• Korean Journal of Ecology and Environment
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• v.45 no.2
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• pp.210-217
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• 2012

This study investigated the water quality of effluents from the wastewater treatment plants, located at the Gumi Complex 4, Gumi, and Wonpyong, in Gumi. DOC accounted for higher than 70% of TOC, and oxidation efficiencies, calculated from carbon, were 13~43% for BOD and 37~73% for CODMn, respectively. Based on the biological decomposition experiments, R-DOC account for higher than 70% of DOC, mostly being occupied by refractory organic matters. This indicated that the biodegradable organics occupied more proportions of organic loadings than the refractory organics. The effect of the organics from a discharge of a sewage treatment plant on rivers, Gumi industrial Complex 4, Gumi, and Wonpyong on lower streams of the Nakdong River were found to be 15%, 6% and 16% respectively. The ratio of 15% suggests that comparatively, no large portions of TOC loadings are occupied, but the problem is that the biodegradable organic matters occupy a lot more proportions than that of the refractory organic matters. Thus, it is highly estimated that the refractory organics can gradually increase the pollution level of organics and precursors of disinfection by-products to the down-stream water treatment plants.

• Journal of the Korean Society for Marine Environment & Energy
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• v.12 no.3
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• pp.143-150
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• 2009

Fuel oil mostly used for a ship is made from crude oil by refining process. In order to produce plenty of high-quality fuel oil, the Fluid catalytic cracking(FCC) method is widely adopted to many refinery factories during the decomposition process from high molecule into lower molecule. The major constituents in spent FCC catalysts are Si, Al, Fe, Ti, alkali metals and some others. The spent catalyst is also composed small amounts of rare metals such as Ce, Nd, Ni and V. The big problem in FCC oil is mixing the catalyst in the oil. This reason is unstable separation of FCC catalyst in separator. Such a FCC catalyst will become a reason of heavy wear down in moving parts of engine. The impurity in oil is ash and deposit compound, such as Al, Si, Ni, Fe and V, which will accelerate the wear down on fuel pump, fuel injection valve cylinder liner and piston ring. It is important to find a basic reason of an engine trouble for preventing similar troubles anymore. Insurance compensation will be different according to the reason of an engine trouble which might be natural abrasion or other external causes. In this study, types and reasons of engine troubles related to fuel oil will be covered.

• Fire Science and Engineering
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• v.24 no.2
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• pp.106-113
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• 2010

Fire characteristics can be analyzed more realistically by using more accurate material properties related to the fire dynamics and one way to acquire these fire properties is to use one of the inverse property analyses. In this study the genetic algorithm which is frequently applied for the inverse heat transfer problems is selected to demonstrate the procedure of obtaining fire properties of the solid charring material with relatively simple chemical structure. The thermal decomposition on the surface of the test plate is occurred by receiving the radiative energy from external heat sources, and in this process the heat transfer through the test plate can be simplified by an unsteady 1-D problem. The inverse property analysis based on the genetic algorithm is then applied for the estimation of the properties related to the reaction pyrolysis. The input parameters for the analysis are the surface temperature and mass loss rate of the char plate which are determined from the unsteady 1-D analysis with a givenset of 8 properties. The estimated properties using the inverse analysis based on the genetic algorithm show acceptable agreements with the input properties used to obtain the surface temperature and mass loss rate with errors between 1.8% for the specific heat of the virgin material and 151% for the specific heat of the charred material.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.789-793
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• 2021

A lithium secondary battery is the most promising candidate for future energy storage devices. On the other hand, the battery capacity decreases gradually due to the small amount of water and decomposition of the salts during the charging and discharging process, which deteriorates at high temperatures. Many researchers focused on increasing the cycling performance, but there have been few studies on the fundamental problem that removes water and HF molecules. In this study, silane molecules that are capable of absorbing water and HF molecules are introduced to the separator. Firstly, silica-coated amino-silane (APTES, 3-aminopropyltriethoxysilane) was synthesized, then the silica reacted with epoxy-silane, GPTMS ((3-glycidyloxypropyl)trimethoxysilane). A ceramic-coated separator was fabricated using the silane-coated silica, which is coated on porous polyethylene substrates. FT-IR spectroscopy and TEM analysis were performed to examine the chemical composition and the shape of the silane-coated silica. SEM was performed to confirm the ceramic layers. LMO half cells were fabricated to evaluate the cycling performance at 60 ℃. The cells equipped with a GPTMS-silica separator showed stable cycling performance, suggesting that it would be a solution for improving the cycling performance of the Li-ion batteries at high temperatures.

