• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.12
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• pp.69-77
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• 1999

This paper describes the design of fuzzy digital PID controller using simplified indirect inference method. First, the fuzzy digital PID controller is derived from the conventional continuous time linear digital PID controller. Then the fuzzification, control-rule base, and defuzzification using SIM in the design of the fuzzy digital controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional digital PID controller, which has the same linear structure, but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability, particularly when the process to be controlled is nonlinear. When the SIM is applied, the fuzzy inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control performance of the one proposed by D. Misir et al.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.11-16
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• 2014

In this paper, the two-wheels robot using a predictive controller to maintain the balance of the posture control in real time have been examined. A reaction wheel pendulum control method is adopted to maintain the balance while the bicycle robot is driving. The objective of this research was to design and implement a self-balancing algorithm using the dsPIC30F4013 embedded processor. To calculate the attitude in ARS using 2 axis gyro(roll, pitch) and 3 axis accelerometers (x, y, z). In this study, the disturbance of the posture for the asymmetrical propose to overcome the predictive controller which was a problem in the control of a remote system by introducing the two wheels of the robot controller and the linear prediction of the system controller combines the simulation was performed. Also, the robust characteristic for realizing the goal of designing a loop filter too robust controller is designed so that satisfactory stability of the control system to improve stability of the system to minimize degradation of performance was confirmed.

• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.901-906
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• 2009

This paper proposes a call admission control(CAC) method for wireless networks, which is based on the upper bound of a possibility distribution of handoff calls dropping rates. The possibility distribution is estimated in a fuzzy inference and a learning algorithm in neural network. The learning algorithm is considered for tuning the membership functions(then parts)of fuzzy rules for the inference. The fuzzy inference method is based on a weighted average of fuzzy sets. The proposed method can avoid estimating excessively large handoff calls dropping rates, and makes possibile self-compensation in real time for the case where the estimated values are smaller than real values. So this method makes secure CAC, thereby guaranteeing the allowed CDR. From simulation studies we show that the estimation performance for the upper bound of call dropping rate is good, and then handoff call dropping rates in CAC are able to be sustained below user's desired value.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.429.2-429.2
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• 2014

ZnO nanowire is known as synthesizable and good mechanical properties. And, stimuli-responsive polymer is widely used in the application of tunable sensing device. So, we combined these characteristics to make precise tunable sensing devise. In this work, we investigate the dependence of ZnO nanowire alignment and morphology on si substrate using nanosphere template with various conditions via hydrothermal process. Also, pH-temperature dependant tuning ability of nanostructure was studied. The brief experimental scheme is as follow. First, Zno seed layer was coated on a si wafer ($20{\times}20mm$) by spin coater. And then $1.15{\mu}m$ sized close-packed PS nanospheres were formed on a cleaned si substrate by using gas-liquid-solid interfacial self-assembly method. After that, zinc oxide nanowires were synthesized using hydrothermal method. Before the wire growth, to specify the growth site, heat treatment was performed. Finally, NIPAM(N-Isopropylacrylamide) was coated onto as-fabricated nanostructure and irradiated by UV light to form the PNIPAM network. The morphology, structures and optical properties are investigated by FE-SEM(Field Emission Scanning electron Microscopy), XRD(X-ray diffraction), OM(Optical microscopy), and WCA(water contact angle).

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.438-441
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• 2007

Recently, due to molecular biology and engineering technology, DNA microarray makes people watch thousands of genes and the state of variation from the tissue samples of living body. With DNA Microarray, it is possible to construct a genetic group that has similar expression patterns and grasp the progress and variation of gene. This paper practices Cluster Analysis which purposes the discovery of biological subgroup or class by using gene expression information. Hence, the purpose of this paper is to predict a new class which is unknown, open leukaemia data are used for the experiment, and MCL (Markov CLustering) algorithm is applied as an analysis method. The MCL algorithm is based on probability and graph flow theory. MCL simulates random walks on a graph using Markov matrices to determine the transition probabilities among nodes of the graph. If you look at closely to the method, first, MCL algorithm should be applied after getting the distance by using Euclidean distance, then inflation and diagonal factors which are tuning modulus should be tuned, and finally the threshold using the average of each column should be gotten to distinguish one class from another class. Our method has improved the accuracy through using the threshold, namely the average of each column. Our experimental result shows about 70% of accuracy in average compared to the class that is known before. Also, for the comparison evaluation to other algorithm, the proposed method compared to and analyzed SOM (Self-Organizing Map) clustering algorithm which is divided into neural network and hierarchical clustering. The method shows the better result when compared to hierarchical clustering. In further study, it should be studied whether there will be a similar result when the parameter of inflation gotten from our experiment is applied to other gene expression data. We are also trying to make a systematic method to improve the accuracy by regulating the factors mentioned above.

