• Applied Chemistry for Engineering
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• v.17 no.5
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• pp.527-531
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• 2006

We have prepared organophilic inorganic particles and polyimide (PI) nanocomposite having excellent thermal stability and high dielectric constant that can be used for electronic application such as capacitor. We have chosen barium titanate (BT), a high dielectric constantmaterial and its surface was coated with nylon 6 to improve the affinity with PI. The FT-IR and TEM studies showed that the organophilic inorganic particle (BTN) has a polymer shell with thickness of 5 nm. We have suggested that it is possible to control the thickness of coating surface and also indicated the relationship between the ratio of inside and outside radius of BTN and the weight fraction of BT. The PI nanocomposite films based on poly(amic acid) and BTN were prepared by cyclodehydration reaction. The homogeneous dispersion of BTN in PI matrix was identified by using SEM. We have investigated the effect of BTN content on the coefficient of thermal stability, integral procedural decomposition temperature (IPDT), and dielectric constant of PI nanocomposite films.

• Journal of the Korean Society of Marine Environment & Safety
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• v.17 no.2
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• pp.105-121
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• 2011

The operation of tidal power facility may induce severe changes of water quality in Shihwa Lake. Current water quality data are quite important to water quality management policy of Shihwa Lake. Thus, the water quality data of Shihwa Lake and its adjacent sea in 2010 were presented to characterize the temporal and spatial changes of water parameters such as pH, SS, DO, COD, dissolved nutrients, chlorophyll-a, TN and TP. Highest levels of water quality parameters were observed near the Shihwa and Banweol industrial complexes and the levels of water quality parameters were on a decreasing trend to those near the water gate. It suggests that the horizontal distributions of water quality levels are mainly controlled by the supply of fresh water from streams and the inflow of outer seawater by operation of water gate. Although the higher concentrations of TN and TP were observed in the location being affected by Sorae port, the levels of water quality parameters in outer sea of Shihwa Lake were lower than those in Lake. In summer season, hypoxic condition was well developed in bottom water by strong stratification and active decomposition of organic matter. Thus, the vertical distributions of dissolved nutrient, TN and TP concentrations showed the concentrations to be higher in bottom seawater than those in surface seawater whereas the vertical distributions of chlorophyll-a, COD and POC concentrations showed the concentrations to be higher in surface seawater than those in bottom water. Results of Pearson's correlation matrix for surface seawater demonstrated that salinity showed negatively good correlation with not only dissolved nutrients except for ammonium but chlorophyll-a, COD and POC This result indicates that the supply of dissolved nutrients through several streams might significantly affect phytoplankton bloom and increase of COD concentration in surface seawater.

• Membrane Journal
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• v.19 no.4
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• pp.341-347
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• 2009

Nanohybrid based on environmentally friendly and biodegradable polymer, poly propylene carbonate (PPC) and cloisite 20B (PPC/C-20B) have been synthesized by solution blending method and their morphology, thermal and water absorption properties have been evaluated. The structure of PPC/C-20B nanohybrid was confirmed by X-ray diffraction (XRD). The thermal property of PPC and PPC/C-20B nanohybrid were investigated by thermal gravimetric analysis (TGA) and differential scanning calorimetric (DSC). The experimental results demonstrated that nanohybrid showed the highest thermal stability in TGA and DSC. TGA tests revealed that the thermal decomposition temperature ($T_{d50%}$) of the nanohybrid increased significantly, being $23^{\circ}C$ higher than that of pure PPC while DSC measurements indicated that the introduction of 5 mass% of clay increased the glass transition temperature from 21 to $30^{\circ}C$. Further the water absorption capacity of the PPC was significantly decreased by the incorporation of clay. Water absorption cause degradation of the coating by the moistures and affect the physical and mechanical performance. This result indicates that organic modifiers have effect on thermal and water absorption capacity of PPC and are of importance for the practical process and application of PPC.

