• Nuclear Engineering and Technology
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• 제46권5호
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• pp.641-654
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• 2014

A new computer code, named CORONA (Core Reliable Optimization and thermo-fluid Network Analysis), was developed for the core thermo-fluid analysis of a prismatic gas cooled reactor. The CORONA code is targeted for whole-core thermo-fluid analysis of a prismatic gas cooled reactor, with fast computation and reasonable accuracy. In order to achieve this target, the development of CORONA focused on (1) an efficient numerical method, (2) efficient grid generation, and (3) parallel computation. The key idea for the efficient numerical method of CORONA is to solve a three-dimensional solid heat conduction equation combined with one-dimensional fluid flow network equations. The typical difficulties in generating computational grids for a whole core analysis were overcome by using a basic unit cell concept. A fast calculation was finally achieved by a block-wise parallel computation method. The objective of the present paper is to summarize the motivation and strategy, numerical approaches, verification and validation, parallel computation, and perspective of the CORONA code.

• 전자공학회논문지A
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• 제31A권6호
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• pp.159-168
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• 1994

Stochastic computation is adopted to reduce the silicon area of the multipliers in implementing neural network in VLSI. In addition to this advantage, the stochastic computation has inherent random errors which is required for implementing Boltzmann machine. This random noise is useful for the simulated annealing which is employed to achieve the global minimum for the Boltzmann Machine. In this paper, we propose a method to implement the Boltzmann machine with stochastic computation and discuss the addition problem in stochastic computation and its simulated annealing in detail. According to this analysis Boltzmann machine using stochastic computation is suitable for the pattern recognition/completion problems. We have verified these results through the simulations for XOR, full adder and digit recognition problems, which are typical of the pattern recognition/completion problems.

• Smart Structures and Systems
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• 제32권1호
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• pp.49-59
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• 2023

A bridge transportation network supplies products from various source nodes to destination nodes through bridge structures in a target region. However, recent frequent earthquakes have caused damage to bridge structures, resulting in extreme direct damage to the target area as well as indirect damage to other lifeline structures. Therefore, in this study, a surrogate model-based comprehensive framework to estimate the seismic resilience of bridge transportation networks is proposed. For this purpose, total system travel time (TSTT) is introduced for accurate performance indicator of the bridge transportation network, and an artificial neural network (ANN)-based surrogate model is constructed to reduce traffic analysis time for high-dimensional TSTT computation. The proposed framework includes procedures for constructing an ANN-based surrogate model to accelerate network performance computation, as well as conventional procedures such as direct Monte Carlo simulation (MCS) calculation and bridge restoration calculation. To demonstrate the proposed framework, Pohang bridge transportation network is reconstructed based on geographic information system (GIS) data, and an ANN model is constructed with the damage states of the transportation network and TSTT using the representative earthquake epicenter in the target area. For obtaining the seismic resilience curve of the Pohang region, five epicenters are considered, with earthquake magnitudes 6.0 to 8.0, and the direct and indirect damages of the bridge transportation network are evaluated. Thus, it is concluded that the proposed surrogate model-based framework can efficiently evaluate the seismic resilience of a high-dimensional bridge transportation network, and also it can be used for decision-making to minimize damage.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 1998년도 추계학술발표논문집
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• pp.202-207
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• 1998

The network reliability is to be computed in terms of the terminal reliability. The computation of a terminal reliability is started with a Boolean sum of products expression corresponding to simple paths of the pair of nodes. This expression is then transformed into another equivalent expression to be a Disjoint Sum of Products form. But this computation of the terminal reliability obviously does not consider the communication between any other nodes but for the source and the sink. In this paper, we derive the overall network reliability which all other remaining nodes. For this, we propose a method to make the SOP disjoint for deriving the network reliability expression from the system success expression using the modified Sheinman's method. Our method includes the concept of spanning trees to find the system success function by the Cartesian products of vertex cutsets.

• 농촌계획
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• 제5권1호
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• pp.20-25
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• 1999

Our interest in this paper is in the efficient computation of a good low bound for the traveling salesman problem and is in the application of a network problem in agriculture. We base our approach on a relatively new formulation of the TSP as a two-commodity network flow problem. By assigning Lagrangian multipliers to certain constraints and relaxing them, the problem separates into two single-commodity network flow problems and an assignment problem, for which efficient algorithms are available.

