• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.91-98
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• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• Korean Membrane Journal
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• v.1 no.1
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• pp.25-37
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• 1999

positron annihilation Lifetime Spectroscopy has been extensively applied in recent years to investigate the free volume in polymers owing to the capability of the electron-positron bound system (positronium) to probe the typical size of sub-nanometric cavities among the macromolecular chains. In this paper we show recent results obtained through this technique in some amorphous polymeric mem-branes(olyurethanes. PUs and polytrimethilsylilpropine PTMSP) after a brief survey of the general features of the annihilation process as well as of the experimental apparatus. Lifetime of o-ps decay({{{{ tau _3}}}}) in PUs increases going from sub {{{{ TAU _g}}}} to over {{{{ TAU _g}}}} temperatures following a sigmoid curve. The coefficient of dilatation of the free volume fraction is shown to be the sum of two contributes due to the variation with T of the number of holes and of their mean volume. PAL spectrum of PTMSP freshly prepared shows four lifetime components: {{{{ tau _3}}}} and {{{{ tau _4}}}}: only are useful for free volume study. Two kinds of holes of different equivalent radius are reported ({{{{ gamma _s}}}} 4.60 nm and {{{{ gamma _1}}}} 0.754) The equivalent volume does not change in a range of 100 K. however the physical aging increases density and decreases oxygen permeability while {{{{ gamma _s}}}} goes down to 0.374 and r1 to 0.735 The number of holes obtained from the intensities{{{{ IOTA _3}}}} and {{{{ IOTA _4}}}} of PAL spectra decreases with aging 21.7% and 3.5% for large and small holes respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.55-65
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• 2004

When a neural network is used to solve a given problem it is necessary to match the complexity of the network to that of the problem because the complexity of the network significantly affects its learning capability and generalization performance. Thus, it is desirable to have an algorithm that can find appropriate network structures in a self-organizing way. This paper proposes algorithms which automatically organize feed forward multi-layer neural networks with sigmoid hidden neurons for given problems. Using both constructive procedures and pruning procedures, the proposed algorithms try to find the near optimal network, which is compact and shows good generalization performance. The performances of the proposed algorithms are tested on four function regression problems. The results demonstrate that our algorithms successfully generate near-optimal networks in comparison with the previous method and the neural networks of fixed topology.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.7
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• pp.32-38
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• 2012

In this paper, to eliminate intersymbol interference of the received signal due to multipath propagation, a parallel equalization algorithm using two error estimation functions is proposed. In the proposed algorithm, multilevel two-dimensional signals are considered as equivalent binary signals, then error signals are estimated using the sigmoid nonlinearity effective at the initial phase equalization and threshold nonlinearity with high steady-state performance. The two errors are scaled by a weight depending on the relative accuracy of the two error estimations, then two filters are updated differentially. As a result, the combined output of two filters was to be the optimum value, fast convergence at initial stage of equalization and low steady-state error level were achieved at the same time thanks to the combining effect of two operation modes smoothly. Usefulness of the proposed algorithm was verified and compared with the conventional method through computer simulations.

• Journal of the Korean Chemical Society
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• v.37 no.10
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• pp.867-873
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• 1993

Influences of crystallization time and $H_2O/Al_2O_3$ ratio of the reaction mixtures on the synthesis of AlPO$_4$-5 molecular sieve have been studied by X-ray powder diffraction, nitrogen adsorption, scanning electron microscope (SEM), and solid state $^{27}$Al magic angle spinning nuclear magnetic resonance (MAS NMR) techniques. The degree of crystallinity of AlPO$_4$-5 follows a sigmoid pattem as crystallization time increases. The induction period is shorter than 1 h when the crystallization process is carried out at 150$^{\circ}$C. The conversion of reactants to product, AlPO$_4$-5, can be clearly observed, and all of the determined physical properties change abruptly after about 2 h. It is found that increase in $H_2O/Al_2O_3$ ratio of the reaction mixtures not only changes the crystal morphology from aggregates to hexagonal single crystals, but also results in the formation of longer AlPO$_4$-5 crystals.