• Journal of Biomedical Engineering Research
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• v.27 no.5
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• pp.203-209
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• 2006

The aim of this study is to investigate the feasibility of ex vivo MR diffusion tensor imaging technique in order to observe the diffusion-contrast characteristics of human gastric tissues. On normal and pathologic gastric tissues, which have been fixed in a polycarbonate plastic tube filled with 10% formalin solution, laboratory made 3D diffusion tensor Turbo FLASH pulse sequence was used to obtain high resolution MR images with voxel size of $0.5{\times}0.5{\times}0.5mm^3\;using\;64{\times}32{\times}32mm^3$ field of view in conjunction with an acquisition matrix of $128{\times}64{\times}64$. Diffusion weighted- gradient pulses were employed with b values of 0 and $600s/mm^2$ in 6 orientations. The sequence was implemented on a clinical 3.0-T MRI scanner(Siemens, Erlangen, Germany) with a home-made quadrature-typed birdcage Tx/Rx rf coil for small specimen. Diffusion tensor values in each pixel were calculated using linear algebra and singular value decomposition(SVD) algorithm. Apparent diffusion coefficient(ADC) and fractional anisotropy(FA) map were also obtained from diffusion tensor data to compare pixel intensities between normal and abnormal gastric tissues. The processing software was developed by authors using Visual C++(Microsoft, WA, U.S.A.) and mathematical/statistical library of GNUwin32(Free Software Foundation). This study shows that 3D diffusion tensor Turbo FLASH sequence is useful to resolve fine micro-structures of gastric tissue and both ADC and FA values in normal gastric tissue are higher than those in abnormal tissue. Authors expect that this study also represents another possibility of gastric carcinoma detection by visualizing diffusion characteristics of proton spins in the gastric tissues.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.10a
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• pp.111-124
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• 2003

Processes that cause immobilization of contaminants in soil are of great environmental importance because they may lead to a considerable reduction in the bioavailability of contaminants and they may restrict their leaching into groundwater. Previous investigations demonstrated that pollutants can be bound to soil constituents by either chemical or physical interactions. From an environmental point of view, chemical interactions are preferred, because they frequently lead to the formation of strong covalent bonds that are difficult to disrupt by microbial activity or chemical treatments. Humic substances resulting from lignin decomposition appear to be the major binding ligands involved in the incorporation of contaminants into the soil matrix through stable chemical linkages. Chemical bonds may be formed through oxidative coupling reactions catalyzed either biologically by polyphenol oxidases and peroxidases, or abiotically by certain clays and metal oxides. These naturally occurring processes are believed to result in the detoxification of contaminants. While indigenous enzymes are usually not likely to provide satisfactory decontamination of polluted sites, amending soil with enzymes derived from specific microbial cultures or plant materials may enhance incorporation processes. The catalytic effect of enzymes was evaluated by determining the extent of contaminants binding to humic material, and - whenever possible - by structural analyses of the resulting complexes. Previous research on xenobiotic immobilization was mostly based on the application of $^{14}$ C-labeled contaminants and radiocounting. Several recent studies demonstrated, however, that the evaluation of binding can be better achieved by applying $^{13}$ C-, $^{15}$ N- or $^{19}$ F-labeled xenobiotics in combination with $^{13}$ C-, $^{15}$ N- or $^{19}$ F-NMR spectroscopy. The rationale behind the NMR approach was that any binding-related modification in the initial arrangement of the labeled atoms automatically induced changes in the position of the corresponding signals in the NMR spectra. The delocalization of the signals exhibited a high degree of specificity, indicating whether or not covalent binding had occurred and, if so, what type of covalent bond had been formed. The results obtained confirmed the view that binding of contaminants to soil organic matter has important environmental consequences. In particular, now it is more evident than ever that as a result of binding, (a) the amount of contaminants available to interact with the biota is reduced; (b) the complexed products are less toxic than their parent compounds; and (c) groundwater pollution is reduced because of restricted contaminant mobility.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.