• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.20-20
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• 2000

Back-propagation neural network (BPNN) is the most prevalently used paradigm in modeling semiconductor manufacturing processes, which as a neuron activation function typically employs a bipolar or unipolar sigmoid function in either hidden and output layers. In this study, applicability of another linear function as a neuron activation function is investigated. The linear function was operated in combination with other sigmoid functions. Comparison revealed that a particular combination, the bipolar sigmoid function in hidden layer and the linear function in output layer, is found to be the best combination that yields the highest prediction accuracy. For BPNN with this combination, predictive performance once again optimized by incrementally adjusting the gradients respective to each function. A total of 121 combinations of gradients were examined and out of them one optimal set was determined. Predictive performance of the corresponding model were compared to non-optimized, revealing that optimized models are more accurate over non-optimized counterparts by an improvement of more than 30%. This demonstrates that the proposed gradient-optimized teaming for BPNN with a linear function in output layer is an effective means to construct plasma models. The plasma modeled is a hemispherical inductively coupled plasma, which was characterized by a 24 full factorial design. To validate models, another eight experiments were conducted. process variables that were varied in the design include source polver, pressure, position of chuck holder and chroline flow rate. Plasma attributes measured using Langmuir probe are electron density, electron temperature, and plasma potential.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.210-215
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• 1998

To design a space frame structure by the conventional method is not easy in practical sense since it is generally a three-dimensional complicated form, and stability and nonlinear problems are not easily checked in the design process. This paper describes two modules, the Model Generator which is based on PATRAN user interface that enables users to generate a complicated finite element model; the Optimum Design Module which analyzes output results of analysis program, and designs members of a space frame. The Model Generator is based on PCL while C++ language is used in the Optimum Design Module. Structural analysis is performed by using ABAQUS. All of these modules constitute Space Frame Integrated Design System. The Core of the system is PATRAN database, in which the Model Generator creates information of a finite element model. Then, PATRAN creates input files needed for the analysis program from the information of the finite element model in the database, and in turn, imports output results of analysis program to the database. Finally, the Optimum Design Module processes member grouping of a space frame based on the output results, and performs optimal member selection of a space frame. This process is repeated until the desired optimum structural members are obtained.

• Bulletin of the Korean Chemical Society
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• v.24 no.3
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• pp.279-284
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• 2003

The One Pass Method (OPM) previously presented for the identification of single input single output systems is used to estimate the parameters of a Decentralized Control System (DCS). The OPM is a linear and therefore a simple estimation method. All of the calculations are performed in one pass, and no initial parameter guess, iteration, or powerful search methods are required. These features are of interest especially when the parameters of multi input-output model are estimated. The benefits of the OPM are revealed by comparing its results against those of two recently published methods based on pulse testing. The comparison is performed using two databases from the literature. These databases include single and multi input-output process transfer functions and relevant disturbances. The closed loop responses of these processes are roughly captured by the previous methods, whereas the OPM gives much more accurate results. If the parameters of a DCS are estimated, the OPM yields the same results in multi or single structure implementation. This is a novel feature, which indicates that the OPM is a convenient and practice method for the parameter estimation of multivariable DCSs.

• Journal of Energy Engineering
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• v.13 no.2
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• pp.112-117
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• 2004

Volatile organic compounds (VOCs) are low calorific value gases (LCVG) emitted from chemical processes such as painting booth, dye works and drying processes etc. Characteristics of VOCs are low calorific values less than 150kcal/㎥, high activation energy for ignition and low energy output. These characteristics usually make combustion unstable and its treatment processes needs high-energy consumption. The cyclone combustion system is suitable for LCVG burning because it can recirculate energy through a high swirling flow to supply the activation energy for ignition, increases energy density In make a combustion temperature higher than usual swirl combustor and also increases mixing intensity. This research was conducted to develop optimized cyclone combustion system for thermal oxidation of VOCs. This research was executed to establish the effect of swirl number with respect to the combustion temperature and composition of exhausted gas in the specific combustor design.