• Journal of the Korean Electrochemical Society
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• v.26 no.1
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• pp.11-18
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• 2023

As the use of lithium-ion secondary batteries is rapidly increasing due to the rapid growth of the electric vehicle market, the disposal and recycling of spent batteries after use has been raised as a serious problem. Since stored energy must be removed in order to recycle the spent batteries, an effective discharging process is required. In this study, graphite and NCM622 were used as active materials to manufacture coin-type half cells and full cells, and the electrochemical behavior occurring during overdischarge was analyzed. When the positive and negative electrodes are overdischarged respectively using a half-cell, a conversion reaction in which transition metal oxide is reduced to metal occurs first in the positive electrode, and a side reaction in which Cu, the current collector, is corroded following decomposition of the SEI film occurs in the negative electrode. In addition, a side reaction during overdischarge is difficult to occur because a large polarization at the initial stage is required. When the full cell is overdischarged, the cell reaches 0 V and the overdischarge ends with almost no side reaction due to this large polarization. However, if the full cell whose capacity is degraded due to the cycle is overdischarged, corrosion of the Cu current collector occurs in the negative electrode. Therefore, cycled cell requires an appropriate treatment process because its electrochemical behavior during overdischarge is different from that of a fresh cell.

• Korean Journal of Social Welfare Studies
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• v.40 no.1
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• pp.235-260
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• 2009

This study aims to analyze consumption inequality of Korean elderly households. The justification for analyzing consumption inequality during old age could be summarized as follows. First, due to the rapid growth of elderly population, the intra generational inequality of older people will bring greater consequences to the society in the coming years. Second, inequality is more actualized during old age when income stops playing a major role and the everyday lives are based mostly on consumption activities. For analysis, this study used the 2nd, 5th, 7th and 9th wave of 『Korea Labor and Income Panel Study』. The findings are as follows. First, total consumption inequality of elderly households is gradually decreasing after the economic crisis. Also, the gini coefficient of consumption items representing modern consumption culture, such as expenditures on eating out and car maintenance is decreasing. However, the inequality contribution rate of such items is continually rising, indicating that whereas the elderly households in general are being assimilated to the mainstream consumption culture, the disparity between classes is continually expanding. Second, gini coefficient and inequality contribution rate of the essentials such as food and housing has decreased indicating that basic livelihoods in general has risen. Third, the inequality of education expenditure is increasing after the year 2000 which implies that the problem of education inequality in general might have an effect on elderly households.

• Journal of the Korea Organic Resources Recycling Association
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• v.17 no.2
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• pp.81-92
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• 2009

This study investigated the occurrence of residues derived animals produced during the process of slaughter and the subsequent channels of processing those residues derived animals; the status of recycling of these residues derived animals by recycling business is investigated to utilize the results as the basic data for management purposes. At present, animal slaughter is highly specialized and the residues derived animals obtained from the slaughter of animals are separated and dissected into different parts to serve as fodder and residual compost. Some of the residues derived animals obtained from slaughter, which are edible are utilized for edible purposes and those parts which are not edible such as horns, claws, fats etc. are confirmed to be recycled. Poultry residues derived animals are mostly recycled as single-component feed, used as original forms, residual compost whereas fish remains are recycled mostly as singlecomponent feed etc. Most of the companies that recycle residues derived animals are situated in provinces such as Jeollanamdo, Jeollabukdo, Gyeongsangnamdo, Gyeongsangbukdo, where many of the slaughterhouses are located. And many of these recylcing business find themselves in the vicinity of these slaughterhouse. Majority of these slaughterhouse are capable of processing residues derived animals in the range of 10~60 ton/day, which is quite small in terms of processing capacity. The problem encountered in the recycling of the residues derived animals is the occurrence of foul smell caused by the decomposition, for which appropriate measures have to be taken. The residues derived animals are on many occasions directly collected and transported to save costs and secure required amount of residues derived animals.

• The Korean Journal of Nuclear Medicine
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• v.36 no.3
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• pp.195-202
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• 2002

Purpose: For clinical application of beta-emitter labeled antibody, high specific activity is imporiant. Carrier-free $^{188}Re$ from $^{188}W/^{188}Re$ generator is an ideal radionuclide for this purpose. However, low stability of $^{188}Re$ labeled antibody, especially in high specific activity, due to radiolytic decomposition by high energy (2.1 MeV) beta ray was problem. We studied the stability of $^{188}Re$ labeled antibody, and stabilizing effect of several stabilizers. Materials and Methods: Pre-reduced monoclonal antibody (CEA79.4) was labeled with $^{188}Re$ by incubating with generator-eluted $^{188}Re-perrhenate$ in the presence of stannous tartrate for 2 hr at room temperature. Radiochemical purity of each preparation was determined by chromatography. Human serum albumin was added to the labeled antibodies (2%). Stability of $^{188}Re-CEA79.4$ was investigated in the presence of ascorbic acid, ethanol, of Tween 80 as stabilizing agents. Results: Labeling efficiencies were $88{\pm}4%\;(n=12)$. Specific activities of $1.25{\sim}4.77MBq/{\mu}g$ were obtained. If stored after purging with $N_2$, all the preparations were stable for 10 hr. However, stability decreased in the presence of air. Perrhenate and $^{188}Re-tartrate$ was major impurity in declined preparation. colloid-formation was not a significant problem in all cases. Addition of ascorbic acid stabilized the labeled antibodies either under $N_2$ or under air by reducing the formation of perrhenate. Conclusion: High specific activity $^{188}Re$ labeled antibody is unstable, especially, in the presence of oxygen. Addition of ascorbic acid increased the stability.

• 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.