• Journal of Power Electronics
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• v.19 no.4
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• pp.835-845
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• 2019

This paper presents a robust-optimal control strategy to improve the output-voltage error-tracking and control capability of a DC-DC boost converter. The proposed strategy employs an optimized Fractional-order Proportional-Integral (FoPI) controller that serves to eliminate oscillations, overshoots, undershoots and steady-state fluctuations. In order to significantly improve the error convergence-rate during a transient response, the FoPI controller is augmented with a pre-stage nonlinear error-modulator. The modulator combines the variations in the error and error-derivative via the signed-distance method. Then it feeds the aggregated-signal to a smooth sigmoidal control surface constituting an optimized hyperbolic secant function. The error-derivative is evaluated by measuring the output-capacitor current in order to compensate the hysteresis effect rendered by the parasitic impedances. The resulting modulated-signal is fed to the FoPI controller. The fixed controller parameters are meta-heuristically selected via a Particle-Swarm-Optimization (PSO) algorithm. The proposed control scheme exhibits rapid transits with improved damping in its response which aids in efficiently rejecting external disturbances such as load-transients and input-fluctuations. The superior robustness and time-optimality of the proposed control strategy is validated via experimental results.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.126-135
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• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Korean Journal of Optics and Photonics
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• v.8 no.6
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• pp.492-499
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• 1997

We proposed and studied an all-optical switching device made of a bent nonlinear waveguide and an all-optical logic gate made of a bent nonlinear Y-junction. The proposed devices as switch and a logic function are based on the evolution of nonlinear guided wave along a bent nonlinear waveguide. Since the characteristics of beam propagation depens on the nonlinearity, input power and bent angle of waveguide, the characteristics of output power transmission is calculated by variation the such parameters. Furthermore, by calculating the output power through the nonlinear media with different positions of detector in nonlinear media, we could find the ideal digital switching performance at specific position of detector and implement several all-optical logic functions (AND, OR, XOR) by power contrast between waveguide end and nonlinear media.

• Smart Structures and Systems
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• v.31 no.3
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• pp.229-245
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• 2023

The absence of excitation measurements may pose a big challenge in the application of structural damage identification owing to the fact that substantial effort is needed to reconstruct or identify unknown input force. To address this issue, in this paper, an iterative strategy, a synergy of Tikhonov regularization method for force identification and modified Jaya algorithm (M-Jaya) for stiffness parameter identification, is developed for damage identification with partial output-only responses. On the one hand, the probabilistic clustering learning technique and nonlinear updating equation are introduced to improve the performance of standard Jaya algorithm. On the other hand, to deal with the difficulty of selection the appropriate regularization parameters in traditional Tikhonov regularization, an improved L-curve method based on B-spline interpolation function is presented. The applicability and effectiveness of the iterative strategy for simultaneous identification of structural damages and unknown input excitation is validated by numerical simulation on a 21-bar truss structure subjected to ambient excitation under noise free and contaminated measurements cases, as well as a series of experimental tests on a five-floor steel frame structure excited by sinusoidal force. The results from these numerical and experimental studies demonstrate that the proposed identification strategy can accurately and effectively identify damage locations and extents without the requirement of force measurements. The proposed M-Jaya algorithm provides more satisfactory performance than genetic algorithm, Gaussian bare-bones artificial bee colony and Jaya algorithm.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.838-848
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• 2018

This paper proposes a single-phase buck-boost AC-AC converter. It consists of three legs with six switching units (each unit is composed of an active switch and a diode) and its input and output ports share a common ground. It can provide buck-boost voltage operation and immune from shoot-through problem. Since only two switching units are involved in the current paths, the conduction losses are low, which improves the system efficiency. The operation principle of the proposed circuit is firstly presented, and then, various operation conditions are introduced to achieve different output voltages with step-changed frequencies. Additionally, the parameters design and comparative analysis of the power losses are also given. Finally, experimental results verify the correctness of the proposed converter.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.48 no.8
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• pp.587-590
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• 1999

