• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.32-38
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• 2007

A batch Process has a multi-way data structure that consists of batch-time-variable axis, so the statistical modeling of a batch process is a difficult and challenging issue to the process engineers. In this study, We applied a statistical process control technique to the general batch process data, and implemented a fault-detection and Statistical process control system that was able to detect, identify and diagnose the fault. Semiconductor etch process and semi-batch styrene-butadiene rubber process data are used to case study. Before the modeling, we pre-processed the data using the multi-way unfolding technique to decompose the data structure. Multivariate regression techniques like support vector regression and partial least squares were used to identify the relation between the process variables and process condition. Finally, we constructed the root mean squared error chart and variable contribution chart to diagnose the faults.

• 전자공학회논문지 IE
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• v.45 no.4
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• pp.11-18
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• 2008

This study is implementation of attitude control system(ACS - Attitude Control System). for a multi legs robot. This study designs H/W of Inertial Measurement Unit (IMU) and attitude control algorithm S/W. Compare performance with Mtx and MTx in order to verify action performance of this system after implementation, and will verify a system integrated IMU of a multi-legs robot. ACS uses Gyro and an accelerometer and an earth magnetism sensor, and it is a system controlling a roll, pitch angle attitude of an object. Generally, low price MEMS is difficult to calculate a correct situation of an object as an error occurs severely the Inertial sensor. This study implements IMU in order to develop ACS as use MEMS, accelerometer, Gyro sensor and earth magnetism sensor. Design algorithm each a roll, pitch, yaw attitude guaranteeing regular performance, and do poling in a system as include an attitude calculation program in an IMU system implemented. Mixed output of Gyro and an accelerometer, and recompensed a roll, pitch angle, and loaded in this study on a target platform in order to implement the ACS which guaranteed performance more than a continuously regular level, and operated by real time, and did porting, and verified.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.469-476
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• 1998

In the case of acquisition and displaying medical Images such as ultrasonography and endoscopy on VGA monitor of PC system, image degradation of tear-drop appears through scan conversion. In this study, we compare several methods which can solve this degradation and implement the hardware system that resolves this problem in real-time with PC. It is possible to represent high quality image display and real-time processing and acquisition with specific de-interlacing device and PCI bridge on our hardware system. Image quality is improved remarkably on our hardware system. It is implemented as PC-based system, so acquiring, saving images and describing text comment on those images and PACS networking can be easily implemented.metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11B
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• pp.1501-1509
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• 2001

Recently, the use of wireless communication systems has been rapidly increasing, which results in a difficult problem in efficient control of limited frequency resources. As a way of solving this problem, the ultra wideband time hopping impulse radio system attracts much attention. The impulse radio system communicates pulse position modulated data using Gaussian monocycle pulses of very short duration less than 1 nsec. Thus the transmitted signal has very low power spectral density and ultra wide bandwidth from near D.C. to a few GHz. It is blown that it hardly interferes with the existing communication systems because of its very low power spectral density. The purpose of this paper is to characterize multipath propagation of the impulse radio signal and to evaluate the performance of the correlator-based receiver for the multipath environments. In this paper, we consider the deterministic two-path model and the statistical indoor multipath model of Saleh and Valenzuela. For the two-path model the output of the correlator with the ideal reference waveform varies according to the relative difference between the indirect path delay and the time interval of PPM, and to the indirect path gains. In addition, the characteristics of bit error rates is measured for the two models through computer simulation. The simulation results indicate that the performance of the impulse radio system depends both on the relative difference between the indirect path delay and the time interval of PPM, and on the indirect path gains. Furthermore, it is observed that the reference signal designed for the AWGN channel can not be applied to the multipath channels.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.593-606
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• 2016

Computer simulation is widely used in a variety of computational science and engineering fields, including computational fluid dynamics, nano physics, computational chemistry, structural dynamics, and computer-aided optimal design, to simulate the behavior of a system. As the demand for the accuracy and complexity of the simulation grows, however, the cost of executing the simulation is rapidly increasing. It, therefore, is very important to lower the total execution time of the simulation especially when that simulation makes a huge number of repetitions with varying values of input parameters. In this paper we develop a simulation service system that provides the ability to predict the result of the requested simulation without actual execution for that simulation: by recording and then returning previously obtained or predicted results of that simulation. To achieve the goal of avoiding repetitive simulation, the system provides two main functionalities: (1) storing simulation-result records into database and (2) predicting from the database the result of a requested simulation using statistical machine learning techniques. In our experiments we evaluate the prediction performance of the system using real airfoil simulation result data. Our system on average showed a very low error rate at a minimum of 0.9% for a certain output variable. Using the system any user can receive the predicted outcome of her simulation promptly without actually running it, which would otherwise impose a heavy burden on computing and storage resources.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.739-752
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• 2002

