• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.37-47
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• 1998

This paper proposes a controller design method using the evolutionary computation algorithm and the fuzzy logic to control the wafer temperature in rapid thermal processing. First, we design the feedforward static controller to provide the control powers of the lamps for the given steady state temperature. Second, the feedforward dynamic controller is designed for the additional control powers to achieve a given transient response. These feedforward controllers are implemented by using the fuzzy logic to act as a global nonlinear controller over a wide range of operating points. The parameters of these controllers are optimized by using the evolutionary computation algorithm so that it can be used when the mathematical model is not available. In addition, the feedback error controller is introduced to compensate the feedforward controllers when there exist disturbances and modeling errors. The gain of feedback error controller is also obtained by the evolutionary computation algorithm. Through simulations, we verify the proposed control system can give a satisfactory performance.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.963-968
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• 1990

An iterative learning control scheme for tracking control of a class of uncertain nonlinear systems is presented. By introducing a model reference adaptive controller in the learning control structure, it is possible to achieve zero tracking of unknown system even when the upperbound of uncertainty in system dynamics is not known apriori. The adaptive controller pull the state of the system to the state of reference model via control gain adaptation at each iteration, while the learning controller attracts the model state to the desired one by synthesizing a suitable control input along with iteration numbers. In the controller role transition from the adaptive to the learning controller takes place in gradually as learning proceeds. Another feature of this control scheme is that robustness to bounded input disturbances is guaranteed by the linear controller in the feedback loop of the learning control scheme. In addition, since the proposed controller does not require any knowledge of the dynamic parameters of the system, it is flexible under uncertain environments. With these facts, computational easiness makes the learning scheme more feasible. Computer simulation results for the dynamic control of a two-axis robot manipulator shows a good performance of the scheme in relatively high speed operation of trajectory tracking.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1739-1741
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• 2016

A compressive sensing based iterative scheme for reducing both the impulsive noise as well as the clipping noise is proposed for OFDM-based communication systems. Nonlinear blanking using adaptive thresholds is used in the 1st stage followed by two consecutive compressive sensing based detection with the aid of decision feedback for reducing the BER gradually. Our simulation results revealed an SNR gain of 4.5dB at the BER of $10^{-5}$.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.4
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• pp.443-450
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• 2016

The linearization technique for low noise amplifier (LNA) has been implemented in standard $0.18-{\mu}m$ BiCMOS process. The MOS-BJT derivative superposition (MBDS) technique exploits a parallel LC tank in the emitter of bipolar transistor to reduce the second-order non-linear coefficient ($g_{m2}$) which limits the enhancement of linearity performance. Two feedback capacitances are used in parallel with the base-collector and gate-drain capacitances to adjust the phase of third-order non-linear coefficients of bipolar and MOS transistors to improve the linearity characteristics. The MBDS technique is also employed cascode configuration to further reduce the second-order nonlinear coefficient. The proposed LNA exhibits gain of 9.3 dB and noise figure (NF) of 2.3 dB at 2 GHz. The excellent IIP3 of 20 dBm and low-power power consumption of 5.14 mW at the power supply of 1 V are achieved. The input return loss ($S_{11}$) and output return loss ($S_{22}$) are kept below - 10 dB and -15 dB, respectively. The reverse isolation ($S_{12}$) is better than -50 dB.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.5
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• pp.563-569
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• 2015

A chemical reaction occurring in CSTR (Continuous Stirred Tank Reactor) is significantly affected by the concentration, temperature, pressure, and reacting time of materials, and thus it has strong nonlinear and time-varying characteristics. Also, when an existing linear PID controller with fixed gain is used, the performance could deteriorate or could be unstable if the system parameters change due to the change in the operating point of CSTR. In this study, a technique for the design of a fuzzy PD plus I controller was proposed for the temperature control of a CSTR process. In the fuzzy PD plus I controller, a linear integral controller was added to a fuzzy PD controller in parallel, and the steady-state performance could be improved based on this. For the fuzzy membership function, a Gaussian type was used; for the fuzzy inference, the Max-Min method of Mamdani was used; and for the defuzzification, the center of gravity method was used. In addition, the saturation state of the actuator was also considered during controller design. The validity of the proposed method was examined by comparing the set-point tracking performance and the robustness to the parameter change with those of an adaptive controller and a nonlinear proportional-integral-differential controller.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.7
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• pp.1044-1052
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• 1999

A 2-stage 1 watt MMIC(Monolithic Microwave Integrated Circuits) HPA(High Power Amplifiers) at 20 GHz band has been designed and fabricated. The $0.15\mu\textrm{m}$ with the width of $400\mu\textrm{m}$for single device pHEMT technology was used for the fabrication of this MMIC HPA. Due to the series feedback technique from source to ground, bias circuits and stabilization circuits on the main microstrip line, the stability factors(Ks) are more than one at full frequency. The independent operation for each stage and excellent S11, S22 less than -20 dB have been obtained by using lange couplers. For beginning the easy design, linear S-parameters have been extracted from the nonlinear equivalent circuit in foundry library, and equivalent circuits of devices at in/output ports were calculated from this S-parameters. The measured performances, which are in well agreement with the predicted ones, showed the MMIC HPA in this paper has the minimum 15 dB of linear gain, -20 dB of reflection coefficients and 31 dBm of output power over 17~25 GHz.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.39-47
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• 1998

A fourth-order $\Sigma$-$\Delta$ modulator is designed and implemented in 0.6 $\mu\textrm{m}$ CMOS technology. The modulator is verified by introducing nonlinear factors such as DC gain and slew rate in system model that determines the transfer function in S-domain and in time-domain. Dynamic range is more than 110 dB and the peak SM is 102.6 dB at a clock rate of 2.8224 MHz for voiceband signal. The structure of a ∑-$\Delta$ modulator is a modified fourth-order ∑-$\Delta$ modulator using direct feedback loop method, which improves performance and consumes less power. The transmission zero for noise is located in the first-second integrator loop, which reduces entire size of capacitors, reduces the active area of the chip, improves the performance, and reduces power dissipation. The system is stable because the output variation with respect to unit time is small compared with that of the third integrator. It is easy to implement because the size of the capacitor in the first integrator, and the size of the third integrator is small because we use the noise reduction technique. This paper represents a new design method by modeling that conceptually decides transfer function in S-domain and in Z-domain, determines the cutoff frequency of signal, maximizes signal power in each integrator, and decides optimal transmission-zero frequency for noise. The active area of the prototype chip is 5.25$\textrm{mm}^2$, and it dissipates 10 mW of power from a 5V supply.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.