• Journal of the Korean GEO-environmental Society
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• v.15 no.4
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• pp.5-11
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• 2014

Recently, there have been various lifeline installations constructed in the underground space of urban area due to the effective use of land. For newly installed lifelines or the management of the installed lifelines, many construction activities of excavation and backfilling are observed. Around these area, there are possibilities of collapse or excessive settlement due to the leaking of the pipe or unsatisfactory compaction of backfill material. Besides, construction costs can be saved since the on-site soils are used. The application of this liquidity filling material is not only to the lifeline installation but also to underpin the foundation under the vibrating machinery. On the evaluation of the applicability of this method to this circumstance, the strength should be investigated against the static load from the machine load as well as the vibration load from the activation of the machine. In this study, the applicability of the liquidity fill material on the foundation under the vibrating machinery is assessed via uniaxial compression and resonant column tests. The liquidity filling material consisting of the on-site soils with loess and kaolinite are tested to investigate the static and dynamic characteristics. Furthermore, the applicability of the reclaimed ash categorized as an industrial waste is evaluated for the recycle of the waste to the construction materials. The experimental results show that the shear modulus and 7 day uniaxial strength of the liquidity filling material mixed with reclaimed ash show higher than those with the on-site soils. However, the damping ratio does not show any tendency on the mixed materials.

• Applied Chemistry for Engineering
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• v.27 no.6
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• pp.583-589
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• 2016

Lubricant additives including zinc dialkyldithiophosphate (ZDDP) containing metal have been widely used due to the advantage of very low cost, but they can generate impurities such as ash. In this work, ZDDP containing metals was partially replaced with bis[3-(dialkyloxyphosphorothionyl) thio-2-methylpropanyloxy] butane (BAP4s) which was synthesized conveniently and effectively from alkanediol without any metal components. Also, the wear resistance property of synthesized BAP4s were studied. Wear scar diameter (WSD) values of BAP4s with butyl, octyl, decyl, dodecyl or tetradecyl groups were also measured by four-ball test. As the length of the alkyl group increased from 4 to 8, the WSD value of BAP4s decreased rapidly from 0.59 to 0.45 mm, but from 8 to 14, the value increased very slowly from 0.45 to 0.50 mm. Thus, among all BAP4s, B8P4 having BAP4 with the octyl group, showed the lowest WSD value. Furthermore, the WSD values were measured in a lubricant base oil mixed with a 0.50 percent concentration (w/w) of either BAP4 or ZDDP. The former was 0.55 mm, and the latter was 0.45 mm. The thermal stability and tribofilm formation peroperty were also measured by thermogravimetric analyzer (TGA) and energy-dispersive X-rays spectroscopy (EDS), respectively.

• Applied Chemistry for Engineering
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• v.23 no.2
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• pp.131-137
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• 2012

Polyaniline (PANI) and polypyrrole (Ppy) hollow sphere structures with controlled shell thicknesses can be easily synthesized than those of using a layer-by-layer method for cathode active material of lithium-ion batteries. Polystyrene (PS) core was synthesized by emulsion polymerization using an anion surfactant. The shell thicknesses of PANI and Ppy were controlled by amounts of aniline and pyrrole monomers. PS was removed by an organic solution. This structure increased in contact with an electrolyte and a specific capacity in lithium-ion batteries. But polymers have disadvantages such as the difficult control of molecular weights and low densities. These disadvantages were completed by controlled shell thicknesses. The amount of aniline monomer increased from 1.2, 2.4, 3.6, 4.8 to 6.0 mL, and the shell thicknesses were 30.2, 38.0, 42.2, 48.2, and 52.4 nm, respectively. And the amount of pyrrole monomer was 0.6, 1.2, 2.4 and 3.6 mL, the shell thicknesses were 16.0, 22.0, 27.0 and 34.0 nm, respectively. In the cathode materials with controlled shell thicknesses, shell thicknesses of the PANI hollow spheres were 30.2, 42.2, and 52.4 nm, and discharge specific capacities of after 10 cycle were ~18, ~29, and ~62 mAh/g, respectively. The shell thicknesses of the Ppy hollow spheres were 16.0, 22.0, 27.0 and 34.0 nm, and discharge specific capacities of after 15 cycle were ~15, ~36, ~56, and ~77 mAh/g, respectively. Thus, shell thicknesses of PANI and Ppy increased, the specific capacities increased.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.278-286
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• 2021

In this study, based on the Computational Fluid Dynamics (CFD) simulation model developed through previous study, inner environmenct of the modified glass greenhouse was predicted. Also, suggested the optimal shape of the greenhouse and location of the heat exchangers for heat energy management of the greenhouse using the developed model. For efficient heating energy management, the glass greenhouse was modified by changing the cross-section design and the location of the heat exchanger. The optimal cross-section design was selected based on the cross-section design standard of Republic of Korea's glass greenhouse, and the Fan Coil Unit(FCU) and the radiating pipe were re-positioned based on "Standard of greenhouse environment design" to enhance energy saving efficiency. The simulation analysis was performed to predict the inner temperature distribution and heat transfer with the modified greenhouse structure using the developed inner environment prediction model. As a result of simulation, the mean temperature and uniformity of the modified greenhouse were 0.65℃, 0.75%p higher than those of the control greenhouse, respectively. Also, the maximum deviation decreased by an average of 0.25℃. And the mean age of air was 18 sec. lower than that of the control greenhouse. It was confirmed that efficient heating energy management was possible in the modified greenhouse, when considered the temperature uniformity and the ventilation performance.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.7
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• pp.820-825
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• 2007

