• 전산구조공학
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• 제9권2호
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• pp.93-101
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• 1996

인공 신경회로망은 인간의 뇌를 전산 모델로 구현한 것으로 상호 연결된 많은 정보 처리 유니트들로 구성되어 있으며, 이를 기초로 논리적인 추론을 수행할 수 있다. 특히, 신경망은 비선형 변수를 많이 포함하고 있는 복잡한 문제 해결에서 더욱 효과적이다. 신경망의 이러한 기능으로 인해 구조분야에서는 비선형적인 각종 구조실험의 결과예측이나 구조계획 그리고 최적 설계에 응용되고 있는 추세이다. 본 논문에서는 인공 신경 회로망의 기본 이론을 설명하고, 현재까지 정립되고 있지 않은 대형 콘크리트 판넬간 수직 접합부의 최대 전단 내력 예측에 기존의 제안식과 인공 신경 회로망의 예측 결과를 비교하여 신경망의 적용가능성을 검토하고자 한다.

• Biotechnology and Bioprocess Engineering:BBE
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• 제11권5호
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• pp.455-461
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• 2006

In this study, we have described a method for the fabrication of a protein chip on silicon substrate using hydrophobic thin film and microfluidic channels, for the simultaneous detection of multiple targets in samples. The use of hydrophobic thin film provides for a physical, chemical, and biological barrier for protein patterning. The microfluidic channels create four protein patterned strips on the silicon surfaces with a high signal-to-noise ratio. The feasibility of the protein chips was determined in order to discriminate between each protein interaction in a mixture sample that included biotin, ovalbumin, hepatitis B antigen, and hepatitis C antigen. In the fabrication of the multiplexed assay system, the utilization of the hydrophobic thin film and the microfluidic networks constitutes a more convenient method for the development of biosensors or biochips. This technique may be applicable to the simultaneous evaluation of multiple protein-protein interactions.

• 폴리머
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• 제39권3호
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• pp.426-432
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• 2015

Poly(ether sulfone) (PES)가 첨가된 비스페놀-A 에폭시 복합재가 그 기계적 특성 및 열 안정성을 증진하기 위하여 제조되었다. 인장강도, 굽힘강도 및 충격강도 등의 기계적 강도가 PES 함량에 따라 의미있게 변화하였다. 특히 그 파괴인성 값은 약 24%의 정도 크게 향상되었다. 적분 열분해 진행온도를 통하여 계산된 PES/에폭시 복합재의 열 안정성은 PES 미첨가 에폭시와 비교하여 12%의 향상을 보였다. 또한 DSC 분석 결과 PES 함량이 증가함에 따라 경화온도와 경화열이 점점 감소함을 확인하였다. 이러한 현상은 에폭시 수지와 선형 PES가 가교구조(semi-interpenetrating polymer networks; semi-IPNs)를 형성하고 잘 분산되었기 때문으로 판단된다.

• 적정기술학회지
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• 제6권2호
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• pp.238-255
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• 2020

2019년말 발생한 COVID-19이 세계적 대유행으로 확산되면서, 선진국과 개발도상국을 가리지 않고 전세계의 의료 시스템을 동시에 무너뜨리는 초유의 사태로 나타나고 있다. 대부분의 시민 사회에서 개인의 보호장구의 부족, 클러스터가 된 지역사회의 격리, 진단과 치료에 필요한 의료시스템의 중단, 사회적 격리로 인한 교육 및 경제활동의 중단 등, 초연결 사회를 지향하던 시민사회가 경험해보지 못한 새로운 위기에 직면하고 있다. 효과적인 대응에 필수적인 개인 보호장비 (PPE), 제독 및 방역 도구, 신속하고 정밀한 대규모 진단, 환자 치료에 필요한 의료기기, 사회적 고립에서 활용될 수 있는 정보의 분석과 연결망 등은 선진국과 개발도상국을 가리지 않고 COVID-19 상황에서 필수적인 사회적 인프라임이 확인되고 있다. 본 Part 1에서는 최근 적정기술로 위기를 극복하기위한 개인보호장구와 개인 및 지역사회의 방역에 대한 사례, 그리고 ICT를 이용한 빅데이터 및 정보활용기술등을 적정기술의 테두리안에서 소개하고자 한다.

• 한국통신학회논문지
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• 제40권10호
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• pp.2054-2061
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• 2015

최근 생물학적으로 영감을 받은 모델링 기술은 단순한 현장 상호작용과 제한된 정보와 함께 이들의 강인성과 확장성, 적응성에 대해 상당한 관심을 받고 있다. 이러한 모델링 기술들 중, 유전자 조절 네트워크(Gene Regulatory Networks)(GRNs)은 세포로부터 생물학적 유기체의 발생과 자연 진화에 대한 이해에서 핵심적인 역할을 하고 있다. 본 논문은 GRN 원리를 무선 센서 네트워크 시스템에 적용하고 시간지연 요건을 충족하는 동시에 에너지 균형을 달성할 수 있는 분산화된 노드 스케쥴링 설계 기법을 제안한다. 각 센서 노드는 소비된 에너지 수준과 지연시간에 반응하여 자동으로 자신의 상태를 스케줄링하며, 이는 GRN 모델에서 영감을 받은 유전자 발현과 단백질 농도 조절 모델에 의해 제어된다. 시뮬레이션 결과는 제안된 방법이 에너지 균형뿐만 아니라 원하는 시간 지연에서 성능을 달성하고 있다는 점을 보여준다.

