• 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.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.2
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• pp.165-174
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• 2010

The numerical modeling of a coal gasification reaction occurring in an entrained flow coal gasifier is presented in this study. The purposes of this study are to develop a reliable evaluation method of coal gasifier not only for the basic design but also further system operation optimization using a CFD(Computational Fluid Dynamics) method. The coal gasification reaction consists of a series of reaction processes such as water evaporation, coal devolatilization, heterogeneous char reactions, and coal-off gaseous reaction in two-phase, turbulent and radiation participating media. Both numerical and experimental studies are made for the 1.0 ton/day entrained flow coal gasifier installed in the Korea Institute of Energy Research (KIER). The comprehensive computer program in this study is made basically using commercial CFD program by implementing several subroutines necessary for gasification process, which include Eddy-Breakup model together with the harmonic mean approach for turbulent reaction. Further Lagrangian approach in particle trajectory is adopted with the consideration of turbulent effect caused by the non-linearity of drag force, etc. The program developed is successfully evaluated against experimental data such as profiles of temperature and gaseous species concentration together with the cold gas efficiency. Further intensive investigation has been made in terms of the size distribution of pulverized coal particle, the slurry concentration, and the design parameters of gasifier. These parameters considered in this study are compared and evaluated each other through the calculated syngas production rate and cold gas efficiency, appearing to directly affect gasification performance. Considering the complexity of entrained coal gasification, even if the results of this study looks physically reasonable and consistent in parametric study, more efforts of elaborating modeling together with the systematic evaluation against experimental data are necessary for the development of an reliable design tool using CFD method.

• 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 Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.105-113
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• 2016

Fire radiative power(FRP), which means the power radiated from wildfire, is used to estimate fire emissions. Currently, the geostationary satellites of East Asia do not provide official FRP products yet, whereas the American and European geostationary satellites are providing near-real-time FRP products for Europe, Africa and America. This paper describes the first retrieval of Himawari-8 FRP using the mid-infrared radiance method and shows the comparisons with MODIS FRP for Sumatra, Indonesia. Land surface emissivity, an essential parameter for mid-infrared radiance method, was calculated using NDVI(normalized difference vegetation index) and FVC(fraction of vegetation coverage) according to land cover types. Also, the sensor coefficient for Himawari-8(a = 3.11) was derived through optimization experiments. The mean absolute percentage difference was about 20%, which can be interpreted as a favourable performance similar to the validation statistics of the American and European satellites. The retrieval accuracies of Himawari FRP were rarely influenced by land cover types or solar zenith angle, but parts of the pixels showed somewhat low accuracies according to the fire size and viewing zenith angle. This study will contribute to estimation of wildfire emissions and can be a reference for the FRP retrieval of current and forthcoming geostationary satellites in East Asia.

• Journal of the Korean Institute of Gas
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• v.20 no.3
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• pp.46-51
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• 2016

Natural gas liquefaction process which spends a huge amount energy is operated under cryogenic conditions. Thus, many researchers have studied on minimizing energy consumption of LNG plant. However, a few studied for cost optimization have performed. This study focused on the cost analysis for the single mixed refrigerant (SMR) process, one of the simplest natural gas liquefaction process, which has different capacity. The process capacity is increased from 1 million ton per annum (MTPA) to 2.5 MTPA by 0.5 MTPA steps. According to the increase of plant size, only flow rate of natural gas and mixed refrigerant are increased and other operating conditions are fixed. Aspen Economic Evaluator(v.8.7) is used for the cost analysis and six tenths factor rule is applied to obtain multi stream heat exchanger cost data which is not supplied by Aspen Economic Evaluator. Moreover, the optimal plant sizes for different sizes of gas wells are found as the result of applying plant cost to small scale gas wells, 20 million ton (MT), 40 MT, and 80 MT. Through this cost analysis, the foundation is built to optimize LNG plant in terms of the cost.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.31-37
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• 2017

In this paper, we implemented for the development of a high output induction lamp system, which lamp design is optimized by gas type, mixing ratio, pressure and discharge tube size, amalgam type and mixing ratio, and characteristics of ferrite core in the lamp. It's the circuit design by improving the power factor and efficiency according to the driving method, which has analyzing the characteristics according to the waveform and frequency. Finally, luminaries design part for applying the optimal lighting system considering the surrounding environment, the characteristics of the lighting circuit for electrodeless lamp has analyzed and the improvement has been proceeded. In conclusion, the driving frequency has optimized at 135kHz with degrading $7{\sim}10^{\circ}C$ based on the results of the optical characteristics of the induction lamp on peak noise FET(Q3,Q4) damage.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.32-40
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• 2016