• Korean Journal of Materials Research
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• v.22 no.5
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• pp.243-248
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• 2012

ZrN nanoparticles were prepared by an exothermic reduction of $ZrCl_4$ with $NaN_3$ in the presence of NaCl flux in a nitrogen atmosphere. Using a solid-state combustion approach, we have demonstrated that the zirconium nitride nanoparticles synthesis process can be completed in only several minutes compared with a few hours for previous synthesis approaches. The chemistry of the combustion process is not complex and is based on a metathesis reaction between $ZrCl_4$ and $NaN_3$. Because of the low melting and boiling points of the raw materials it was possible to synthesize the ZrN phase at low combustion temperatures. It was shown that the combustion temperature and the size of the particles can be readily controlled by tuning the concentration of the NaCl flux. The results show that an increase in the NaCl concentration (from 2 to 13 M) results in a temperature decrease from 1280 to $750^{\circ}C$. ZrN nanoparticles have a high surface area (50-70 $m^2/g$), narrow pore size distribution, and nano-particle size between 10 and 30 nm. The activation energy, which can be extracted from the experimental combustion temperature data, is: E = 20 kcal/mol. The method reported here is self-sustaining, rapid, and can be scaled up for a large scale production of a transition metal nitride nanoparticle system (TiN, TaN, HfN, etc.) with suitable halide salts and alkali metal azide.

• Nuclear Engineering and Technology
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• v.27 no.3
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• pp.336-343
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• 1995

The Self Tuning Regulator(STR) method which is an approach of adaptive control theory, is ap-plied to design the fully automatic power controller of the nonlinear reactor model. The adaptive control represent a proper approach to design the suboptimal controller for nonlinear, time-varying stochastic systems. The control system is based on a third­order linear model with unknown, time-varying parameters. The updating of the parameter estimates is achieved by the recursive extended least square method with a variable forgetting factor. Based on the estimated parameters, the output (average coolant temperature) is predicted one-step ahead. And then, a weighted one-step ahead controller is designed so that the difference between the output and the desired output is minimized and the variation of the control rod position is small. Also, an integral action is added in order to remove the steady­state error. A nonlinear M plant model was used to simulate the proposed controller of reactor power which covers a wide operating range. From the simulation result, the performances of this controller for ramp input (increase or decrease) are proved to be successful. However, for step input this controller leaves something to be desired.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.2
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• pp.162-172
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• 2011

In this paper, a design and fabrication of 4 W power amplifier for the WiMAX frequency band(2.3~2.7 GHz) are presented. The adopted active device is a commercially available GaN HEMT chip of Triquint Company, which is recently released. The optimum input and output impedances are extracted for power amplifier design using a specially self-designed tuning jig. Using the adopted impedances value, class-F power amplifier was designed based on EM simulation. For integration and matching in the small package module, spiral inductors and interdigital capacitors are used. The fabricated power amplifier with $4.4{\times}4.4\;mm^2$ shows the efficiency above 50 % and harmonic suppression above 40 dBc for second(2nd) and third(3rd) harmonic at the output power of 36 dBm.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.54-67
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• 2005

This paper provides an evaluation of an optimization-based, multiple-input double-output (MIDO) run-to-run (R2R) control scheme for general semiconductor manufacturing processes. The controller in this research, termed adaptive dual response optimizing controller (ADROC), can serve as a process optimizer as well as a recipe regulator between consecutive runs of wafer fabrication. In evaluation, it is assumed that the equipment model could be appropriately described by a pair of second-order polynomial functions in terms of a set of controllable variables. Of practical relevance is to consider a drifting effect in the equipment model since in common semiconductor practice the process tends to drift due to machine aging and tool wearing. We select a typical application of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing in this evaluation, and there are five different CMP process scenarios demonstrated, including mean shift, variance increase, and IMA disturbances. For the controller, ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual response approach, arising from the response surface methodology (RSM) literature, is used to seek the optimum recipe within the acceptability region for the execution of next run. The main components of ADROC are described and its control performance is assessed. It reveals from the evaluation that ADROC can provide excellent control actions for the MIDO R2R situations even though the process exhibits complicated, nonlinear interaction effects between control variables, and the drifting disturbances.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.31-38
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• 2024

This study proposes hyper-parameter settings for developing a generative AI-based learning support tool to facilitate programming education in online distance learning. We implemented an experimental tool that can set research hyper-parameters according to three different learning contexts, and evaluated the quality of responses from the generative AI using the tool. The experiment with the default hyper-parameter settings of the generative AI was used as the control group, and the experiment with the research hyper-parameters was used as the experimental group. The experiment results showed no significant difference between the two groups in the "Learning Support" context. However, in other two contexts ("Code Generation" and "Comment Generation"), it showed the average evaluation scores of the experimental group were found to be 11.6% points and 23% points higher than those of the control group respectively. Lastly, this study also observed that when the expected influence of response on learning motivation was presented in the 'system content', responses containing emotional support considering learning emotions were generated.