Recently, to utilize the limited bandwidth effectively, the concept of partial response (PR) signaling has widely been adopted in both the high-speed data transmission and high-density digital recording/playback systems such as digital microwave, digital subscriber loops, hard disk drives, digital VCR's and digital versatile recordable disks and so on. This paper is concerned with adaptive equalization of partial response channels particularly for the magnetic recording channels. Specifically we study how the PR channel equalizers work for different choices of desired or reference signals used for adjusting the equalizer weights. In doing so, we consider three different configurations that are actually implemented in the commercial products mentioned above. First of all, we show how to compute the theoretical values of the optimum Wiener solutions derived by minimizing the mean-squared error (MSE) at the equalizer output. Noting that this equalizer MSE measure cannot be used to fairly compare the three configurations, we propose to use the data MSE that is computer just before the final detector for the underlying PR system. We also express the data MSE in terms of the channel impulse response values, source data power and additive noise power, thereby making it possible to compare the performance of the configurations under study. The results of extensive computer simulation indicate that our theoretical derivation is correct with high precision. Comparing the three configurations, it also turns out that one of the three configurations needs to be further improved in performance although it has an apparent advantage over the others in terms of memory size when implemented using RAM's for the decision feedback part.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.21-26
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• 2016

This paper presents a step-down DC-DC buck converter with a CCM/DCM dual-mode function for the internal power stage of portable electronic device. The proposed converter that is operated with a high frequency of 1 MHz consists of a power stage and a control block. The power stage has a power MOS transistor, inductor, capacitor, and feedback resistors for the control loop. The control part has a pulse width modulation (PWM) block, error amplifier, ramp generator, and oscillator. In this paper, an external capacitor for compensation has been replaced with a multiplier equivalent CMOS circuit for area reduction of integrated circuits. In addition, the circuit includes protection block, such as over voltage protection (OVP), under voltage lock out (UVLO), and thermal shutdown (TSD) block. The proposed circuit was designed and verified using a $0.18{\mu}m$ CMOS process parameter by Cadence Spectra circuit design program. The SPICE simulation results showed a peak efficiency of 94.8 %, a ripple voltage of 3.29 mV ripple, and a 1.8 V output voltage with supply voltages ranging from 2.7 to 3.3 V.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.11
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• pp.893-900
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• 2021

In this study, air density in a narrow test section is measured using a laser absorption spectroscopy system that detects oxygen absorption lines. An absorption line pair at 13156.28 and 13156.62 cm^-1 are detected. A gas chamber with a height of 40 mm is used as a narrow test section. A triangular spiral-shaped laser path is applied in the gas chamber to amplify absorption strength by extending laser beam path length. A well-known logarithm amplifier and a secondary amplifier are used to electrically amplify absorption signal. An AC-coupling is applied after the logarithm amplifier for signal saturation prevention and noise suppression. Procedure of calculating spectral absorbance from output signal is introduced considering the logarithm amplifier circuit configuration. Air density is determined by fitting the theoretically calculated spectral absorbance to the measured spectral absorbance. Test conditions with room temperature and a pressure range of 10~100 kPa are made in a gas chamber using a Bourdon pressure gauge. It is confirmed that air density in a narrow test section can be measured within a 16 % error through absorption signal amplification using a triangular spiral-shaped beam path and a logarithm amplifier.

• Journal of the Korean Institute of Gas
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• v.22 no.6
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• pp.90-103
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• 2018

This paper presents the development of two ANN models which can predict an optimum production rate by controlling choke size in oil well, and gas injection rate in gas-lift well. The input data was solution gas-oil ratio, water cut, reservoir pressure, and choke size or gas injection rate. The output data was wellhead pressure and production rate. Firstly, a range of each parameters was decided by conducting sensitive analysis of input data for onshore oil well. In addition, 1,715 sets training data for choke size decision model and 1,225 sets for gas injection rate decision model were generated by nodal analysis. From the results of comparing between the nodal analysis and the ANN on the same reservoir system showed that the correlation factors were very high(>0.99). Mean absolute error of wellhead pressure and oil production rate was 0.55%, 1.05% with the choke size model, respectively. And the gas injection rate model showed the errors of 1.23%, 2.67%. It was found that the developed models had been highly accurate.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.