IEEE 802.15.4 is the one of the protocols for radio communication in a personal area network. Because of low cost and low power communication for IoT communication, it requires the highest optimization level in the implementation. Recently, the studies of location aware algorithm based on IEEE802.15.4 standard has been achieved. Location estimation is performed basically in equal consideration of reference node information and blind node information. However, an error is not calculated in this algorithm despite the fact that the coordinates of the estimated location of the blind node include an error. In this paper, we enhanced a conventual maximum likelihood estimation using weighted coefficient and implement the hardwired location aware engine for small code size and low power consumption. On the field test using test-beds, the suggested hardware based location awareness method results better accuracy by 10 percents and reduces both calculation and memory access by 30 percents, which improves the systems power consumption.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.33-43
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• 2011

Routing technologies considering QoS-based hypermedia services have been seen as a crucial network property in next generation optical Internet (NGOI) networks based on IP/dense-wavelength division multiplexing (DWDM). The huge potential capacity of one single fiber. which is in Tb/s range, can be exploited by applying DWDM technology which transfers multiple data streams (classified and aggregated IP traffics) on multiple wavelengths (classified with QoS-based) simultaneously. So, DWDM-based optical networks have been a favorable approach for the next generation optical backbone networks. Finding a qualified path meeting the multiple constraints is a multi-constraint optimization problem, which has been proven to be NP-complete and cannot be solved by a simple algorithm. The majority of previous works in DWDM networks has viewed heuristic QoS routing algorithms (as an extension of the current Internet routing paradigm) which are very complex and cause the operational and implementation overheads. This aspect will be more pronounced when the network is unstable or when the size of network is large. In this paper, we propose a flooding-based unicast and multicast QoS routing methodologies(YS-QUR and YS-QMR) which incur much lower message overhead yet yields a good connection establishment success rate. The simulation results demonstrate that the YS-QUR and YS-QMR algorithms are superior to the previous routing algorithms.

• Biomedical Science Letters
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• v.22 no.4
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• pp.174-183
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• 2016

In the present study, a serum-based minimum inhibitory concentration (MIC) testing to caspofungin was optimized and evaluated to solve the limitations of the conventional Clinical and Laboratory Standards Institute (CLSI) guideline-based antifungal agent MIC test and the usefulness of this testing for clinical application was determined. A total of 105 Candida albicans clinical isolates were used for measuring MIC to caspofungin. Results showed that growth characteristics were different according to types of serum and the mouse serum was the most suitable for this assay. In order to measure the optimal concentration of mouse serum, 0 to 100% mouse serum were added to the media during fungal culture. The optimal concentration of serum was 50% when consideration of antifungal agent administration and inoculum size, serum components and ease of hyphae separated, and the consideration of the degree of growth. In comparison of the usefulness between the conventional Alamar-modified broth microdilution MIC assay and 50% mouse serum-based MIC testing, the range of $MIC_{80}$ of the Alamar-modified broth microdilution MIC assay was $0.13{\sim}2.0{\mu}g/mL$ (SD ${\pm}0.42{\mu}g/mL$) and that of the 50% mouse serum-based MIC assay was $2.0{\sim}32.0{\mu}g/mL$ (SD ${\pm}9.01{\mu}g/mL$). The range of $MIC_{50}$ of the Alamar-modified broth microdilution MIC assay was $0.13{\sim}2.0{\mu}g/mL$ (SD ${\pm}0.40{\mu}g/mL$) and that of the 50% mouse serum-based MIC assay was $1.0{\sim}16.0{\mu}g/mL$ (SD ${\pm}2.36{\mu}g/mL$). The MICs of 50% mouse serum-based MIC testing was increased by up to 4 to 64 times than Alamar-modified broth microdilution MIC assay. In conclusion, a 50% mouse serum-based MIC assay was more useful for measuring MIC in Candida albicans clinical isolates than conventional colorimetric broth microdilution MIC testing.

• Progress in Medical Physics
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• v.23 no.3
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• pp.162-168
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• 2012

In proton therapy, in vivo dose verification is one of the most important parts to fully utilize characteristics of proton dose distribution concentrating high dose with steep gradient and guarantee the patient safety. Currently, in order to image the proton dose distribution, a prompt gamma distribution detection system, which consists of an array of multiple CsI(Tl) scintillation detectors in the vertical direction, a collimator, and a multi-channel DAQ system is under development. In the present study, the optimal design of prompt gamma distribution detection system was studied by Monte Carlo simulations using the MCNPX code. For effective measurement of high-energy prompt gammas with enough imaging resolution, the dimensions of the CsI(Tl) scintillator was determined to be $6{\times}6{\times}50mm^3$. In order to maximize the detection efficiency for prompt gammas while minimizing the contribution of background gammas generated by neutron captures, the hole size and the length of the collimator were optimized as $6{\times}6mm^2$ and 150 mm, respectively. Finally, the performance of the detection system optimized in the present study was predicted by Monte Carlo simulations for a 150 MeV proton beam. Our result shows that the detection system in the optimal dimensions can effectively measure the 2D prompt gamma distribution and determine the beam range within 1 mm errors for 150 MeV proton beam.