• Bioinformatics and Biosystems
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• 제2권2호
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• pp.71-74
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• 2007

Cell phenotypes are determined by groups of functionally related genes. Microarray profiling of gene expression provides us response of cellular state to its perturbation. Several methods for uncovering a cellular network show reliable network reconstruction. In this study, we present reconstruction of genetic regulatory network of inflammation bowel disease in human peripheral blood mononuclear cell. The microarray based on Affymetrix Gene Chip Human Genome U133 Array Set HG-U133A is processed and applied network reconstruction algorithm, ARACNe. As a result, we will show that inferred network composed of 450 nodes and 2017 edges is roughly scale-free network and hierarchical organization. The major hub, CCNL2 (cyclin A2), in inferred network is shown to be associated with inflammatory function as well as apoptotic function.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2005년도 추계학술대회 및 정기총회
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• pp.321-333
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• 2005

With the limited frequency spectrum and an increasing demand for cellular communication services, the problem of channel assignment becomes increasingly important. However, finding a conflict free channel assignment with the minimum channel span is NP hard. As demand for services has expanded in the cellular segment, sever innovations have been made in order to increase the utilization of bandwidth. The innovations are cellular concept, dynamic channel assignment and hierarchical network design. Hierarchical network design holds the public eye because of increasing demand and quality of service to mobile users. We consider the frequency assignment problem and the base station placement simultaneously. Our model takes the candidate locations emanating from this process and the cost of assigning a frequency, operating and maintaining equipment as an input. In addition, we know the avenue and demand as an assumption. We propose the network about the profit maximization. This study can apply to GSM(Global System for Mobile Communication) which has 70% portion in the world. Hierarchical network design using GA(Genetic Algorithm) is the first three-tier (Macro, Micro, Pico) model, We increase the reality through applying to EMC (Electromagnetic Compatibility Constraints). Computational experiments on 72 problem instances which have 15${\sim}$40 candidate locations demonstrate the computational viability of our procedure. The result of experiments increases the reality and covers more than 90% of the demand.

• Journal of Electrical Engineering and information Science
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• 제3권2호
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• pp.151-157
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• 1998

Free-space optical interconnection networks can be classified into two types, space variant and space invariant, according to the degree of space variance. In terms of physical implementations, the degree of space variance can be interpreted as the degree of sharing beam steering optics among the nodes of a given network. This implies that all nodes in a totally space-invariant network can share a single beam steering optics to realize the given network topology, whereas, in a totally space variant network, each node requires a distinct beam steering optics. However, space invariant networks require mechanisms for distinguishing the origins of incoming signals detected at the node since several signals may arrive at the same time if the node degree of the network is greater than one. This paper presents a signal source encoding scheme for distinguishing incoming signals efficiently, in terms of the number of detectors at each node or the number of unique wavelengths. The proposed scheme is solved by developing a new parallel genetic algorithm called distributed asynchronous genetic algorithm (DAGA). Using the DAGA, we solved signal distinction schemes for various network sizes of several topologies such as hypercube, the mesh, and the de Brujin.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권11호
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• pp.5243-5263
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• 2017

Data center networks provide critical bandwidth for the continuous growth of cloud computing, multimedia storage, data analysis and other businesses. The problem of low link bandwidth utilization in data center network is gradually addressed in more hot fields. However, the current scheduling strategies applied in data center network do not adapt to the real-time dynamic change of the traffic in the network. Thus, they fail to distribute resources due to the lack of intelligent management. In this paper, we present an improved adaptive traffic scheduling strategy utilizing the simulated annealing genetic algorithm (SAGA). Inspired by the idea of software defined network, when a flow arrives, our strategy changes the bandwidth demand dynamically to filter out the flow. Then, SAGA distributes the path for the flow by considering the scheduling of the different pods as well as the same pod. It is implemented through software defined network technology. Simulation results show that the bisection bandwidth of our strategy is higher than state-of-the-art mechanisms.

• Industrial Engineering and Management Systems
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• 제9권3호
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• pp.227-241
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• 2010

A memory module industry's supply chain usually consists of multiple manufacturing sites and multiple distribution centers. In order to fulfill the variety of demands from downstream customers, production planners need not only to decide the order allocation among multiple manufacturing sites but also to consider memory module industrial characteristics and supply chain constraints, such as multiple material substitution relationships, capacity, and transportation lead time, fluctuation of component purchasing prices and available supply quantities of critical materials (e.g., DRAM, chip), based on human experience. In this research, a directed graph-based supply network planning (DGSNP) model is developed for memory module industry. In addition to multi-site order allocation, the DGSNP model explicitly considers production planning for each manufacturing site, and purchasing planning from each supplier. First, the research formulates the supply network's structure and constraints in a directed-graph form. Then, a proposed genetic algorithm (GA) solves the matrix form which is transformed from the directed-graph model. Finally, the final matrix, with a calculated maximum profit, can be transformed back to a directed-graph based supply network plan as a reference for planners. The results of the illustrative experiments show that the DGSNP model, compared to current memory module industry practices, determines a convincing supply network planning solution, as measured by total profit.

