• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.412-418
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• 2000

In this paper, we will propose that HCN(n,n) gets Hamiltonian Cycles and analyze embedding among HCN(n,n) and UFN(n,n), and HFN(n,n) and In-hypercube. Further, we will prove that HCN(n,n) can be embedded into HFN(n,n) with dilation 3 and the cost for HFN(n,n) to be embedded into HCN(n,n) will be O(n), and HW(n,n) can be embedded into 2n-hypercube with dilation 3 and the cost for In-hypercube to be embedded into HFN(n,n) will be O(n).

• The KIPS Transactions:PartA
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• v.14A no.6
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• pp.327-332
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• 2007

In this paper, we will analysis embedding between $2^{2n-k}{\times}2^k$ torus and interconnection networks HFN(n,n), HCN(n,n). First, we will prove that $2^{2n-k}{\times}2^k$ torus can be embedded into HFN(n,n) with dilation 3, congestion 4 and the average dilation is less than 2. And we will show that $2^{2n-k}{\times}2^k$ torus can be embedded into HCN(n,n) with dilation 3 and the average dilation is less than 2. Also, we will prove that interconnection networks HFN(n,n) and HCN(n,n) can be embedded into $2^{2n-k}{\times}2^k$ torus with dilation O(n). These results mean so many developed algorithms in torus can be used efficiently in HFN(n,n) and HCN(n,n).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1361-1368
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• 2014

In this paper, we analyze emddings among HFCube(n,n), HCN(n,n), HFN(n,n) with lower network cost than that of Hypercube. The results are as follows. We propose that $Q_{2n}$ can be embedded into HFCube(n,n) with dilation 5, congestion 2. HCN(n,n) and HFN(n,n) are subgraphs of HFCube(n,n). HFCube(n,n) can be embedded into HFN(n,n) with dilation 3. HFCube(n,n) can be embedded into HCN(n,n) with dilation O(n). The results will be helpful to analyze several efficient properties in each interconnection network.

• The KIPS Transactions:PartA
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• v.16A no.4
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• pp.299-306
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• 2009

Hierarchical Folded HyperStar Network has lower network cost than HCN(n,n) and HFN(n,n) which are hierarchical networks with the same number of nodes. In this paper, we analyze embedding between Hierarchical Folded HyperStar HFH($C_n,C_n$) and Hypercube, HCN(n,n), HFN(n,n). The results of embedding are that HCN(n,n), HFN(n,n) and Hypercube $Q_{2n}$ can be embedded into HFH($C_n,C_n$) with expansion $\frac{C^n}{2^{2n}}$ and dilation 2, 3, and 4, respectively. Also, HFH($C_n,C_n$) can be embedded into HFN(2n,2n) with dilation 1. These results mean so many developed algorithms in Hypercube, HCN(n,n), HFN(n,n) can be used efficiently in HFH($C_n,C_n$).

• The KIPS Transactions:PartA
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• v.9A no.3
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• pp.333-340
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• 2002

Embedding is a mapping an interconnection network G to another interconnection network H. If a network G can be embedded to another network H, algorithms developed on G can be simulated on H. In this paper, we first propose a method to embed between Hierarchical Cubic Network HCN(n, n) and Hierarchical Folded-hypercube Network HFN(n, n). HCN(n, n) and HFN(n, n) are graph topologies having desirable properties of hypercube while improving the network cost, defined as degree${\times}$diameter, of Hypercube. We prove that HCN(n, n) can be embedded into HFN(n, n) with dilation 3 and congestion 2, and the average dilation is less than 2. HFN(n, n) can be embedded into HCN(n, n) with dilation 0 (n), but the average dilation is less than 2. Finally, we analyze the fault tolerance of HCN(n, n) and prove that HCN(n, n) is maximally fault tolerant.

• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.191-196
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• 2002

It is one of the important measures in the area of algorithm design that any interconnection network should be embedded into another interconnection network for the practical use of algorithm. A HCN(n, n), HFN(n, n) graph also has such a good properties of a hypercube and has a lower network cost than a hypercube. In this paper, we propose a method to embed between hypercube $Q_2n$ and HCN(n, n), HFN(n, n) graph. We show that hypercube $Q_2n$ can be embedded into an HCN(n, n) and KFN(n, n) with dilation 3, and average dilation is smaller than 2. Also, we has a result that the embedding cost, a HCN(n, n) and KFN(n, n) can be embedded into a hypercube, is O(n)

• Annual Conference of KIPS
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• 2002.11a
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• pp.111-114
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• 2002

임베딩은 어떤 연결망이 다른 연결망 구조에 포함 혹은 어떻게 연관되어 있는지를 알아보기 위해 어떤 특정한 연결망을 다른 연결망에 사상하는 것으로, 특정한 연결망에서 사용하던 여러 가지 알고리즘을 다른 연결망에서 효율적으로 이용할 수 있도록 한다. 본 논문에서는 $2^{2n-k}\times2^k$ 토러스를 HFN(n,n)에 연장율 3, 밀집율 4 로 임베딩 가능함을 보이고, HFN(n,n)을 $2^{2n-k}\times2^k$ 토러스에 연장율 O(N)으로 임베딩됨을 보인다($N=2^n$).

• Annual Conference of KIPS
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• 2000.10a
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• pp.637-640
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• 2000

본 논문에서는 하이퍼큐브보다 망비용이 개선된 HFN(n,n)과 2n-hypercube 사이의 임베딩을 분석한다. 2n-hypercube를 HFN(n,n)에 연장율 3에 임베딩 가능함을 보이고, HFN(n,n)을 2n-hypercube에 임베딩하는 비용이 O(n)임을 보인다.

• The KIPS Transactions:PartA
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• v.15A no.2
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• pp.95-100
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• 2008

In this paper, we propose a new interconnection network topology, hierarchical folded hyper-star network HFH($C_n,\;C_n$), which is based on folded hyper-star network. Our results show that the proposed hierarchical folded hyper-star network performs very competitively in comparison to folded hyper-star network and hierarchical network HCN(m,m), HFN(m,m) have been previously proposed, when diameter ${\times}$ degree is used as a network cost measure. We also investigate various topological properties of HFH($C_n,\;C_n$) including connectivity, routing algorithm, diameter, broadcasting.

• Journal of applied mathematics & informatics
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• v.42 no.2
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• pp.379-390
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• 2024

This study proposes a fuzzy inventory model for managing large-scale production, incorporating cost considerations. The model accounts for two types of expenditure scenarios-parametric and exponential. Uncertainty surrounds holding costs, setup costs, and demand rates. The approach considers a supply chain system with a complex manufacturing process, factoring in transportation costs based on the quantity of goods and distance between the supplier and retailer. The initial crisp model is then transformed into a fuzzy simulation, incorporating specific fuzzy variables affecting inventory costs. The proposed method significantly reduces overall inventory costs for the entire supply chain. Retailer demand is linked to inventory levels, and vendor/distributor storage deteriorates over time. The fuzzy condition assumes hexagonal variables for all associated factors. The study employs the signed distance method for defuzzification to determine the optimal order quantity with hexagonal fuzzy numbers. Mathematical examples are provided to illustrate the practicality of the proposed approach.