts can dispose of process data available only by linguistic terms, that would be a waste to neglect just because of their intrinsic vagueness. Thus a hybrid approach, which integrates fuzzy set theory and common statistical tools, sounds useful in order to improve effectiveness of statistical process control in such a case. In this work, a fuzzy approach is adopted to manage linguistic information, and the use of a Chi-squared control chart is proposed to monitor process performance.

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.46-51
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• 2007

The near-optimal control algorithm for central cooling system has been developed for minimizing energy consumption while maintaining the comfort of indoor thermal environment in terms of the environmental variables such as time varying indoor cooling load and outdoor temperatures. The optimal set-points of control parameters with near-optimal control are supply air temperature and chilled water temperature. This study has been done by using LapVIEW program with PID control in order to analyze the central cooling system energy saving.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.163-168
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• 2002

The voting algorithm for action selection performs self-improvement by Reinforcement learning algorithm in the dynamic environment. The proposed voting algorithm improves the navigation of the robot by adapting the eligibility of the behaviors and determining the Command Set Generator (CGS). The Navigator that using a proposed voting algorithm corresponds to the CGS for giving the weight values and taking the reward values. It is necessary to decide which Command Set control the mobile robot at given time and to select among the candidate actions. The Command Set was learnt online by means as Q-learning. Action Selector compares Q-values of Navigator with Heterogeneous behaviors. Finally, real-world experimentation was carried out. Results show the good performance for the selection on command set as well as the convergence of Q-value.

• Journal of Power Electronics
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• v.17 no.3
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• pp.610-620
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• 2017

The Finite Control Set-Model Predictive Controller (FCS-MPC) for quasi Z-Source Inverters (qZSIs) is designed to reduce the number of sensors by proposing a current observer for the inductor current. Unlike the traditional FCS-MPC algorithm, the proposed model removes the inductor current sensor and observes the inductor current value based on the deposited prior optimized state as well as the capacitor voltage during this state. The proposed observer has been validated versus a typical MPC. Then, a comparative study between the proposed Modified Finite Control Set-Model Predictive Controller (MFCS-MPC) and a linear PID controller is provided under the same operating conditions. This study demonstrates that the dynamic response of the control objectives by MFCS-MPC is faster than that of the PID. On the other hand, the PID controller has a lower Total Harmonic Distortion (THD) when compared to the MFCS-MPC at the same average switching. Experimental results validate both methods using a DSP F28335.

• Journal of Power Electronics
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• v.19 no.4
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• pp.989-999
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• 2019

This paper proposes a fault-tolerant control strategy with finite control set model predictive control (FCS-MPC) based on hierarchical optimization for five-level H-bridge neutral-point-clamped (5L-HNPC) inverter-fed induction motor drives. Fault-tolerant operation is analyzed, and the fault-tolerant control algorithm is improved. Adopting FCS-MPC based on hierarchical optimization, where the voltage is used as the controlled objective, called model predictive voltage control (MPVC), the postfault controller is simplified as a two layer control. The first layer is the voltage jump limit, and the second layer is the voltage following control, which adopts the optimal control strategy to ensure the current following performance and uniqueness of the optimal solution. Finally, simulation and experimental results verify that 5L-HNPC inverter-fed induction motor drives have strong fault tolerant capability and that the FCS-MPVC based on hierarchical optimization is feasible.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.453-462
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• 2016

This paper proposes a torque control method for surface mounted permanent magnetic synchronous motor (PMSM) driven by a 2-level voltage source driven inverter, which has fast torque response and small torque ripple. The proposed torque control method follows the finite control set model predictive control (FCS-MPC) strategy. A reference state is derived at each time step for the given time varying torque reference and the cost index is defined so that the tracking error for this reference state should be penalized. The choice of an optimal output voltage vector is made first from the 6 possible active voltage vectors of the 2-level voltage source inverter. Then a modulation factor for the chosen optimal voltage vector is obtained so that the torque ripple can be reduced further. It is shown that the proposed FCS-MPC control method yields fast torque tracking response and small torque ripple through simulation and experiments.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.2
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• pp.99-104
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• 2019

Solar power generation systems require maximum power point tracking (MPPT) control to acquire maximum power using inefficient and high-cost PV modules. Most conventional MPPT algorithms are based on the slope-tracking concept. The perturb and observe (P&O) algorithm is a typical slope-tracking method. The two factors that determine the MPPT performance of P&O algorithm are the MPPT control period and the magnitude of the perturbation voltage. The MPPT controller quickly moves to the new maximum power point at insolation change when the perturbation voltage is set to large, and the error of output power will be huge in the steady state even when insolation is not changing. The dynamics of the MPPT controller can be accelerated even though the perturbation voltage is set to small when the MPPT control period is set to short. However, too short MPPT control period does not improve MPPT performance but consumes the MPPT controller resources. Therefore, analyzing the performance of the MPPT controller is necessary for actual insolation conditions in real weather environment to determine the optimum MPPT control period and the magnitude of the perturbation voltage. This study proposes an optimum MPPT control period that maximizes MPPT efficiency by measuring and analyzing actual insolation profiles in typical clear and cloudy weather in central Korea.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.324-328
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• 2007

An analytical framework for fuzzy modeling and control of nonlinear systems using a set of linear models is presented. Fuzzy clustering is applied on the aerodynamic coefficients of a missile to obtain an optimal number of rules in a Tagaki-Sugeno fuzzy rule-set. Next, the obtained membership functions and rule-sets are applied to a set of linear optimal controllers towards extraction of a global controller. Reported simulations demonstrate the performance, stability, and robustness of the controller.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.662-665
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• 2001

As the fuzzy control is applied to servo system the hardware implementation of the fuzzy information systems requires the high speed operations, short real time control and the small size systems. The aims of this study is to develop hardware of the fuzzy information systems to be apply to servo system. In this paper, we propose a calculation method of approximate reasoning for fuzzy control based on $\alpha$-level set decomposition of fuzzy sets by quantize $\alpha$-cuts. This method can be easily implemented with analog hardware. The influence of quantization levels of $\alpha$-cuts on output from fuzzy inference engine is investigated. It is concluded that 4 quantization levels give sufficient result for fuzzy control performance of do servo system. It examined useful with experiment for dc servo system.