• Journal of Communications and Networks
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• 제18권1호
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• pp.112-122
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• 2016

Cognitive radio (CR) is considered an attractive technology to deal with the spectrum scarcity problem. Multi-radio access technology (multi-RAT) can improve network capacity because data are transmitted by multiple RANs (radio access networks) concurrently. Thus, multi-RAT embedded in a cognitive radio network (CRN) is a promising paradigm for developing spectrum efficiency and network capacity in future wireless networks. In this study, we consider a new CRN model in which the primary user networks consist of heterogeneous primary users (PUs). Specifically, we focus on the energy-efficient resource allocation (EERA) problem for CR users with a special location coverage overlapping region in which heterogeneous PUs operate simultaneously via multi-RAT. We propose a two-tier crossover genetic algorithm-based search scheme to obtain an optimal solution in terms of the power and bandwidth. In addition, we introduce a radio environment map to manage the resource allocation and network synchronization. The simulation results show the proposed algorithm is stable and has faster convergence. Our proposal can significantly increase the energy efficiency.

• 디지털콘텐츠학회 논문지
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• 제2권1호
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• pp.51-61
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• 2001

퍼지 제어기(FLC)는 고전적인 제어기 보다 외란에 강하고 초기치에 대한 과도응답도 우수할 뿐만 아니라 시스템의 수학적 모델과 파라메터 값을 알지 못하더라도 적절한 제어가 가능하다. 그러나 퍼지 제어기의 제어 규칙 생성은 전문가의 경험과 일단 결정된 제어 규칙은 고정됨으로 인해 제어 시스템의 환경변화에 적응할 수 없는 한계성이 있다. 또한 제어기의 출력값은 미세한 오차를 가지곤 있어 정확한 목표 값에 수렴할 수 없다. 이러한 미세한 오차를 없애기 위하여 여러 가지 방법이 연구되고 있는데, 본 논문에서는 FLC에 NN(Neural Network)과 GA(Genetic Algorithm)를 결합한 GA-FNNIC(유전알고리즘-퍼지 신경망 지능 제어기 : Genetic Algorithm - Fuzzy Neural network Intelligence Controller)를 제안한다. 제안된 GA-FNNIC와 FLC 제어기 간의 출력 특성, 수렴속도, 과도특성과 상승시간에 대해 비교 분석하고, 최종적으로 본 GA-FNNIC가 오차없이 목표치에 정확하게 수렴하는 것을 보인다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.460-463
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• 1999

In this paper, we proposed an block-based evolvable neural network(BENN). The BENN can optimize it＇s structure and weights simultaneously. It can be easily implemented by FPGA whose connection and internal functionality can be reconfigured. To solve the local minima problem that is caused gradient descent learning algorithm, genetic algorithms are applied for optimizing the proposed evolvable neural network model.

• 한국항공우주학회지
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• 제37권5호
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• pp.425-432
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• 2009

본 연구에서는 오일러 CFD코드에서 얻은 데이터를 이용하여 2차원 익형의 비정상 공력하중을 모델링하고 예측할 수 있는 신경망의 능력을 확인하였다. 신경망 모델은 감독자 관리 학습을 기반으로 하여 르벤버그-마쿼트 알고리즘, 그리고 여기에 유전알고리즘을 결합시킨 혼합형 유전알고리즘을 사용하여 구성하고 각 경우에 대하여 그 효율성을 비교 분석하였다. 복잡한 시스템을 모사하는 신경망을 학습시키는 데는 혼합형유전알고리즘이 더 효율적이라는 것을 보였으며 신경망모델에 의한 2차원 익형의 비정상공력하중 예측결과 실제 수치결과와 비교적 정확하게 일치하여 신경망 모델이 축소모델로서의 기능을 발휘하는 것을 입증하였다.

• 한국지능시스템학회논문지
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• 제11권3호
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• pp.215-222
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• 2001

신경망은 선형 시스템뿐만 아니라 비선형 시스템에 있어서도 탁월한 모델링 및 예측 성능을 갖고 있다. 하지만 좋은 성능을 갖는 신경망을 구현하기 위해서는 최적화 해야할 파라미터들이 있다. 은닉층의 뉴런의 수, 학습율, 모멘텀, 학습오차 등이 그것인데 이러한 파라미터들은 경험에 의해서, 또는 문헌들에서 제시하는 값들을 선택하여 사용하는 것이 일반적인 경향이다. 하지만 신경망의 전체적인 성능은 이러한 파라미터들의 값에 의해서 결정되기 때문에 이 값들의 선택은 보다 체계적인 방법을 사용하여 구하여야 한다. 본 논문은 유전 알고리즘을 이용하여 이러한 신경망 파라미터들의 최적 값을 찾는데 목적이 있다. 유전 알고리즘을 이용하여 찾은 파라미터들을 사용하여 학습된 신경망의 학습오차와 예측오차들을 심플렉스 알고리즘을 이용하여 찾는 파라미터들을 사용하여 학습된 신경망의 오차들과 비교하여 본 결과 유전 알고리즘을 이용하여 찾을 파라미터들을 이용했을 때의 신경망의 성능이 더욱 우수함을 알 수 있다.