• 한국정보과학회논문지:소프트웨어및응용
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• 제32권4호
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• pp.247-257
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• 2005

일반적으로 네트워크 설계 문제는 네트워크의 크기가 늘어남에 따라 지수적으로 복잡도가 증가하여 전통적인 방법으로는 풀이하기 힘든 NP-hard 조합 최적화 문제 중의 하나로 분류될 수 있다. 본 논문에서는 네트워크 신뢰도 제약을 고려하면서 네트워크 구축비용을 효과적으로 최소화하는, 높은 신뢰도의 네트워크 토폴로지 설계 문제를 풀기 위해 스패닝 트리를 효율적으로 표현할 수 있는 Prufer수(PN) 기반의 진화 연산법과 2-연결성을 고려하는 휴리스틱 방법으로 구성된 두 단계의 효율적인 해법을 제안한다. 즉, 먼저 스패닝 트리를 찾아내기 위해 진화 연산법 중에 보편적으로 널리 알려져 있는 유전자 알고리즘(GA)을 이용하고 그 다음으로 첫 번째 단계에서 발견한 스패닝 트리에 대해 최적의 네트워크 토폴로지를 찾기 위해서 2-연결성을 고려한 휴리스틱 방법을 적용한다. 마지막으로 수치예의 결과를 통해 제안한 해법의 성능에 대해서 살펴보도록 한다.

• 전자공학회논문지C
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• 제34C권1호
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• pp.28-41
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• 1997

This paper describes a parallel implementation of a neurla network ensemble developed for object recognition on the connection machine CM-2. The implementation ensures that multiple networks are implemented simultaneously starting from different initial weights and all training samples are applied to each network by one sample per a copy of each network. When compared with a sequential implementation, this accelerates the computation speed by O(N.m.n) where N, m, and n are the network, respectively. The speedup in the computation time and the convergence characteristics of sthe modified backpropagation learning precedure were evaluated by two-dimensional object recognition problem.

• 전자공학회논문지B
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• 제32B권3호
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• pp.48-56
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• 1995

This paper presents a method of implementing efficient optical flow estimation for dynamic scene analysis using the hierarchical Hopfield neural networks. Given the two consequent inages, Zhou and Chellappa suggested the Hopfield neural network for computing the optical flow. The major problem of this algorithm is that Zhou and Chellappa's network accompanies self-feedback term, which forces them to check the energy change every iteration and only to accept the case where the lower the energy level is guaranteed. This is not only undesirable but also inefficient in implementing the Hopfield network. The another problem is that this model cannot allow the exact computation of optical flow in the case that the disparities of the moving objects are large. This paper improves the Zhou and Chellapa's problems by modifying the structure of the network to satisfy the convergence condition of the Hopfield model and suggesting the hierarchical algorithm, which enables the computation of the optical flow using the hierarchical structure even in the presence of large disparities.

• 한국콘텐츠학회논문지
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• 제6권12호
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• pp.136-146
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• 2006

무선 센서 네트워크의 노드에서 측정된 데이터는 노드의 전송 거리의 제한 때문에 멀티 홉을 통해 베이스 노드에 전송된다. 또한 노드의 에너지가 한정되어 있기 때문에 무선 센서 네트워크의 수명을 연장하기 위해서는 각 노드의 에너지 소모를 가능한 균일하게 하여야 한다. Level based MultiPath Routing (LMPR)은 베이스 노드로 부터의 거리에 해당하는 노드의 레벨을 기반으로 무선 센서 네트워크를 자가 구축하고 데이터 처리 및 전송 부하를 각 센서 노드에 분산시키는 무선 센서 네트워크 라우팅 프로토콜이다. 본 논문에서는 TinyOS 기반으로 LMPR를 구현하고 실험을 통해 무선 센서 네트워크상에서 LMPR의 성능을 측정하였다. 실험 결과 LMPR이 최소 비용 방식으로 선택된 단일 경로로 데이터를 전송하는 프로토콜보다 데이터 처리 및 전송 부하를 약 4.6배 분산시켰다. LMPR 을 사용하여 데이터 처리 및 전송 부하를 각 노드에 분산시켜 TinyOS를 기반으로 구성된 무선 센서 네트워크의 수명을 연장시킬 수 있을 것으로 기대한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권11호
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• pp.3711-3730
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• 2014

Cognitive radio networks (CRNs) have emerged as a promising solution to enhance spectrum utilization by using unused or less used spectrum in radio environments. The basic idea of CRNs is to allow secondary users (SUs) access to licensed spectrum, under the condition that the interference perceived by the primary users (PUs) is minimal. In CRNs, the channel availability is uncertainty due to the existence of PUs, resulting in intermittent communication. Transmission control protocol (TCP) performance may significantly degrade in such conditions. To address the challenges, some transport protocols have been proposed for reliable transmission in CRNs. In this paper we survey the state-of-the-art transport protocols for CRNs. We firstly highlight the unique aspects of CRNs, and describe the challenges of transport protocols in terms of PU behavior, spectrum sensing, spectrum changing and TCP mechanism itself over CRNs. Then, we provide a summary and comparison of existing transport protocols for CRNs. Finally, we discuss several open issues and research challenges. To the best of our knowledge, our work is the first survey on transport protocols for CRNs.