• Journal of Sensor Science and Technology
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• v.8 no.1
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• pp.53-62
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• 1999

Micro polysilicon actuators, which are widely used in the field of MEMS (Microelectromechanical System) technology, were fabricated using polysilicon thin layers. Polysilicon deposition were carried out to have symmetrical layer structures with a LPCVD (Low Pressure Chemical Vapor Deposition) system, and we have measured physical characteristics by micro test patterns, such as bridges and cantilevers to verify minimal mechanical stress and stress gradient in the polysilicon layers according to the methods of mutilayer deposition, doping, and thermal treatment, also, analyzed the properties of each specimen, which have a different process condition, by XRD, and SIMS etc.. Finally, the fabricated planar polysilicon resonator, symmetrically stacked to $6.5{\mu}m$ thickness, showed Q of 1270 and oscillation ampitude of $5{\mu}m$ under DC 15V, AC 0.05V, and 1000 mtorr pressure. The developed micro polysilicon resonator can be utilized to micro gyroscope and accelerometer sensor.

• Journal of Sensor Science and Technology
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• v.9 no.2
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• pp.127-133
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• 2000

We propose a novel monitoring system for a recycling piggery slurry treatment system through neural networks. Here we tried to model treatment process for each tank(influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) in the system based on population densities of heterotrophic and lactic acid bacteria. Principle component analysis(PCA) was first applied to identify a relation between input(microbial densities and parameters for the treatment) and output, and then multilayer neural networks were employed to model the treatment process for each tank. PCA filtration of input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of input. Neural networks independently trained for each treatment tank and their subsequent combinatorial data analysis allowed a successful prediction of the treatment system for at least two days.

• Asian Journal of Atmospheric Environment
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• v.10 no.2
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• pp.67-79
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• 2016

The forecasting of air pollution is an important and popular topic in environmental engineering. Due to health impacts caused by unacceptable particulate matter (PM) levels, it has become one of the greatest concerns in metropolitan cities like Karaj City in Iran. In this study, the concentration of $PM_{2.5}$ was predicted by applying a multilayer percepteron (MLP) neural network, a radial basis function (RBF) neural network and a Markov chain model. Two months of hourly data including temperature, NO, $NO_2$, $NO_x$, CO, $SO_2$ and $PM_{10}$ were used as inputs to the artificial neural networks. From 1,488 data, 1,300 of data was used to train the models and the rest of the data were applied to test the models. The results of using artificial neural networks indicated that the models performed well in predicting $PM_{2.5}$ concentrations. The application of a Markov chain described the probable occurrences of unhealthy hours. The MLP neural network with two hidden layers including 19 neurons in the first layer and 16 neurons in the second layer provided the best results. The coefficient of determination ($R^2$), Index of Agreement (IA) and Efficiency (E) between the observed and the predicted data using an MLP neural network were 0.92, 0.93 and 0.981, respectively. In the MLP neural network, the MBE was 0.0546 which indicates the adequacy of the model. In the RBF neural network, increasing the number of neurons to 1,488 caused the RMSE to decline from 7.88 to 0.00 and caused $R^2$ to reach 0.93. In the Markov chain model the absolute error was 0.014 which indicated an acceptable accuracy and precision. We concluded the probability of occurrence state duration and transition of $PM_{2.5}$ pollution is predictable using a Markov chain method.

• Journal of KIISE:Software and Applications
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• v.26 no.12
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• pp.1383-1392
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• 1999

이미 학습된 다층퍼셉트론 신경망을 디지털 VLSI 기술을 사용하여 하드웨어로 구현할 경우 신경망의 가중치 및 뉴런 출력들을 양자화해야 하는 문제가 발생한다. 이러한 신경망 변수들의 양자화는 결과적으로 주어진 입력에 대한 신경망의 최종 출력에서의 왜곡을 초래한다. 본 논문에서는 먼저 이러한 양자화로 인한 신경망 출력에서의 왜곡을 통계적으로 분석하였다. 분석 결과에 의하면 입력패턴 각 성분의 제곱들의 합과 가중치의 크기들이 양자화 영향에 주로 기여하는 것으로 나타났다. 이러한 분석 결과를 이용하여 양자화를 위한 정밀도가 주어졌을 때, 양자화 영향이 최소화된 다층퍼셉트론 신경망을 설계하는 방법을 제시하였다. 그리고 제안된 방법에 의해 얻은 신경망과 오류역전파 학습방법에 의하여 얻은 신경망의 성능을 비교함으로써 제안된 방법의 효율성을 입증하였다. 실험결과는 낮은 양자화 정밀도에서도 제안된 방법이 더 좋은 성능을 보였다.Abstract When we implement a multilayer perceptron with the digital VLSI technology, we generally have to quantize the weights and the neuron outputs. These quantizations eventually cause distortion in the output of the network for a given input. In this paper first we made a statistical analysis about the effect caused by the quantization on the output of the network. The analysis revealed that the sum of the squared input components and the sizes of the weights are the major factors which contribute to the quantization effect. We present a design method for an MLP which minimizes the quantization effect when the precision of the quantization is given. In order to show the effectiveness of the proposed method, we developed a network by our method and compared it with the one developed by the regular backpropagation. We could confirm that the network developed by our method performs better even with a low precision of the quantization.