• Journal of Information Technology Applications and Management
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• v.23 no.3
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• pp.13-23
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• 2016

In recent years, big data has usefully been deployed by organizations with the aim of getting a better prediction for the future. Moreover, knowledge management systems are being used by organizations to identify and create knowledge. Here, the output from analysis of big data and a knowledge management system are used to develop a new model with the goal of minimizing the cost of implementing new recognized processes including staff training, transferring and employment costs. Strategies are proposed from big data analysis and new processes are defined accordingly. The company requires various skills to execute the proposed processes. Organization's current experts and their skills are known through a pre-established knowledge management system. After a gap analysis, managers can make decisions about the expert arrangement, training programs and employment to bridge the gap and accomplish their goals. Finally, deduction graph is used to analyze the model.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.9
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• pp.997-1005
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• 1990

Integral Error and State Feedback (IESF) controller which incorporates state feedback as a modern control scheme and integral action as a classical control scheme has better performance than that of conventional PID controller in linear time invariant system. But the structure of the IESF controller requires all the state variables of the system and is applicable only to pole assignable linear time invariant systems without time delay. Many industrial processes have large time delay and it is impossible to directly apply IESF control scheme to those processes. In this paper, a new controller structure, Modified Integral Error and State Feedback (MIESF) has been suggested in order to effectively control processes having time delay and its performance has been analyzed and its effectiveness has also been confirmed. As the proposed controller uses output feedback scheme based on integral error and state feedback (IESF) method, it can be simply designed by pole assignment algorithm irrespective of the order of the process. The MIESF controller can follow setpoint changes without overshoot. It is robuster than conventional Smith-Predictor plus PI(D) controller in case of occurring time delay mismatch and extra parameter mismatches between the process and the model. It can enhance control performance by intentional time delay mismatch.

• Journal of Welding and Joining
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• v.32 no.1
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• pp.53-60
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• 2014

The automatic arc welding is generally accepted as the preferred joining technique and commonly chosen for assembly of large metal structures such as in areas of automotive, aircraft and shipbuilding due to its joint strength, reliability, and low cost compared to other joint processes. Recently, several mathematical models have been developed and studied for control and monitoring welding quality, productivity, microstructure and weld properties in arc welding processes. This study indicates the prediction of process parameters for the expected welding quality with accordance to the adaptive GTA welding process. Furthermore, the mathematical models is also develop to aid the selection of an optimal welding process as the generation of process controls to predict the bead geometry as a function output parameters in the GTA welding process. The developed models through this study showed comparatively excellent predicted results, and will extend to other welding processes to integrate an optimized system for the robotic welding process.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.155-158
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• 2007

Printed organic thin-film transistor(OTFT) to use as a switching device for an organic light emitting diode(OLED) were fabricated in the microcontact printing and direct printing processes at room temperature. The gate electrodes($5{\mu}m$, $10{\mu}m$, and $20{\mu}m$) of OTFT was fabricated using microcontact printing process, and source/drain electrodes ($W/L=500{\mu}m/5{\mu}m$, $500{\mu}m/10{\mu}m$, and $500{\mu}m/20{\mu}m$) was fabricated using direct printing process with hard poly(dimethylsiloxane)(h-PDMS) stamp. Printed OTFT with dielectric layer was formed using special coating system and organic semiconductor layer was ink-jet printing process. Microcontact printing and direct printing processes using h-PDMS stamp made it possible to fabricate printed OTFT with channel lengths down to $5{\mu}m$, and reduced the process by 20 steps compared with photolithography. As results of measuring he transfer characteristics and output characteristics of OTFT fabricated with the printing process, the field effect characteristic was verified.

• Journal of the Korean Institute of Gas
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• v.3 no.1
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• pp.27-32
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• 1999

Chemical processes are highly nonlinear, multivariable systems and have complex structures. However, the controllability evaluation procedures are complicated, and the required information is very often unknown at the early design stage. Therefore, it is necessary to develop a procedure to evaluate and enhance controllability while designing processes and plants. To evaluate controllability in the design stage, it is most efficient to analyze process structure. Relative order can be used as a measure of 'physical closeness' between input and output variable. Structural controllability analysis using relative order is shown to be effective in a case study of heat exchanger network synthesis.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.5
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• pp.18-24
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• 2003

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative sybcycles. Previous researches predifined the numbers of design processes in groups, but these group sizes should be determined optimally to balance the computing time of each groups. This paper proposes adaptive decomposition method, which determines the group sizes and the order of processes simultaneously to raise design efficiency by expanding the chromosome of the genetic algorithm. Finally, two sample cases are presented to show the effects of optimizing the sequence of processes with the adaptive decomposition method.