• 미생물과산업
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• 제19권4호
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• pp.32-35
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• 1993

ras는 활성화 형태인 GTP bound form과 비활성화 형태인 GDP bound form의 두 형태로 존재하며 두 형태를 매개하는 regulatory protein들에 의해 그 activity가 조절된다. 또한 ras는 GTP와 GDP에 강한 친화성이 있으며 세포내에는 GTP보다 GDP가 더 많이 있어서 평소에는 ras가 GDP와 결합하고 있다가 활성화될때만 GTP와 결합하는 것으로 추정된다. GDP bound ras는 guanine nucloetide exchange protein(GEP)에 의해 활성화된 GTP bound form으로 전환되며 ras의 기능이 발휘된 후에는 GTPase activating protein(GAP)에 의해 비활성화된다. Yeast의 경우 IRA1과 2의 product가 GAP의 역할을 하는 것으로 알려져 있고 CDC25 gene의 product가 GEP의 기능을 담당하는 것으로 알려져 있다. NF1 gene은 Von Recklinghausen Neurofibromatosis Type I 질병을 가진 환자에게서 발견되었는데 부분적으로 sequencing한 결과에 따르면 yeast의 IRA1/2, mammalian GAP gene product와 protein homology가 높은 것으로 나타났다. Yeast의 경우 IRA1/2 gene의 손실이나 mammalian ras gene의 transformation으로 인한 heat shock sensitivity가 NF1 gene(2,3) 혹은 GAP(4)의 expression으로 suppression된 것으로 보아 NF1이 GAP protein으로서 ras를 불활성화 시킨다는 것이 판명되었다. 결론적으로 ras의 활성은 GTP bound 혹은 GDP bound의 양쪽형태를 이동하면서 조절되는데 이 기능은 GAP과 GEP 또는 그의 유사 protein들에 의해 수행되며 이러한 regulatory protein들은 growth factor, cytokine 그리고 protein kinase 같은 signal에 의해 활성화된다고 생각된다. 본 총설에서는 ras protein의 여러가지 성질보다는 ras의 modification과 관련하여 항암제로 사용할 수 있는 ras에 specific한 약품개발의 가능성과 현재 알려진 ras의 inhibitor를 중심으로 논하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권6호
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• pp.1273-1293
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• 2010

Immune-inspired intrusion detection is a promising technology for network security, and well known for its diversity, adaptation, self-tolerance, etc. However, scalability and coverage are two major drawbacks of the immune-inspired intrusion detection systems (IIDSes). In this paper, we propose an IIDS framework, named GEP-IIDS, with improved basic system elements to address these two problems. First, an additional bio-inspired technique, gene expression programming (GEP), is introduced in detector (corresponding to detection rules) representation. In addition, inspired by the avidity model of immunology, new avidity/affinity functions taking the priority of attributes into account are given. Based on the above two improved elements, we also propose a novel immune algorithm that is capable of integrating two bio-inspired mechanisms (i.e., negative selection and positive selection) by using a balance factor. Finally, a pruning algorithm is given to reduce redundant detectors that consume footprint and detection time but do not contribute to improving performance. Our experimental results show the feasibility and effectiveness of our solution to handle the scalability and coverage problems of IIDS.

• Childhood Kidney Diseases
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• 제9권2호
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• pp.119-127
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• 2005

목 적 : 단백뇨 질환에서 볼 수 있는 사구체 상피세포(glomerular epithelial cells, GEpC) 족돌기 사이에 위치한 세극막(slit diaphragm)의 P-cadherin의 당뇨조건에 따른 병리학적 변화를 알아보고자 하였다. 방 법 : 백서 GEpC을 배양하고 고농도의 당과 후기당화합물(advanced glycosylation endproducts, AGE)을 적용하여 당뇨병 환경에 가까운 조건을 설정한 후, p-cadherin 단백양은 Western 분석으로, 유전자 표현의 변화는 RT-PCR로 관찰하였다. 실험군은 당의 농도를 5 또는 30mM로, AGE와 BSA를 첨가하고 osmotic control로서 당 5 mM에 mannitol 25 mM을 섞은 것을 조합하여 A5, A30, B5, B30, Aosm로 하였다. 결 과 : P-cadherin 단백양은 B5 결과를 대조군으로 비교하여 당을 첨가한 B30에서 50.4$\%$의 감소, AGE를 추가한 조건인 A5와 A30에서 각각 7.4$\%$와 30.4$\%$의 의미 있는 감소를 보였다. 또한 P-cadherin mRNA의 표현은 B30에서 40.3$\%$의 감소, A30에서 27.2$\%$의 의의 있는 감소를 보였다. 이러한 감소 소견은 osmotic control(Aosm)에서는 관찰할 수 없었다. 결 론 : 고농도의 당과 AGE에 의한 GEpC의 P-cadherin을 유전자 수준에서의 억제로 단백의 생성 감소를 초래함으로써, 당뇨환경에서 세극막 성분의 변화를 설명할 수 있으며, 추후 이의 변화 기전에 대한 연구가 필요할 것으로 사료된다.