• Journal of the Korean Society of Groundwater Environment
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• v.6 no.1
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• pp.42-47
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• 1999

This study considers pseudosteady-state flow to a restricted-entry well in a single or multilayer aquifer with crossflow. A simple method for calculating the pseudoskin factor caused by partial penetration is presented to overcome a limited applicability in geometrical or computational aspects of previous methods. The computation is based on the solution of a simplified pseudosteady-state equation that describes the long-time behavior of the closed radial system. We illustrate the applicability of this method to various types of cylindrical systems and provide the results graphically. Comparisons with previously published results have indicated that this method yields highly accurate estimates of pseu-doskin factor with minimum computational effort. This method has also shown to be particularly useful for geometrically-complicated systems. Greatly improved computational efficiency of pseudosteady-state approach permits the engineer to easily account for the effect of partial penetration on the late-time performance of a well.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.45 no.5
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• pp.14-22
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• 2013

In this study, we investigated retention characteristics of nanocoated GCC that was positively modified by Layer-by-Layer (LbL) multilayering process. Three layers were formed onto GCC particles with poly-DADMAC/PSS/poly-DADMAC (PD3) and C-starch/A-PAM/C-starch (CS3) systems, respectively. Untreated GCC, PD3 GCC (strongly positive charge) and CS3 GCC (weekly positive charge) were retained on pulp fibers under single retention system or microparticle retention system conditions. In single retention system, PD3 particles were not affected by cationic retention aid due to their strong positive charge, whereas CS3 particles reacted with cationic retention aid due to anionic sites on the surface of the weekly positive particles. In a microparticle retention system, positively modified GCC (PD3 and CS3) showed higher retention level than untreated GCC at the same dosage of retention aid. The cationic surface of GCC particles were more reacted with bentonite so the deposition onto pulp fibers was improved. In addition, the retention level of nanocoated GCC was increased with maintaining good formation.

• Clean Technology
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• v.17 no.1
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• pp.25-30
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• 2011

This study presents a fabrication method of cell-chip using aqueous solution based surface modification. The applications of cell-chip have potential for fundamental study of genetics, cell biology as well as cancer diagnostics and treatment. Conventional methods for fabrication of cell-chip have been limited in economic loss and environmental pollution because of the use of harsh organic solvent, complex process of silicon technology, and expensive equipment. In order to fabricate cell chip, we have proposed simple and eco-friendly process combined polyelectrolyte multilayer coating with microcontact printing. For the proof of concept, the cell chip can be applied to analyze the different expression of cell surface glycans and derivatives between cancer and normal cells. Our proposed method is useful technique for the application of novel cancer diagnostics and basic medical engineering.

• Proceedings of the Korean Society of Dyers and Finishers Conference
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• 2011.03a
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• pp.61-61
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• 2011

지구 온난화로 인해 환경파괴, 병원성 세균 감염 등에 의한 각종 질병과 아토피 피부염 등 수없이 많은 요소들에서 우리 몸을 보호하기 위하여 친환경 소재의 용품들이 각광 받고 있다. 이중에서도 키토산과 칼슘알지네이트는 천연재료로써 이미 다른 분야에서 응용되어 사용되고 있으며, 이 두 가지 천연재료를 두 층으로 면섬유에 코팅시킨 CCAC섬유를 제조하였다. CCAC섬유와 키토산이 코팅된 면섬유, 칼슘알지네이트가 코팅된 면섬유, 미처리 면섬유의 총 4가지 섬유에 체액, 증류수, 생리식염수의 각각의 조건에서 흡습량, 흡습시간을 측정하여 비교하고, 수분율과 함수율을 측정하고, 접촉각을 Contact angle system OCA20을 이용하여 측정하였다. CCAC섬유의 키토산 부착 함량을 알아보기 위하여 정량적인 방법으로 add-on율을 이용하여 확인하고, 정성적인 방법으로 원소분석기(Elemental Analyzer, FLASH 1112)를 이용하여 측정하였다. 칼슘알지네이트의 함량 분석은 EDS(EX-250, HORIBA, Japan)를 이용하여 측정하고, 직물의 표면과 단면의 형태는 주사전자현미경(S-4100, Hitachi Co., Japan)으로 ${\times}100$, ${\times}1000$ 배율로 측정하여 단면과 표면 상태를 확인하고, 물리적인 특성은 KES-FB system 을 통하여 확인 하였다.

• Membrane Journal
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• v.17 no.3
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• pp.161-173
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• 2007

Membrane filtration processes are increasingly popular for drinking water treatment that requires high quality of water. Low pressure membrane(LPM) processes such as microfiltration(MF) and ultrafiltration(UF), however, are ineffective in the removal of dissolved organic matter and also membrane fouling is still an important issue to be resolved. High pressure membranes(HPMs) may guarantee better water quality, but at the high energy consumption. Thus, various approaches to combine LPM processes with other physicochemical methods have been recently made to achieve their efficiency to the level comparable to that of HPM processes. In this work, therefore, hybrid processes that coupled MF/UF with coagulation, adsorption, chemical reactions(e.g., chelation and oxidation) are reviewed regarding system design and performance and also membrane surface modifications conducted by grafting and polyelectrolyte multilayer formation were assessed.

• The Journal of the Acoustical Society of Korea
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• v.13 no.4
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• pp.60-68
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• 1994

In this paper we propose a new Hybrid learning rule applied to multilayer feedforward neural networks, which is constructed by combining Hebbian learning rule that is a good feature extractor and Back-Propagation(BP) learning rule that is an excellent classifier. Unlike the BP rule used in multi-layer perceptron(MLP), the proposed Hybrid learning rule is used for uptate of all connection weights except for output connection weigths becase the Hebbian learning in output layer does not guarantee learning convergence. To evaluate the performance, the proposed hybrid rule is applied to classifier problems in two dimensional space and shows better performance than the one applied only by the BP rule. In terms of learning speed the proposed rule converges faster than the conventional BP. For example, the learning of the proposed Hybrid can be done in 2/10 of the iterations that are required for BP, while the recognition rate of the proposed Hybrid is improved by about $0.778\%$ at the peak.