• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Korean Journal of Microbiology
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• v.51 no.3
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• pp.271-279
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• 2015

Lactoferrin (LF) is a multifunctional, iron-binding glycoprotein found in physiological secretions of mammals. LF shows antibacterial, antiviral and antifungal activities. In the present study, a gene encoding the N-terminal lobe of human lactoferrin (hLF) was isolated, cloned and expressed in methylotrophic yeast, Pichia pastoris. The recombinant hLF-N (rhLF-N) protein was secreted into the culture medium at the level of $458{\mu}g/ml$ in 3 L fermentor. The size of purified hLF-N was estimated as 35 kDa when analyzed by SDS-PAGE and western blotting. The rhLF-N was further confirmed by immunodiffusion using the anti-hLF polyclonal antibody. The expression profile analysis by qRT-PCR showed that the relative mRNA expression of rhLF-N was maximal after 2-3 days of methanol induction and reduced gradually at 4 days. The purified rhLF-N showed broad antibacterial activities against the pathogens such as Staphylococcus aureus, E. coli, Pseudomonas aeruginosa, Burkholderia cepacia, and Salmonella typhimurium. However, rhLF-N showed relatively lower activity when compared to peptides derived from LF. In spite of this weak activity, the rhLF-N expressed in P. pastoris might be more advantageous for the industrial application, because rhLF-N is secreted into the culture medium and the production can also be increased by optimization of culture conditions.

• Journal of the Microelectronics and Packaging Society
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• v.21 no.2
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• pp.43-51
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• 2014

In 3-dimensional (3D) integrated package, thermal management is one of the critical issues due to the high heat flux generated by stacked multi-functional chips in miniature packages. In this study, we used numerical simulation method to analyze the thermal behaviors, and investigated the thermal issues of 3D package using TSV (through-silicon-via) technology for mobile application. The 3D integrated package consists of up to 8 TSV memory chips and one logic chip with a interposer which has regularly embedded TSVs. Thermal performances and characteristics of glass and silicon interposers were compared. Thermal characteristics of logic and memory chips are also investigated. The effects of numbers of the stacked chip, size of the interposer and TSV via on the thermal behavior of 3D package were investigated. Numerical analysis of the junction temperature, thermal resistance, and heat flux for 3D TSV package was performed under normal operating and high performance operation conditions, respectively. Based on the simulation results, we proposed an effective integration scheme of the memory and logic chips to minimize the temperature rise of the package. The results will be useful of design optimization and provide a thermal design guideline for reliable and high performance 3D TSV package.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.3
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• pp.437-447
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• 2017

In current, there has not been the evaluation model for the optimum design of the multi-utility tunnel by considering urban type and size, the function of surrounding road and feasibility analyses with respect to construction method, and arrangement of accommodation facilities inside multi-utility tunnel. Thus, in this study, we developed the evaluation model for the optimum design of the multi-utility tunnel before and after the decision of the multi-utility tunnel installation. In this paper, we have selected the Deming cycle which is used in various fields among several decision methods for optimizing the design. For the purpose of reflecting the various factors in the design of the multi-utility tunnel, 11 higher indicators were set up to lead to more detailed approaches. In addition, based on the "Plan-Do-Check-Action (PDCA)" circulation method, we can realize the installation of the multi-utility tunnel and design more efficiently through the first phase for conception and the second phase for optimization, and develop the program for the evaluation model accordingly.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.610-618
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• 2008

It is not easy to compare the treatment processes and find an optimum operating condition by the experiments due to influent conditions, treatment processes, various operational conditions and complex factors in real wastewater treatment system and also need a lot of time and costs. In this paper, the activated sludge models are applied to four principal biological wastewater treatment processes, $A_2O$(anaerobic/anoxic/oxic process), Bardenpho(4 steps), VIP(Virginia Initiative Plant) and UCT(University of Cape Town), and are used to compare their environmental and economic assessment for four key processes. In order to evaluate each processes, a new assessment index which can compare the efficiency of treatment performances in various processes is proposed, which considers both environmental and economic cost. It shows that the proposed index can be used to select the optimum processes among the candidate treatment processes as well as to find the optimum condition in each process. And it can find the change of economic and environmental index under the changes of influent flowrate and aerobic reaction size and predict the optimum index under various operation conditions.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.1
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• pp.117-117
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• 1992

