• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.45-52
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• 2012

T cells induce immune responses and thereby eliminate infected micro-organisms when peptides from the microbial proteins are bound to HLAs in the host cell surfaces, It is known that the more stable the binding of peptide to HLA is, the stronger the T cell response gets to remove more effectively the source of infection. Accordingly, if peptides (HLA binder) which can be bound stably to a certain HLA are found, those peptieds are utilized to the development of peptide vaccine to prevent infectious diseases or even to cancer. However, HLA is highly polymorphic so that HLA has a large number of alleles with some frequencies even in one population. Therefore, it is very inefficient to find the peptides stably bound to a number of HLAs by testing random possible peptides for all the various alleles frequent in the population. In order to solve this problem, computational methods have recently been developed to predict peptides which are stably bound to a certain HLA. These methods could markedly decrease the number of candidate peptides to be examined by biological experiments. Accordingly, this paper not only introduces a method of machine learning to predict peptides binding to an HLA, but also suggests a new prediction model so called 'knowledge-based genetic algorithm' that has never been tried for HLA binding peptide prediction. Although based on genetic algorithm (GA). it showed more enhanced performance than GA by incorporating expert knowledge in the process of the algorithm. Furthermore, it could extract rules predicting the binding peptide of the HLA alleles common in Koreans.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.17-23
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• 2016

In the future network such as Internet of Things (IoT), the number of computing devices are expected to grow exponentially, and each of the things communicates with the others and acquires information by itself. Due to the growing interest in IoT applications, the broadcasting in Opportunistic ad-hoc networks such as Machine-to-Machine (M2M) is very important transmission strategy which allows fast data dissemination. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose a fuzzy logic based probabilistic multi-hop broadcast (FPMCAST) algorithm which statistically disseminates data accordingly to the remaining energy rate, the replication density rate of sending node, and the distance rate between sending and receiving nodes. In proposed FPMCAST, the inference engine is based the fuzzy rule base which is consists of 27 if-then rules. It maps input and output parameters to membership functions of input and output. The output of fuzzy system defines the fuzzy sets for rebroadcasting probability, and defuzzification is used to extract a numeric result from the fuzzy set. Here Center of Gravity (COG) method is used to defuzzify the fuzzy set. Then, the performance of FPMCAST is evaluated through a simulation study. From the simulation, we demonstrate that the proposed FPMCAST algorithm significantly outperforms flooding and gossiping algorithms. Specially, the FPMCAST algorithm has longer network lifetime because the residual energy of each node consumes evenly.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.123-131
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• 2016

Process mining in big data environment utilize a number of data were generated from the business process. It generates lots of knowledge and insights regarding implementation and improvement of the process through the event log of the company's enterprise resource planning (ERP) system. In recent years, various research activities engaged with the audit work of company organizations are trying actively by using the maximum strength of the mining process. However, domestic studies on applicable sales auditing system for the process mining are insufficient under big data environment. Therefore, we propose process-mining methods that can be optimally applied to online and traditional auditing system. In advance, we propose continuous monitoring information system that can early detect and prevent the risk under the big data environment by monitoring risk factors in the organizations of enterprise. The scope of the research of this paper is to design a pre-verification system for risk factor via practical examples in sales auditing. Furthermore, realizations of preventive audit, continuous monitoring for high risk, reduction of fraud, and timely action for violation of rules are enhanced by proposed sales auditing system. According to the simulation results, avoidance of financial risks, reduction of audit period, and improvement of audit quality are represented.

• Applied Chemistry for Engineering
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• v.25 no.1
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• pp.116-120
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• 2014

Light cycle oil (LCO), one of the by-products of the catalytic cracking gasoline manufacturing process, contains a lot of valuable aromatics. In particular, 2,6-dimethylnaphthalene (2,6-DMN) contained in LCO has been becoming important as the basic material of polyethylene naphthalate plastic and liquid crystal polymer, etc. If it were possible to separate and purify the valuable aromatic hydrocarbons (such as 2,6-DMN) from LCO, which have only been used as fuel mixed with heavy oil, it would be very meaningful in terms of the efficient use of resources. We investigated the high-purity purification of 2,6-DMN by the combined method of melt crystallization (MC) and solute crystallization (SC). The enriched DMN isomer mixtures (concentration of 2,6-DMN : 10.43%) recovered from LCO by distillation-extraction combination and the crystal recovered by MC used as raw materials of MC and SC, respectively. The solvent of SC used was a mixture of methanol and acetone (60 : 40 wt%). The crystal of 2,6-DMN with a high-purity of 99.5% was recovered by MC-SC combination. We confirmed that the MC-SC combination was one of the very useful combinations for the high-purity purification of 2,6-DMN contained in the enriched DMN isomer mixtures.