• BMB Reports
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• 제35권1호
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• pp.7-18
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• 2002

The major function of the thymus is to eliminate developing thymocytes that are potentially useless or autoreactive, and select only those that bear functional T cell antigen receptors (TCRs) through fastidious screening. It is believed that glucocorticoids (GCs) are at least in part responsible for cell death during death by neglect. In this review, we will mainly cover the topic of the GC-induced apoptosis of developing thymocytes. We will also discuss how thymocytes that are fated to die by GCs can be rescued from GC-induced apoptosis in. response to a variety of signals with antagonizing properties for GC receptor (GR) signaling. Currently, a lot of evidence supports the notion that the decision is made as a result of the integration of the multiple signal transduction networks that are triggered by GR, TCR, and Notch. A few candidate molecules at the converging point of these multiple signaling pathyways will be discussed. We will particularly describe the role of the SRG3 protein as a potent modulator of GC-induced apoptosis in the crosstalk.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2004년도 The 3rd Annual Conference for The Korean Society for Bioinformatics Association of Asian Societies for Bioinformatics 2004 Symposium
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• pp.265-271
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• 2004

The interaction network of protein -protein plays an important role to understand the various biological functions of cells. Currently, the high -throughput experimental techniques (two -dimensional gel electrophoresis, mass spectroscopy, yeast two -hybrid assay) provide us with the vast amount of data for protein-protein interaction at the proteome scale. In order to recognize the role of each protein in their network, the efficient bioinformatical and computational analysis methods are required. We propose a systematic and mathematical method which can analyze the protein -protein interaction network rigorously and enable us to capture the biological and physical essence of a topological character and stability of protein -protein network, and sensitivity of each protein along the biological pathway of their network. We set up a Laplacian matrix of spectral graph theory based on the protein-protein network of yeast proteome, and perform an eigenvalue analysis and apply a perturbation method on a Laplacian matrix, which result in recognizing the center of protein cluster, the identity of hub proteins around it and their relative sensitivities. Identifying the topology of protein -protein network via a Laplacian matrix, we can recognize the important relation between the biological pathway of yeast proteome and the formalism of master equation. The results of our systematic and mathematical analysis agree well with the experimental findings of yeast proteome. The biological function and meaning of each protein cluster can be explained easily. Our rigorous analysis method is robust for understanding various kinds of networks whether they are biological, social, economical...etc

• Bulletin of the Korean Chemical Society
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• 제30권9호
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• pp.1981-1984
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• 2009

Subcellular localization of protein kinase often plays an important role in determining its activity and specificity. Protein kinase C (PKC), a family of multi-gene protein kinases has long been known to be translocated to the particular cellular compartments in response to DAG or its analog phorbol esters. We used C-terminal green fluorescent protein (GFP) fusion proteins of PKC isoforms to visualize the subcellular distribution of individual PKC isoforms. Intracellular localization of PKC-GFP proteins was monitored by fluorescence microscopy after transient transfection of PKC-GFP expression vectors in the HeLa cells. In unstimulated HeLa cells, all PKC isoforms were found to be distributed throughout the cytoplasm with a few exceptions. PKC$\theta$ was mostly localized to the Golgi, and PKC$\gamma$, PKC$\delta$ and PKC$\eta$ showed cytoplasmic distribution with Golgi localization. DAG analog TPA induced translocation of PKC-GFP to the plasma membrane. PKC$\alpha$, PKC$\eta$ and PKC$\theta$ were also localized to the Golgi in response to TPA. Only PKC$\delta$ was found to be associated with the nuclear membrane after transient TPA treatment. These results suggest that specific PKC isoforms are translocated to different intracellular sites and exhibit distinct biological effects.

• Steel and Composite Structures
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• 제14권4호
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• pp.367-377
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• 2013

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

• Environmental Engineering Research
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• 제25권4호
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• pp.515-521
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• 2020

Nowadays, due to various pollution sources, it is essential for environmental scientists to monitor water quality. Phytoplanktons form the end of the food chain in water bodies and are one of the most important biological indicators in water pollution studies. Chlorophyll-A, a green pigment, is found in all phytoplankton. Chlorophyll-A concentration indicates phytoplankton biomass directly. Therefore, Chlorophyll-A is an indirect indicator of pollutants, including phosphorus and nitrogen, and their refinement and control are important. The present study, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were used to estimate the chlorophyll-A concentration in southern coastal waters in the Caspian Sea. For this purpose, Multi-layer perceptron neural networks (NNs) were applied which contained three and four feed-forward layers. The best three-layer NN has 15 neurons in its hidden layer and the best four-layer one has 5 in each. The three- and four- layer networks both resulted in similar root mean square errors (RMSE), 0.1($\frac{{\mu}g}{l}$), however, the four-layer NNs proved superior in terms of R² and also required less training data. Accordingly, a four-layer feed-forward NN with 5 neurons in each hidden layer, is the best network structure for estimating Chlorophyll-A concentration in the southern coastal waters of the Caspian Sea.