• Korean Chemical Engineering Research
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• 제45권1호
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• pp.25-31
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• 2007

석유화학단지내에서 석유화학공장과 정유공장과 같은 산업현장에서는 상당량의 수소가 부산물로 발생되고 있으나, 이는 대부분 자체적으로 연료로 사용되고 있다. 그러나 연료로 사용되는 상당량의 수소를 에너지원의 원료나 기타 공정의 원료로 재활용할 경우, 현재보다 수소의 가치를 높여서 사용할 수 있다. 본 연구에서는 석유화학단지내 공장간 수소 재활용 네트워크를 설계하였다. 수소 핀치 분석을 통하여 교환망 구성에 필요한 최소의 수소 요구 및 정제량을 파악하고, 네트워크 구성에 필요한 비용과 기타 제약 조건으로 최적화 문제를 구성하여 공급처(source)와 수요처(sink) 공장간에 최적으로 수소를 재활용하기 위한 네트워크를 설계하였다.

• 생태와환경
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• 제50권4호
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• pp.478-482
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• 2017

In order to identify key nations and bird species of conservation concern we described multinational collaborations as defined using network analysis linked by birds that are found in all nations in the network. We used network analysis to assess the patterns in bird occurrence for 10,422 bird inventories from 244 countries and territories. Nations that are important in multinational collaborations for bird conservation were assessed using the centrality measures, closeness and betweenness centrality. Countries important for the multinational collaboration of bird conservation were examined based on their centrality measures, which included closeness and betweenness centralities. Comparatively, the co-occurrence network was divided into four groups that reveal different biogeographical structures. A group with higher closeness centrality included countries in southern Africa and had the potential to affect species in many other countries. Birds in countries in Asia, Australia and the South Pacific that are important to the cohesiveness of the global network had a higher score of betweenness centrality. Countries that had higher numbers of bird species and more extensively distributed bird species had higher centrality scores; in these countries, birds may act as excellent indicators of trends in the co-occurrence bird network. For effective bird conservation in the world, much stronger coordination among countries is required. Bird co-occurrence patterns can provide a suitable and powerful framework for understanding the complexity of co-occurrence patterns and consequences for multinational collaborations on bird conservation.

• Genomics & Informatics
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• 제1권1호
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• pp.7-19
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• 2003

The latest measure of the relative evolutionary age of protein structure families was applied (based on taxonomic diversity) using the protein structural interactome map (PSIMAP). It confirms that, in general, protein domains, which are hubs in this interaction network, are older than protein domains with fewer interaction partners. We apply a hypothesis of 'biological network evolution' to explain the positive correlation between interaction and age. It agrees to the previous suggestions that proteins have acquired an increasing number of interaction partners over time via the stepwise addition of new interactions. This hypothesis is shown to be consistent with the scale-free interaction network topologies proposed by other groups. Closely co-evolved structural interaction and the dynamics of network evolution are used to explain the highly conserved core of protein interaction pathways, which exist across all divisions of life.

• 대한의용생체공학회:의공학회지
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• 제23권4호
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• pp.329-332
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• 2002

일반적으로 금지뉴런의 효과는 신경망을 안정시킨다고 알려져 있다. 본 연구에서는 확률적 평균 필드 이론에 근거한 신경망 모델에서 느린 금지뉴런의 역할을 살펴보았다. 흥분뉴런과 빠른 금지뉴런으로 구성된 신경망에 느린 금지뉴런을 더하면, 느린 금지 뉴런이 없는 모델에서보다 매우 낮은 역치에서 안정적인 동시적 활동이 유도된다는 것을 발견하였다. 이 역치는 대뇌 피질 신경의 생리학적 역치와 일치하며. 느린 금지 뉴런만이 신경망에 낮은 발화율과 낮은 역치를 유지시키는 네거티브 피드백을 줄 수 있다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.395-397
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• 1997

We have applied the neural network method for the neural networkmethod for the automatic scoring of the sleep stage. 17 features are extracted from the recorded EEG, EOG and EMG signals. These features are inputed to tile multilayer perceptron model. Neural network was trained with error-back propagation method. Results are compared with manual scoring of the experts, and show the possibility of application of automatic method in sleep stage scoring.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 춘계학술대회 학술발표 논문집 제14권 제1호
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• pp.273-276
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• 2004

Genome-scale expression data provides us with valuable insights about organisms, but the biological validation of in-silico analysis is difficult and often controversial. Here we present a new approach for integrating previously established knowledge with computational analysis. Based on the known biological evidences, IGAM (Integrated Gene Association Matrix) automatically estimates the relatedness between a pair of genes. We combined this association knowledge to the regulatory network modeling and fuzzy clustering in yeast 5. Cerevisiae. The result was found to be more effective for extracting biological meanings from in-silico inferences for gene expression data.

