• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.5
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• pp.23-29
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• 2013

Recent advances in printing technology have increased the productivity of the roll-to-roll (R2R) printing process for printed circuitry. In the R2R printed electronics, characteristics of printed and coated layers are one of the most important issues that determine the functional quality of final products. The slot-die technology can coat a large area with high uniformity using low-viscosity materials; determining the process parameters is important to obtain excellent coating qualities. In this study, a viscocapillary model was used to predict qualities of coated layers and patterns. On the basis of analysis results, a novel meta model was derived using design of experiment methodology to improve accuracy. Sensitivity analysis was performed to define major parameters in R2R slot-die coating process. The coating speed was found to most significantly affect the coated layer thickness and was easily controlled. The performance of the proposed model is verified through experimental studies. Based on the statistical analysis results, R2R slot die process can be optimized to guarantee a desired thickness.

• Membrane and Water Treatment
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• v.8 no.4
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• pp.323-336
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• 2017

The main challenge in many applications of acetic acid is acid dehydration and its recovery from wastewater streams. Therefore, the performance of polyamide thin film composite is evaluated to separate acetic acid from water. To reach this goal, the formation of polyamide layer on polysulfone support membrane was investigated via interfacial polymerization (IP) of meta-phenylenediamine (MPD) in water with trimesoyl chloride (TMC) in hexane. Also, the effect of synthesis conditions, such as concentration of monomers and curing temperature on separation of acetic acid from water were investigated by reverse osmosis process. Moreover, the separation mechanism was discussed. The solute permeation was carried out under applied pressure of 5 bar at $25^{\circ}C$. Surface properties of TFC membrane were characterized by ATR-FTIR, SEM and AFM. The performance test indicated that 3.5 wt% of MPD, 0.35 wt% of TMC and curing temperature of $75^{\circ}C$ are the optimum conditions. Moreover, the permeate flux was $4.3{\frac{L}{m^2\;h}}$ and acetic acid rejection was about 43% at these conditions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.83-89
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• 2016

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.235.1-235.1
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• 2015

Artificially-engineered materials, whose electromagnetic properties are not available in nature, such as negative reflective index, are called metamaterials (MMs). Although many scientists have investigated MMs for negative-reflective-index properties at the beginning, their interests have been extended to many other fields comprising perfect lenses. Among various kinds of MMs, metamaterial absorbers (MM-As) mimic the blackbody through minimizing transmission and reflection. In order to maximize absorption, the real and the imaginary parts of the permittivity and permeability of MM-As should be adjusted to possess the same impedance as that of free space. We propose a dual-wide-band and polarization-independent MM-A. It is basically a triple-layer structure made of metal/dielectric multilayered truncated cones. The multilayered truncated cones are periodically arranged and play a role of meta-atoms. We realize not only a wide-band absorption, which utilizes the fundamental magnetic resonances, but also another wide-band absorption in the high-frequency range based on the third-harmonic resonances, in both simulation and experiment. In simulation, the absorption bands with absorption higher than 90% are 3.93 - 6.05 GHz and 11.64 - 14.55 GHz, while the experimental absorption bands are in 3.88 - 6.08 GHz and 9.95 - 13.84 GHz. The physical origins of these absorption bands are elucidated. Additionally, it is also polarization-independent because of its circularly symmetric structures. Our design is scalable to smaller size for the infrared and the visible ranges.

• Journal of Sensor Science and Technology
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• v.12 no.6
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• pp.289-297
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• 2003

In this paper, we propose accurate classification method of an EEG signals during a mental tasks. In the experimental task, subjects achieved through the process of responding to visual stimulus, understanding the given problem, controlling hand motions, and select a key. To recognize the subjects' selection time, we analyzed with 4 types feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, $\theta$, $\gamma$ waves. From the analysed features, we construct specific rules for each subject meta rules including common factors in all subjects. In this system, the architecture of the neural network is a three layered feedforward networks with one hidden layer which implements the error back propagation learning algorithm. Applying the algorithms to 4 subjects show 87% classification success rates. In this paper, the proposed detection method can be a basic technology for brain-computer-interface by combining with discrimination methods.

• Journal of Powder Materials
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• v.12 no.3
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• pp.186-191
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• 2005

A formation of aluminum hydroxide by hydrolysis of nano and micro aluminum powder has been studied. The nano aluminum powder of 80 to 100 nm in diameter was fabricated by a pulsed wire evaporation (PWE) method. The micro powder was commercial product with more than $10\;{\mu}m$ in diameter. The hydroxide type and morphology depending on size of the aluminum powder were examined by several analyses such as XRD, TEM, and BET. The hydrolysis procedure of micro aluminum powder was different from that of nano aluminum powder. The nano aluminum powder after immersing in the water was transformed rapidly to a nano fibrous boehmite, accompanying with a remarkable temperature increase, and then further transformed slowly to a stable bayerite. However, the micro powder was changed to the stable bayerite slowly and directly. The formation of fibrous aluminum hydroxide from nano aluminum powder might be due to the fine cracks which were formed by hydrogen gas pressure on the surface hydroxide layer during hydrolysis. The nano powder with large specific surface area and small size reacted more actively and faster than the micro powder, and transformed to meta-stable hydroxide in relatively short reaction time. Therefore, the formation of fibrous boehmite is special characteristic of hydrolysis of nano aluminum powder.

• Archives of Pharmacal Research
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• v.27 no.2
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• pp.217-224
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• 2004

Previously, we reported that water-extracted Acanthopanax senticasus exhibited anti-meta-static activity by stimulating the immune system. In this study, we fractionated glycoproteins (EN-SP) from the soluble protein layer (GF-AS) of A. senticasus and determined their basic chemical properties. We also investigated the anti-tumor and immunostimulating activities of the fractionated glycoprotein, EN-SP. We found that intravenous (i.v.) administration of GF-AS dramatically inhibited metastasis of colon26-M3.1 carcinoma cells to the lung in a dose-dependent manner. In vitro analysis showed GF-AS to enhance the proliferation of splenocytes. GF-AS also stimulated peritoneal macrophage, which was followed by the production of various cytokines such as IL-1$\beta$, TNF-$\alpha$, IL-12 and IFN-${\gamma}$. Furthermore, the production of these cytokines was partially blocked when peritoneal macrophage was cultured with the polyclonal antibodies against GF-AS. The depletion of NK cells by rabbit anti-asialo GM1 serum partly abolished the inhibitory effect of GF-AS on lung metastasis of colon26-M3.1 cells. Using gel filtration, EN-SP, an active glycoprotein fraction, is isolated from GF-AS. While both GF-AS and EN-SP stimulated the proliferatation of splenocytes of normal mice, EN-SP showed higher anti-metastatic activity and more potently stimulated the proliferation of splenocytes compared to GF-AS. These results suggest the use of EN-SP, the fractionated glycoprotein from A. senticasus, can be used as a therapeutical reagent to prevent or inhibit tumor metastasis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.342-345
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• 2014

Network intrusion detection system based on machine learning methods is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features from generally used features to detect network intrusion requires extensive computing resources. For instance, the number of possible feature combinations from given n features is $2^n-1$. In this paper, to tackle this problem we propose a optimal feature selection algorithm. Proposed algorithm is based on the local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In addition, the accuracy of clusters which obtained using selected feature components and k-means clustering algorithm is adopted to evaluate a feature assembly. In order to estimate the performance of our proposed algorithm, comparing with a method where all features are used on NSL-KDD data set and multi-layer perceptron.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.15-28
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• 2022

Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.