• Journal of the Korean Ceramic Society
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• v.42 no.2 s.273
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• pp.104-109
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• 2005

To study the role of PbO as the buffer layer, Pt/PZT/PbO/Si with the MFIS structure was deposited on the p-type (100) Si substrate by the r.f. magnetron sputtering with $Pb_{1.1}Zr_{0.53}Ti_{0.47}O_3$ and PbO targets. When PbO buffer layer was inserted between the PZT thin film and the Si substrate, the crystallization of the PZT thin films was considerably improved and the processing temperature was lowered. From the result of an X-ray Photoelectron Spectroscopy (XPS) depth profile result, we could confirm that the substrate temperature for the layer of PbO affects the chemical states of the interface between the PbO buffer layer and the Si substrate, which results in the inter-diffusion of Pb. The MFIS with the PbO buffer layer show the improved electric properties including the high memory window and low leakage current density. In particular, the maximum value of the memory window is 2.0V under the applied voltage of 9V for the Pt/PZT(200 nm, $400^{\circ}C)/PbO(80 nm)/Si$ structures with the PbO buffer layer deposited at the substrate temperature of $300^{\circ}C$.

• Elastomers and Composites
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• v.37 no.2
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• pp.79-85
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• 2002

Ethyl-branched polyethylene [PE(2)] containing 2mole% ethyl branch and three ethylene-propylene rubbers (EPR's) having the same ethylene(E)-propylene(P) molar ratio(E/P=50/50) with different stereoregularity, that is, random EPR (r-EPR), alternating-EPR (alt-EPR) and isotactic-alternating-EPR (iso-alt-EPR) were mixed for the investigation or their properties depending on the stereoregularity. Crystallinity of the prepared blends decreased with increasing content of amorphous EPR because of a decrease in both the degree of annealing and kinetics of diffusion of the crystallizable polymer content. With blend composition, crystallinity was reduced with the stereoregularity in EPR. The thermodynamic interaction parameter(x) for the three blend systems approximately equals to zero near the melting point. These systems were determined to be miscible on a molecular scale near or above the crystalline melting point or the crystalline PE(2). From the measurement of $T_m$ vs. $T_c$, the behavior of PE(2) is mainly due to a diluent effect of EPR component. The spherulite size measured by small angle light scattering (SALS) technique depended upon blend composition, and stereoregularity of EPR. The size of spherulite was enlarged with the content of rubbery EPR and the decrease of stereoregularity in EPR.

• Journal of the Korean Society for Marine Environment & Energy
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• v.11 no.1
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• pp.13-23
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• 2008

In coastal region, eutrophication, Do deficit and red tide are frequently occurred by influx of fresh water. When the fresh water containing pollutants is discharged into the sea, the surrounding water is contaminated by dispersion of freshwater flowing into coastal waters. The prediction and analysis about the dispersion process of the discharged fresh water should be conducted. A modeling system using GUI was developed to simulate hydrodynamic flow and fresh water dispersion in coastal waters and to analyze the results efficiently. The modeling module of the system includes a tide model using a finite element method and a fresh water dispersion model using a particle-tracking method. This system was applied to predict the tidal currents and fresh water dispersion in Mokpo coastal zone. To verify accuracy of the hydrodynamic model, the simulation results were compared with observed sea level and time variations of tidal currents showing a good agreement. The fresh water dispersion was verified with observed salinity distribution. The dispersion model also was verified with analytic solutions with advection-diffusion problems in 1-dimensional and 2-dimensional simple domain. The system is operated on GUI environment, to ease the model handling such as inputting data and displaying results. Therefore, anyone can use the system conveniently and observe easily and accurately the simulation results by using graphic functions included in the system. This system can be used widely to decrease the environmental disaster induced by inflow of fresh water into coastal waters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2075-2082
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• 2006

This paper presents modeling and optimization techniques for hish efficiency solar cell process on single-crystalline float zone (FZ) wafers. Among a sequence of multiple steps of fabrication, the followings are the most sensitive steps for the contact formation: 1) Emitter formation by diffusion; 2) Anti-reflection-coating (ARC) with silicon nitride using plasma-enhanced chemical vapor deposition (PECVD); 3) Screen-printing for front and back metalization; and 4) Contact formation by firing. In order to increase the performance of solar cells in terms of efficiency, the contact formation process is modeled and optimized using neural networks and genetic algorithms, respectively. This paper utilizes the design of experiments (DOE) in contact formation to reduce process time and fabrication costs. The experiments were designed by using central composite design which consists of 24 factorial design augmented by 8 axial points with three center points. After contact formation process, the efficiency of the fabricated solar cell is modeled using neural networks. Established efficiency model is then used for the analysis of the process characteristics and process optimization for more efficient solar cell fabrication.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.35-44
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• 2022

In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.

• Korean Journal of Radiology
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• v.22 no.1
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• pp.106-117
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• 2021

Objective: To explore the value of magnetic resonance imaging (MRI)-based whole tumor texture analysis in differentiating borderline epithelial ovarian tumors (BEOTs) from FIGO stage I/II malignant epithelial ovarian tumors (MEOTs). Materials and Methods: A total of 88 patients with histopathologically confirmed ovarian epithelial tumors after surgical resection, including 30 BEOT and 58 MEOT patients, were divided into a training group (n = 62) and a test group (n = 26). The clinical and conventional MRI features were retrospectively reviewed. The texture features of tumors, based on T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging, were extracted using MaZda software and the three top weighted texture features were selected by using the Random Forest algorithm. A non-texture logistic regression model in the training group was built to include those clinical and conventional MRI variables with p value < 0.10. Subsequently, a combined model integrating non-texture information and texture features was built for the training group. The model, evaluated using patients in the training group, was then applied to patients in the test group. Finally, receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the models. Results: The combined model showed superior performance in categorizing BEOTs and MEOTs (sensitivity, 92.5%; specificity, 86.4%; accuracy, 90.3%; area under the ROC curve [AUC], 0.962) than the non-texture model (sensitivity, 78.3%; specificity, 84.6%; accuracy, 82.3%; AUC, 0.818). The AUCs were statistically different (p value = 0.038). In the test group, the AUCs, sensitivity, specificity, and accuracy were 0.840, 73.3%, 90.1%, and 80.8% when the non-texture model was used and 0.896, 75.0%, 94.0%, and 88.5% when the combined model was used. Conclusion: MRI-based texture features combined with clinical and conventional MRI features may assist in differentitating between BEOT and FIGO stage I/II MEOT patients.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.