In this study, it is the purpose to develope a cheap and compact $CO_2$ laser and to apply 60 Hz AC discharges as a new exciting source. An axial and water cooledtype was adopted as the laser mode. The laser performance characteristics as various parameters, such as gas pressure and discharge current, have been investigated. And the laser output and the efficiency of DC and 60 Hz AC discharge-exciting type have been measured and compared for the different input powers at the static operational pressure. As a result, the case of 60 Hz AC discharge-exciting type, the laser oscillation began at the condition of operational pressure 6 Torr and discharge current 5 mA. A maximum laser output of about 32 W was obtained at an operational pressure of 18 Toorr and a discharge current of 30 mA. In addition, the laser output was saturated from an operational pressure of about 14 Torr and a discharge current of about 20 mA. In this $CO_2$ laser, the laser output of 60 Hz AC discharge-exciting type was slightly higher than that of DC discharge-exciting type. And the laser efficiency was about 10 to 13% for the various operational pressures and the discharge currents.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.2
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• pp.60-67
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• 2002

This paper presents a control approach for designing a self tuning fuzzy controller for a synchronous generator excitation and SVC system. A combination of thyristor-controlled reactors and fixed capacitors (TCR-FC) type SVC is recognized as having the most flexible control and high speed response, which has been widely utilized in power systems, is considered and designed to improve the response of a synchronous generator, as well as controlling the system voltage. The proposed parameter self tuning algorithm of fuzzy controller is based on the steepest decent method using two direction vectors which make error between inference values of fuzzy controller and output values of the specially selected PSS reduce steepestly. Using input-output data pair obtained from PSS, the parameters in antecedent part and in consequent part of fuzzy inference rules are learned and tuned automatically using the proposed steepest decent method. The related simulation results show that the proposed fuzzy controller is more powerful than the conventional ones.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.131-138
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• 2022

The demand for chiller equipment that keeps each machine at a constant temperature to maintain the best possible condition is growing rapidly. PID (Proportional Integral Derivation) control is a popular temperature control method. The error, which is the difference between the desired target value and the current system output value, is calculated and used as an input to the system using a proportional, integrator, and differentiator. Through such a closed-loop configuration, a desired final output value of the system can be reached using only the target value and the feedback signal. Furthermore, various temperature control methods have been devised as the control performance of various high-performance equipment becomes important. Therefore, it is necessary to design for accurate data-driven temperature control for chiller equipment. In this research, support vector regression is applied to the classic PID control for accurate temperature control. Simulated annealing is applied to find optimal PID parameters. The results of the proposed control method show fast and effective control performance for chiller equipment.

• Geomechanics and Engineering
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• v.9 no.3
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• pp.345-360
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• 2015

Wave equation analysis programs (WEAP) such as GRLWEAP and TNOWave were primarily developed for pre-driving analysis. They can also be used for post-driving measurement applications with some refinements. In the case of pre-driving analysis, the programs are used for the purpose of selecting the right equipment for a given ground condition and controlling stresses during pile driving processes. Recently, the program is increasingly used for the post-driving measurement application, where an assessment based on a variety of input parameters such as hammer, driving system and dynamic behaviour of soil is carried out. The process of this type of analysis is quite simple and it is performed by matching accurately known parameters, such as from CAPWAP analysis, to the parameters used in GRLWEAP analysis. The parameters that are refined in the typical analysis are pile stresses, hammer energy, capacity, damping and quakes. Matching of these known quantities by adjusting hammer, cushion and soil parameters in the wave equation program results in blow counts or sets and stresses for other hammer energies and capacities and cushion configuration. The result of this analysis is output on a Bearing Graph that establishes a relationship between ultimate capacity and net set per blow. A further application of this refinement method can be applied to the assessment of dynamic formulae, which are extensively used in pile capacity calculation during pile driving process. In this paper, WEAP analysis is carried out to establish the relationship between the ultimate capacities and sets using the various parameters and using this relationship to recalibrate the dynamic formula. The results of this analysis presented show that some of the shortcoming of the dynamic formula can be overcome and the results can be improved by the introduction of a correction factor.