Pubilc utility industries including the electric utility industry are facing a new stream of privatization com-petition with the private sector and deregulation. The necewssity to solve now and in the future power supply and demand problems has been increasing through the sophisticated generation expansion plan(GEP) approach con-sidering not only KEPCo's supply-side resources but also outside resources such as non-utility generation(NUG) demand-side management (DSM). Under the environmental situation in the current electric utility industry a new approach is needed to acquire multiple resources competitively. This study presents the development of a modified electric generation expansion analysis system(EGEAS) model with avoided cost based on the existing EGEAS model which is a dynamic program to develope an optimal generation expansion plan for the electric utility. We are trying to find optimal GEP in Korea's case using our modified model and observe the difference for the level of reliabilities such as the reserve margin(RM) loss of load probability(LOLP) and expected unserved energy percent(EUEP) between the existing EGEAS model and our model. In addition we are trying to calculate avoided cost for NUG resources which is a criterion to evaluate herem and test possibility of connection calculation of avoided cost with GEP implementation using our modified model. The results of our case study are as follows. First we were able to find that the generation expansion plan and reliability measures were largely influenced by capacity size and loading status of NUG resources, Second we were able to find that avoided cost which are criteria to evaluate NUG resources could be calculated by using our modified EGEAS model with avoided cost. We also note that avoided costs were calculated by our model in connection with generation expansion plans.

• Membrane Journal
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• v.27 no.6
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• pp.487-498
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• 2017

In this study, pervaporation performances of water/methanol and water/butanol mixture were evaluated using zeolite 4A membranes manufacutred by FINETECH by experimental works and numerical modeling. Permeation and separation characteristics, such as flux and separation factor, were analyzed by gas chromatography (TCD) and liquid nitrogen traps. Experiments have shown that water is selectively separated from a mixture of water and methanol (separation factor up to approximately 250) and water and butanol (separation factor up to approximately 1,500). Generalized Maxwell Stefan (GMS) theory was implemented to predict pervaporation behaviors of water/alcohol mixtures and diffusional coefficients of zeolite layer were obtained through parameter estimation using $MATLAB^{(R)}$ optimization toolbox. Since the pore size of zeolite 4A are much larger than kinetic diameter of water molecules and smaller than those of methanol and butanol, zeolite 4A membranes can be applied to in situ water removal process such as membrane reactors or hybrid reaction-dehydration process.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.33 no.2
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• pp.54-60
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• 2023

In this study, microstructure and basic property analysis of DG (Desulfurization gypsum) and CCMs (Carbondioxide conversion capture materials) made by reacting CO₂ with DG were conducted to analyze applicability as a cement admixture. The main crystalline phases of DG were CaO and CaSO₄, and CCMs were CaSO₄, CaCO₃, Ca(OH)₂ and CaSO₄·H₂O. As a result of particle size analysis, the difference in average particle sizes between the two materials was about 7 ㎛. No major heavy metals were detected in the CCMs, and as a result o f TGA, the CO₂ decomposition of CCMs was more than twice as high as that of DG. Therefore, it was judged that CCMs could be used as a cement admixture through optimization of manufacturing conditions. As a results of measuring the strength behavior of DG and CCMs mixture ratios, the long-term strength of CCMs-mixed mortar was higher, and this is due to the filler effect of CaCO₃ in CCMs.

• Clean Technology
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• v.24 no.3
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• pp.239-248
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• 2018

In this study, pervaporation experiments of water, ethanol and IPA (Isopropyl alcohol) single components and water/ethanol, water/IPA mixtures were carried out using zeolite 4A membranes developed by Fine Tech Co. Ltd. Those membranes were fabricated by hydrothermal synthesis (growth in hydrothermal condition) after uniformly dispersing the zeolite seeds on the tubular alumina supports. They have a pore size of about $4{\AA}$ by ion exchange of $Na^+$ to the LTA structure with Si/Al ratio of 1.0, and shows strong hydrophilic property. Physical characteristics of prepared membranes were evaluated by using SEM (surface morphology), porosimetry (macro- or meso- pore analysis), BET (micropore analysis), and load tester (compressive strength). Pervaporation experiments with various temperature and concentration conditions confirmed that the zeolite 4A membrane can selectively separate water from ethanol and IPA. Water/ethanol separation factor was over 3,000 and water/IPA separation factor was over 1,500 (50 : 50 wt%, initial feed concentration). Pervaporation behaviors of single components and binary mixtures were predicted using ACM (activity coefficient model), GMS (generalized Maxwell Stefan) model and DGM (Dusty Gas Model). The adsorption and diffusion coefficients of the zeolite top layer were obtained by parameter estimation using GA (Genetic Algorithm, stochastic optimization method). All the calculations were carried out using MATLAB 2018a version.