• Genomics & Informatics
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• 제1권1호
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• pp.1-6
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• 2003

In conclusion, the seemingly fuzzy and disorganized data of biology with thousands of different layers ranging from molecule to the Internet have refused so far to be mapped precisely and predicted successfully by mathematicians, physicists or computer scientists. Genomics and bioinformatics are the fields that process such complex data. The insights on the nature of biological entities as complex interaction networks are opening a door toward a generalization of the representation of biological entities. The main challenge of genomics and bioinformatics now lies in 1) how to data mine the networks of the domains of bioinformatics, namely, the literature, metabolic pathways, and proteome and structures, in terms of interaction; and 2) how to generalize the networks in order to integrate the information into computable genomic data for computers regardless of the levels of layer. Once bioinformatists succeed to find a general principle on the way components interact each other to form any organic interaction network at genomic scale, true simulation and prediction of life in silico will be possible.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1867-1870
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• 2002

In the present paper, we will focus on the characterization of the biological network behaviour, in terms of synchronization and desynchronization of the measured signals by Micro Electrode array. We evaluate a easy calculable estimator that implies de/synchronization property of the biological neural network.

• Journal of Ecology and Environment
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• 제46권4호
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• pp.292-303
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• 2022

Background: Climate change significantly influences the geographical distribution of plant species worldwide. Selecting indicator species allows for better-informed and more effective ecosystem management in response to climate change. The Korean Peninsula is the northernmost distribution zone of warm temperate evergreen broad-leaved (WTEB) species in Northeast Asia. Considering the ecological value of these species, we evaluated the current distribution range and future suitable habitat for 13 WTEB tree species designated as climate-sensitive biological indicator species. Results: Up-to-date and accurate WTEB species distribution maps were constructed using herbarium specimens and citizen science data from the Korea Biodiversity Observation Network. Current northern limits for several species have shifted to higher latitudes compared to previous records. For example, the northern latitude limit for Stauntonia hexaphylla is higher (37° 02' N, Deokjeokdo archipelago) than that reported previously (36° 13' N). The minimum temperature of the coldest month (Bio6) is the major factor influencing species distribution. Under future climate change scenarios, suitable habitats are predicted to expand toward higher latitudes inland and along the western coastal areas. Conclusions: Our results support the suitability of WTEB trees as significant biological indicators of species' responses to warming. The findings also suggest the need for consistent monitoring of species distribution shifts. This study provides an important baseline dataset for future monitoring and management of indicator species' responses to changing climate conditions in South Korea.

• 대한의용생체공학회:의공학회지
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• 제22권4호
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• pp.369-375
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• 2001

The statistical regression model is one of the most frequently used clinical analysis methods. It has basic assumption of linearity, additivity and normal distribution of data. However, most of biological data in medical field are nonlinear and unevenly distributed. To overcome the discrepancy between the basic assumption of statistical model and actual biological data, we propose a new analytical method based on artificial neural network. The newly developed multilayer perceptron(MLP) is trained with 120 data set (60 normal, 60 patient). On applying test data, it shows the discrimination power of 0.76. The diabetic risk factors were also identified from the MLP neural network model and the logistic regression model. The signigicant risk factors identified by MLP model were post prandial glucose level(PP2), sex(male), fasting blood sugar(FBS) level, age, SBP, AC and WHR. Those from the regression model are sex(male), PP2, age and FBS. The combined risk factors can be identified using the MLP model. Those are total cholesterol and body weight, which is consistent with the result of other clinical studies. From this experiment we have learned that MLP can be applied to the combined risk factor analysis of biological data which can not be provided by the conventional statistical method.