• Advances in pediatric surgery
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• v.8 no.2
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• pp.138-142
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• 2002

Mesenteric and omental cysts are rare intra-abdominal lesions in childhood, and may present various clinical features such as an asymptomatic mass or an acute abdomen. Therefore, these entities are frequently misdiagnosed preoperatively or are found only incidentally at operation for other conditions. We analyzed our experiences of 19 cases in a 19 year period from 1981 to 1999, at College of Medicine, Catholic University of Korea. There were 12 boys and 7 girls with a mean age of 4.8 years (range, 3 days to 15 years). Common presenting symptoms were abdominal pain (47%), abdominal distension (31%), abdominal mass (24%), vomiting (15%) and fever (10%). Ultrasonography was the most preferred method of diagnosis. Other diagnostic modalities include CT, MRI, and abdominal ascites tapping in selected patients. Location of the mesenteric cysts was small bowel mesentery in nine, the right mesocolon and retroperitoneum in one, the left mesocolon in one, and the jejunum, sigmoid-colon mesentery in one. Most of the patients underwent cyst excision, but six patients required concomitant bowel resection for complete removal of the lesions, and two patients underwent unroofing and simple aspiration respectively. There was one mortality case due to sepsis.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.5
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• pp.45-53
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• 2000

This paper presents an analysis of the dynamical behavor in the chaotic neuron fabricated using 0.8${\mu}{\textrm}{m}$ single poly CMOS technology. An approximated empirical equation models for the sigmoid output function and chaos generative block of the chaotic neuron are extracted from the measurement data. Then the dynamical responses of the chaotic neuron such as biurcation diagram, frequency responses, Lyapunov exponent, and average firing rate are calculated with numerical analysis. In addition, we construct the chaotic neural networks which are composed of two chaotic neurons with four synapses and obtain bifurcation diagram according to synaptic weight variation. And results of experiments in the single chaotic neuron and chaotic neural networks by two neurons with the $\pm$2.5V power supply and sampling clock frequency of 10KHz are shown and compared with the simulated results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.18 no.2
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• pp.179-189
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• 2000

This study is tried to organic combination in implementing the suitability analysis of urban landuse between GSIS and ANN(Artificial Neural Network). ANN has merit that can decide rationally connectivity weights among neural network nodes through procedure of learning. It is estimated to be possible that replacing the weight among factors needed in spatial analysis of the connectivity weight on neural network. This study is composed of two kinds of neural networks to be executed. First neural network was used in the suitability analysis of landuse and second one was oriented to analyze of optimum landuse pattern. These neural networks were learned with back-propagation algorithm using the steepest gradient which is embodied by C++ program and used sigmoid function as a active function. Analysis results show landuse suitability map and optimum landuse pattern of study area consisted of residental, commercial. industrial and green zone in present zoning system. Each result map was written by the Grid format of Arc/Info. Also, suitability area presented in the suitability map and optimum landuse pattern show distribution pattern consistent with theroretical concept or urban landuse plan in aspect of location and space structure.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1759-1769
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• 1997

The Error BackPropagation (EBP) algorithm for multi-layered neural networks is widely used in various areas such as associative memory, speech recognition, pattern recognition and robotics, etc. Nevertheless, many researchers have continuously published papers about improvements over the original EBP algorithm. The main reason for this research activity is that EBP is exceeding slow when the number of neurons and the size of training set is large. In this study, we developed new learning speed acceleration methods using variable learning rate, variable momentum rate and variable slope for the sigmoid function. During the learning process, these parameters should be adjusted continuously according to the total error of network, and it has been shown that these methods significantly reduced learning time over the original EBP. In order to show the efficiency of the proposed methods, first we have used binary data which are made by random number generator and showed the vast improvements in terms of epoch. Also, we have applied our methods to the binary-valued Monk's data, 4, 5, 6, 7-bit parity checker and real-valued Iris data which are famous benchmark training sets for machine learning.

• Journal of Korean Neurosurgical Society
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• v.30 no.sup1
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• pp.128-132
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• 2001

Aneurysm of the basilar artery trunk are rare and the surgical approach is very difficult because of the complexity of surgical anatomy around the basilar trunk and the vulnerable adjacent neurovascular structures. The development of brain CT and MRI makes the accurate diagnosis and produces the improvement of surgical approaches at the lesion of the skull base. One of the surgical approaches of basilar trunk aneurysms, the retrolabyrinthine presigmoid transtentorial transpetrosal approach to the aneurysm of the basilar trunk has some advantages of minimal retraction of cerebellum and temporal lobe, intact auditory and facial nerve function by the preservation of the vestibulocochlear and facial nerves, a preservation of sigmoid sinus and vein of Labbe and a relatively good operation field. We had a good result with this approach for the patient of basilar trunk aneurysm and reported the case with the review of literatures.