• Applied Chemistry for Engineering
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• v.23 no.6
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• pp.546-553
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• 2012

Urushiol extracted from lacquer tree exhibits good thermal stabilities as well as antimicrobial andantioxidant properties. However, it has been known that the urushiol derivates bring out allergy. In this study, polyurushiol (PUOH) powders were successfully synthesized for the safe and convenient handling of allergic urushiol. First, the as-synthesized PUOH was confirmed by Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), thermal gravimetric analyzer (TGA), antioxidant test and antimicrobial test. And then, six different LDPE/PUOH composite films were prepared via a twin screw extruder system and investigated their feasibility to use as active packaging materials. Their chemical structures, morphology, thermal optical and antimicrobial properties of the LDPE/PUOH composite films were investigated as a function of PUOH contents. FTIR and SEM results showed that LDPE/PUOH composite films have a weak interfacial interaction and poor dispersion with a high PUOH loading. The thermal properties increased up to 3 wt% as the content of PUOH increases. Compared to the pure LDPE films, LDPE/PUOH composite films are more effective in the UV absorbance and antibacterial activity against E. coli. To maximize the performance of LDPE/PUOH compositefilms as the packaging materials, further researches are required to enhance the dispersion of PUOH powders in the LDPE matrix.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2006.06a
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• pp.46-66
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• 2006

새로운 지능기반사회(ubiquitous society) 패러다임이란 전제 아래 미국을 비롯해 일본, 유럽 등 세계 각국에서는 모바일, 브로드밴드, 극소형 컴퓨터, IPv6의 기술이 창출해 내는 컴퓨팅 혁명의 실체를 유비궈터스 IT로 파악하고 각 국의 걸부, 기업, 연구소들이 주도권을 잡기 위해 많은 노력을 기울이고 있다. 현재 유비쿼터스 IT를 활용한 u-비즈니스를 활성화하고 관련 시장을 선점하기 위한 전 세계적인 경쟁은 매우 치열한 양상을 보이고 있다. 주요 국가들은 서로 지향하는 영역은 다르지만 궁극적으로 지능기반사회 구현이라는 대명제 아래 독자적인 산업 구축을 위해 노력하고 있다. 이러한 이유는 기본적으로 지능기반사회로의 이행과정에서 발생하는 경제적 파급효과, 예컨대 디지털 가전, 이동통신기기, 전자의료기기, 디스플레이, 2차 전지, 보안 등 관련 산업에 미치는 영향 및 새로운 시장 창출의 기회가 있기 때문이다. 기존의 많은 연구 및 문헌에서는 아직도 유비쿼터스 IT에 대한 개념적인 논의에 치중되어 있고 시장 중심적 또는 수요자 중심적 사고의 논의가 부족하다. 성공적으로 유비쿼터스 산업을 육성하기 위해서는 유비쿼터스 IT자체기술 개발사업(tangible service) 뿐만 아니라 비가시적(intangible) 서비스, 그리고 컨버전스(convergence) 서비스에 대한 상용화 모델과 성공적 사업 전개 방법론에 대한 깊은 연구가 수행되어야 한다. 본 연구에서는 먼저 유비쿼터스 IT의 기술동향과 시장 동향 및 산업 규모에 대한 문헌연구를 통해 u-비즈니스의 가능성을 고찰해보고, u-비즈니스 추진 전략을 탐색적으로 제시하고자 한다.되었다. SPME-GC/MS에 의한 휘발성분 분석 결과, 기능성 유지에서 홍국쌀 에탄올 추출물로부터 기인되는 특유의 휘발성분은 관찰되지 않았으나, RT=12.1 min인 hexanal의 함량이 control에 비하여 유의적인 감소 경향을 나타내는 것이 확인되었다(p<0.05).히지 못하여, 이의 기전을 밝히기 위한 추후 연구가 필요하다./TEX>로서 p<0.01 수준에서 현저히 증가하였다. 혈청의 인지질과 콜레스테롤 에스테르의 지방산 조성에서 vitamin C를 혼합 투여한 3,5,7군에서 PUFA 함량이 증가한 반면, SFA 함량은 감소하여 P/S 비율, n-3P/n-6P 비율은 증가하는 경향이었으며 이는 간장의 인지질, 콜레스테롤 에스테르, 총 지질의 지방산조성에서도 같은 경향을 볼 수 있었다.X>$(C_{18:2})$와 n-3계 linolenic acid$(C_{18:3})$가 대부분을 차지하였다. 야생 돌복숭아 과육 중의 지방산 조성은 포화지방산이 16.74%, 단불포화지방산 17.51% 및 다불포화지방산이 65.73%의 함유 비율을 보였는데, 이 중 다불포화지방산인 n-6계 linoleic acid$(C_{18:2})$와 n-3계 linolenic acid$(C_{18:3})$가 지질 구성 총 지방산의 대부분을 차지하는 함유 비율을 나타내었다.했다. 하강하는 약 4일간의 기상변화가 자발성 기흉 발생에 영향을 미친다고 추론할 수 있었다. 향후 본 연구에서 추론된 기상변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것

