• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.43-48
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• 2002

This paper discusses the application of fuzzy-ARTMAP neural network to compensate the nonlinearity of satellite communication channel. The fuzzy-ARTMAP is the class of ART(adaptive resonance theory) architectures designed fur supervised loaming. It has capabilities not fecund in other neural network approaches, that includes a small number of parameters, no requirements fur the choice of initial weights, automatic increase of hidden units, and capability of adding new data without retraining previously trained data. By a match tracking process with vigilance parameter, fuzzy-ARTMAP neural network achieves a minimax teaming rule that minimizes predictive error and maximizes generalization. Thus, the system automatically leans a minimal number of recognition categories, or hidden units, to meet accuracy criteria. As a input-converting process for implementing fuzzy-ARTMAP equalizer, the sigmoid function is chosen to convert actual channel output to the proper input values of fuzzy-ARTMAP. Simulation studies are performed over satellite nonlinear channels. QPSK signals with Gaussian noise are generated at random from Volterra model. The performance of proposed fuzzy-ARTMAP equalizer is compared with MLP equalizer.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.4
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• pp.13-20
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• 2003

The model of the transistor saturation current of the BJT for integrated circuits based upon the semiconductor physics is proposed. The method for calculating the doping profile in the base region using process conditions is presented and the method for calculating the base Gummel number of lateral PNP BJT and vertical NPN BJT is proposed. The transistor saturation currents of NPN BJT using 20V and 30V process conditions obtained from the proposed method show an average relative error of 6.7% compared with the measured data and the transistor saturation currents of PNP BJT show an average relative error of 6.0% compared with the measured data.

• Wind and Structures
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• v.39 no.1
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• pp.15-30
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• 2024

In this paper, detailed wind field data of the full path of typhoon "Bailu" were obtained based on site measurements. Typhoon "Bailu" made first landfall southeast of the Taiwan Strait with a wind speed of approximately 30 m/s near the center of the typhoon eye and a second landfall in Dongshang County in Fujian Province. The moving process is classified into 3 regions for analysis and comparison. Detailed analyses of wind characteristics including wind profile, turbulence intensity, gust factor, turbulence integral scale and wind power spectral density function at the full process of the typhoon are conducted, and the findings are presented in this paper. Wind speed shows significant dependence on both the direction of the moving path and the distance between the typhoon center and measurement site. Wind characteristics significantly vary with the moving path of the typhoon center. The relationship between turbulence intensity and gust factor at different regions is investigated. The integral turbulence scales and wind speed are fitted by a Gaussian model. Such analysis and conclusions may provide guidance for future bridge wind-resistant design in engineering applications.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.4
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• pp.153-164
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• 2019

This paper presents the development of an end-to-end numerical simulator for signal design of the next generation global navigation satellite system (GNSS). The GNSS services are an essential element of modern human life, becoming a core part of national infra-structure. Several countries are developing or modernizing their own positioning and timing system as their demand, and South Korea is also planning to develop a Korean Positioning System (KPS) based on its own technology, with the aim of operation in 2034. The developed simulator consists of three main units such as a signal generator, a channel unit, and a receiver. The signal generator is constructed based on the actual navigation satellite payload model. For channels, a simple Gaussian channel and land mobile satellite (LMS) multipath channel environments are implemented. A software receiver approach based on a commercial GNSS receiver model is employed. Through the simulator proposed in this paper, it is possible to simulate the entire transceiver chain process from signal generation to receiver processing including channel effect. Finally, numerical simulation results for a simple example scenario is analyzed. The use of the numerical signal simulator in this paper will be ideally suited to design a new navigation signal for the upcoming KPS by reducing the research and development efforts, tremendously.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.70-75
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• 1998

The comparison of Topex/Poseidon sea surface heights and Tide Gauge sea levels was studied in the South Indian Ocean after Topex/Poseidon mission of about 3 years (11- 121 cycles) from January 1993 through December 1995. The user's handbook (AVISO) for sea surface height data process was used in this study Topex/Poseidon sea suface heights ($\zeta$$^{T/P}$), satellite data at the point which is very closed to Tide Gauge station, were chosen in the same latitude of Tide Gauge station. These data were re-sampled by a linear interpolation with the interval of about 10 days, and were filtered by the gaussian filter with a 60 day-window. Tide Gauge sea levels ($\zeta$$^{Argos}$, $\zeta$$^{In-situ}$ and $\zeta$$^{Model}$), were also treated with the same method as satellite data. The main conclusions obtained from the root-mean-square and correlation coefficient were as follows: 1) to Produce Tide Gauge sea levels from bottom pressure, in-situ data of METEO-FRANCE showed very good values against to the model data of ECMWF and 2) to compare Topex/Poseidon sea surface heights of Tide Gauge sea levels, the results of the open sea areas were better than those of the coast and island areas.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.474-484
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• 2017

This paper studies the applicability of an efficient numerical model based on artificial neural networks (ANNs) to predict the dynamic responses of the wing structure of an airplane due to atmospheric turbulence in the time domain. The turbulence velocity is given in the form of a stationary Gaussian random process with the von Karman power spectral density. The wing structure is modeled by a classical beam considering bending and torsional deformations. An unsteady vortex-lattice method is applied to estimate the aerodynamic pressure distribution on the wing surface. Initially, the trim condition is obtained, then structural dynamic responses are computed. The numerical solution of the wing structure's responses to a random turbulence profile is used as a training data for the ANN. The current ANN is a three-layer network with the output fed back to the input layer through delays. The results from this study have validated the proposed low-cost ANN model for the predictions of dynamic responses of wing structures due to atmospheric turbulence. The accuracy of the predicted results by the ANN was discussed. The paper indicated that predictions for the bending moments are more accurate than those for the torsional moments of the wing structure.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.981-984
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• 2010

In this paper, we used Support Vector Machines(SVMs) as the learning method, one of Artificial Neural Network, to segregated from the continuous speech into phonemes, an initial, medial, and final sound, and then, performed continuous speech recognition from it. A Decision boundary of phoneme is determined by algorithm with maximum frequency in a short interval. Speech recognition process is performed by Continuous Hidden Markov Model(CHMM), and we compared it with another phoneme segregated from the eye-measurement. From the simulation results, we confirmed that the method, SVMs, we proposed is more effective in an initial sound than Gaussian Mixture Models(GMMs).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.162-165
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• 2005

The objective of this research work is to investigate into the three-dimensional temperature distribution using quasi steady-state heat transfer analysis fur the case of the laser cutting of CSP 1N sheet using high power CW Nd:YAG laser. The laser heat source is assumed as a volumetric heat source with a gaussian heat distribution in a plane. Through the comparison of the results of analyses with those of the experiments, the optimal finite element model is obtained. Finally, characteristics of the three-dimensional heat transfer and temperature distribution have been estimated by the optimal finite element model.