• 자연과 농업
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• 통권265호
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• pp.42-45
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• 2011

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제9권2호
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• pp.83-89
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• 2005

In this paper a new algorithm of learning and evolving artificial neural networks using gene expression programming (GEP) is presented. Compared with other traditional algorithms, this new algorithm has more advantages in self-learning and self-organizing, and can find optimal solutions of artificial neural networks more efficiently and elegantly. Simulation experiments show that the algorithm of evolving weights or thresholds can easily find the perfect architecture of artificial neural networks, and obviously improves previous traditional evolving methods of artificial neural networks because the GEP algorithm imitates the evolution of the natural neural system of biology according to genotype schemes of biology to crossover and mutate the genes or chromosomes to generate the next generation, and the optimal architecture of artificial neural networks with evolved weights or thresholds is finally achieved.

• 대한전기학회논문지:전력기술부문A
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• 제53권9호
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• pp.521-528
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• 2004

The demise of the native franchise markets and the emergence of competitive markets in electricity generation service is substantially altering the way that operation and planning activity is conducted and is making it increasingly difficult for market participants such as generation firms to prospect the future electricity markets. Traditional generation expansion planning (GEP) problems which centrally determine the least-cost capacity addition plan that meets forecasted demand within pre-specified reliability criteria over a planning horizon (typically 10 to 20 years) is becoming no more valid in competitive market environments. Therefore, it requires to develop a new methodology for generation investments, which is applicable to the changed electric industry business environments and is able to address the post-privatization situation where individual generation firms seek to maximize their return on generation investments against uncertain market revenues. This paper formulates a new generation expansion planning problem and solve it in a market-oriented manner.

• 대한전기학회논문지:전력기술부문A
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• 제50권4호
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• pp.180-187
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• 2001

This paper proposes an efficient evolutionary programming algorithm for solving a generation expansion planning(GEP) problem known as a highly-nonlinear dynamic problem. Evolutionary programming(EP) is an optimization algorithm based on the simulated evolution (mutation, competition and selection). In this paper, new algorithm is presented to enhance the efficiency of the EP algorithm for solving the GEP problem. By a domain mapping procedure, yearly cumulative capacity vectors are transformed into one dummy vector, whose change can yield a kind of trend in the cost value. To validate the proposed approach, this algorithm is tested on two cases of expansion planning problems. Simulation results show that the proposed algorithm can provide successful results within a resonable computational time compared with conventional EP and dynamic programming.

• Structural Engineering and Mechanics
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• 제83권5호
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• pp.577-591
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• 2022

This paper develops a comparatively time-efficient methodology for performing seismic fragility analysis of the reinforced concrete (RC) buildings in the presence of uncertainty sources. It aims to appraise the effectiveness of any variation in the material's mechanical properties as epistemic uncertainty, and the record-to-record variation as aleatory uncertainty in structural response. In this respect, the fuzzy set theory, a well-known 𝛼-cut approach, and the Genetic Algorithm (GA) assess the median of collapse fragility curves as a fuzzy response. GA is requisite for searching the maxima and minima of the objective function (median fragility herein) in each membership degree, 𝛼. As this is a complicated and time-consuming process, the authors propose utilizing the Gene Expression Programming-based (GEP-based) equation for reducing the computational analysis time of the case study building significantly. The results indicate that the proposed structural analysis algorithm on the derived GEP model is able to compute the fuzzy median fragility about 33.3% faster, with errors less than 1%.

• 경영과학
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• 제17권1호
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• pp.117-134
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• 2000

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.

• 한국실천공학교육학회논문지
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• 제4권2호
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• pp.53-59
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• 2012

본고는 학생주도 창의융합 프로젝트식 교육 모델의 설계와 시행을 통해 21세기에 요구되는 창의적 공학인재 양성을 위한 교육의 새로운 방법론을 제시하고 그 성과에 대해 고찰하는 것을 목적으로 한다. 이를 위해 먼저, 공학적 창의성에 대한 개념과 R&D 3.0 및 교육3.0의 구체적 내용을 검토하였다. R&D 3.0과 교육 3.0의 내용검토를 통해 기술과 인문을 융합할 수 있는 융합 역량과 국제 협력 역량 강화를 지향하는 학생 주도의 프로젝트식 교육의 필요성을 확인하였고, 공학적 창의성에 대한 논의를 통해 기술의 사회인문학적 맥락을 읽을 수 있는 인문사회적 소양 교육과 경험 중심의 현장 교육 및 집단적 창의성을 함양할 수 있는 팀 기반 교육의 필요성을 도출하였다. 이를 바탕으로, 학생주도 창의융합 프로젝트인 공학도를 위한 Global Engineering Project(이하 GEP) 프로그램을 설계하고 이를 3차에 걸쳐 시행한 후, 시행 결과를 바탕으로 프로그램의 성과와 기대 효과를 정리하였다.