• Journal of Animal Environmental Science
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• v.11 no.1
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• pp.35-44
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• 2005

The objectives of this study were to develop an image processing algorithm for finding the sick or the dead layers(SDL) rearing in the multi-tier layer battery, which is a core technology of remote monitoring systems for layers, and to test the performance of algorithm developed in the experimental poultry housing. Based on the literature study and experiment, the standing up of layer was set as a criterion for judging layers whether sick or dead. Then, by the criterion set, an algorithm was developed. The image processing algorithm developed was tested how well it could and SDL at the experimental poultry housing. Test results showed that its monitoring correctness of layers standing up in the cages having all healthy layers was $92\%$, and $96\%$ in the cages having SDL. Therefore, it would be concluded that the image processing algorithm developed in this study was well suited to the purpose of development.

• Journal of Life Science
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• v.15 no.6 s.73
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• pp.895-903
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• 2005

Thrombospondin-1 (TSP-1), a negative regulator in tumor growth and angiogenesis, is cell-type specifically regulated under pathological conditions or by extracellular stimuli, and the regulation of TSP-1 gene expression is important for developing new approaches in tumor therapy. Mistletoe is a parasitir plant that have been used for immunomodulation and antitumor therapy. Helixor A is an aqueous part of mistletoes extract. Here we showed that TSP-1 expression was significantly induced at both mRNA and protein levels in the Hepatocarcinorna cell line (Hep3B) and primary bovine endothelial cell line (BAE) exposed to Helixor A. Our promoter analysis confirmed that the expression of TSP-1 gene was regulated by Helixor A at the transcriptional level. In cell invasion assay, the conditioned media obtained from treatment of these cells significantly reduced the number of invasive cells and also inhibited capillary-like tube formation of BAE cells on Matrigel. Moreover, the inhibitory efforts of the conditioned media on cell invasion and tube formation were reversed by blocking with anti-TSP-1 neutralizing antibodies, suggesting that TSP-1 is involved in Helixor A-indured antiangiogenic effect. Taken together, our results suggest that Helixor A have an antiangiogenic effects through upregulation of TSP-1.

• Journal of Life Science
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• v.7 no.3
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• pp.192-197
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• 1997

Italian ryegrass, red clover, sorghum, and alfalfa were used for leaf protein preparation. Fresh leaves were pulped in the presence or absence of a reducing agent(sodium ascorbate or NaHSO$_{4}$) and green juice was heated and washed with acetone. The biological evaluation of leaf proteins was carried out by the growth method with male rats weighing about 45g. Italian ryegrass, red clover, and sorghum were brown when leaves were pulped in the absence of a reducing agent. On the other hand, alfalfa had neither o-diphenolics nor polyphenolase, and hence the alfalfa leaf protein did not brown during pulping ever in the absence of a reducing agent. The brown leaf protein from Italian ryegrass hd lower digestibility than the leaf protein protected from browning, although there were no difference in growth-promoting effect and protein efficiency ratio(PER)between the two leaf protein. The feeding of brown leaf protein from red clover resulted in the lowering of weight gain, digestibility, and PER, and all the measurement including diet intake were lowered by feeding the brown leaf protein from sorghum. In the case of alfalfa leaf protein, there were no difference in nutritional quality between the two leaf protein made with and without an attempt to prevent browning. The results mentioned above indicate that the occurrence of phenolics and polyphemolase in a crop in responsible for the browning of leaf protein and that the browning of leaf protein caused its nutritional impairment.