Earthworm casting was the natural fertilizer that contained high concentrations of nutrients such as nitrogen, phosphate and potassium and of over $10^8$ CFU/ml of microorganisms. Greater than 80% of feed was excreted through the fermentation by the intestinal enzyme, after worm had eaten feeds such as fallen leaves and rotten roots under the ground. Also, the soil structure of casting was known to be very efficient in the aspects of the porosity, the water permeability, and deodorizing activities. In this research, the biofilter packed with a biomedia made of casting and waste polyurethane foam, a binder, which helped to improve the durability and perpetuity of casting, was investigated to degrade malodorous hydrogen sulfide gas. The biomedia had no need of extra supply of nutrients and of microbial inoculations. On the beginning of the operations, it showed 100% removal of hydrogen sulfide gas without lag phase. At SV of 50 $h^{-1}$, hydrogen sulfide gas from the outlet of the biofilter was not detected, when inlet concentration increased to 450 ppmv. After that, removal efficiency decreased as increasing inlet hydrogen sulfide concentration. Hydrogen sulfide removal was maintained at almost 93% until inlet concentration was increased up to 950 ppmv, at which the elimination capacity of $H_2S$ was 61.2 g $S{\cdot}m^{-3}{\cdot}h^{-1}$. Maximum elimination capacity guaranteing 90% removal was 61.2, 65.9, 84.7, 89.4 g $S{\cdot}m^{-3}{\cdot}h^{-1}$ at SV ranging from 50 $h^{-1}$ to 300 $h^{-1}$, but was 59.3 g $S{\cdot}m^{-3}{\cdot}h^{-1}$ at SV of 400 $h^{-1}$. The results calculated from Michaelis-Menten equation revealed that $V_m$ increased from 66.04, 88.96, 117.35, 224.15, to 227.54 g $S{\cdot}m^{-3}{\cdot}h^{-1}$ with increasing space velocity in the range of 50 $h^{-1}$ to 400 $h^{-1}$. However, saturation constant$(K_s)$ decreased from 79.97 ppmv to 64.95 and 65.37 ppmv, and then increased to 127.72 and 157.43 ppmv.

• Journal of the Korean Electrochemical Society
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• v.19 no.3
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• pp.80-86
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• 2016

High Ni content layered oxide materials for the positive electrode in lithium-ion batteries have high specific capacity. However, their poor electrochemical and thermal stability at elevated temperature restrict the practical use. A small amount of $Al_2O_3$ was added to the mixture of transition metal hydroxide and lithium hydroxide. The $LiNi_{0.6}Co_{0.2}Mn_{0.2}O_2$ was simultaneously doped and coated with $Al_2O_3$ during heat-treatment. Electrochemical characteristics of modified $LiNi_{0.6}Co_{0.2}Mn_{0.2}O_2$ were evaluated by the galvanostatic cycling and the LSTA(linear sweep thermmametry) at the constant voltage conditions. The nano-sized $Al_2O_3$ added materials show better cycle performance at elevated temperature than that of micro-sized $Al_2O_3$. As the added amount of nano-$Al_2O_3$ increased, the thermal stability of electrode also enhanced, but the use of 2.5 mol% Al showed the best high temperature performance.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.7
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• pp.505-514
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• 2009

Aqueous potassium salt of serine was proposed as an alternative $CO_2$ absorbent to monoethanolamine (MEA) and its $CO_2$ absorption characteristics were studied. The experiment has been conducted using screening test equipment with NDIR type gas analyzer and vapor-liquid equilibrium apparatus. $CO_2$ absorption/desorption rate and net amount of $CO_2$ absorbed in cyclic process are the criteria to assess the $CO_2$ absorption characteristics in this study. Effective $CO_2$ loading of potassium salt of serine and MEA are 0.425 and 0.230 respectively. Cyclic capacities are 0.354 and 0.298 for potassium salt of serine and MEA. The absorption rate of the potassium serinate decreased sharply at $CO_2$ loading is 0.1 and were maintained approximately at half of MEA. To enhance the absorption rate of aqueous potassium salt of serine, small quantities of rate promoters, namely piperazine and tetraethylenepentamine were blended, so that rich $CO_2$ loading were increased by 13.7% and 18.7% respectively. The rich $CO_2$ loading of potassium salt of serine was 29.2% and 35.0% higher than those of aqueous sodium and lithium salt of serine, respectively. The absorption rate of potassium salt of valine and isoleucine which have similar molecular structures to serine were lower than that of serine because of the presence of bulky side group. Precipitation phenomena during $CO_2$ absorption were discussed by the aid of literatures.