• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.215-225
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• 1996

For an undirected stochastic network G, the 2-terminal reliability of G, R(G) is the probability that the specific two nodes (called as terminal nodes) are connected in G. A. typical network reliability problem is to compute R(G). It has been shown that the computation problem of R(G) is NP-hard. So, any algorithm to compute R(G) has a runngin time which is exponential in the size of G. If by some means, the problem size, G is reduced, it can result in immense savings. The means to reduce the size of the problem are the reliability preserving reductions and graph decompositions. We introduce a net set of reliability preserving reductions : the $K^{4}$ (complete graph of 4-nodes)-chain reductions. The total number of the different $K^{4}$ types in R(G), is 6. We present the reduction formula for each $K^{4}$ type. But in computing R(G), it is possible that homeomorphic graphs from $K^{4}$ occur. We devide the homemorphic graphs from $K^{4}$ into 3 types. We develop the reliability preserving reductions for s types, and show that the remaining one is divided into two subgraphs which can be reduced by $K^{4}$-chain reductions 7 polygon-to-chain reductions.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2001.09a
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• pp.27-30
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• 2001

Flow and transport at fracture intersections, and their effects on network scale transport, are investigated in three-dimensional random fracture networks. Fracture intersection mixing rules complete mixing and streamline routing are defined in terms of fluxes normal to the intersection line between two fractures. By analyzing flow statistics and particle transfer probabilities distributed along fracture intersections, it is shown that for various network structures with power law size distributions of fractures, the choice of intersection mixing rule makes comparatively little difference in the overall simulated solute migration patterns. The occurrence and effects of local flows around an intersection (local flow cells) are emphasized. Transport simulations at fracture intersections indicate that local flow circulations can arise from variability within the hydraulic head distribution along intersections, and from the internal no flow condition along fracture boundaries. These local flow cells act as an effective mechanism to enhance the nondiffusive breakthrough tailing often observed in discrete fracture networks. It is shown that such non-Fickian (anomalous) solute transport can be accounted for by considering only advective transport, in the framework of a continuous time random walk model. To clarify the effect of forest environmental changes (forest type difference and clearcut) on water storage capacity in soil and stream flow, watershed had been investigated.

• Proceedings of the KOR-KST Conference
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• 1995.05a
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• pp.51-59
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• 1995

It is an objective of Shanghai urban development to quickly build Shanghai into one of the international centres of economy, finance and trade. To fulfil the objective, a rational urban general layout and a satisfactory traffic network are needed. As a metropolis with complete urban functions and specified geographical position, Shanghai must develop a perfect citytown system that best suits its composite urban distribution. In planning the central city, the principle of "open and multi-centred" and "optimization of land use" should be taken into consideration. To build a satisfactory urban traffic network, emphasis should be laid upon the construction of deefwater wharf, air-field and inforation centre. In addition, determination should be made to build a high-speed traffic means including high speed railroad and express highway so that a public traffic network is realised on and above ground, and underground. A solution of traffic preblem in Shanghai lies in good understanding of traffic policy and strenthening of strategic management combined with a rational layout of traffic circulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.3
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• pp.339-347
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• 1993

The channel assignment problem in a mobile communication system is a NP-complete combinatorial optimization problem, in which the calculation time increases exponentially as the range of the problem is extended. This paper adapts a conventional Hopfield neural network model to the channel assignment problem to relieve the calculation time by means of the parallelism supplied from the neural network. In the simulation study, we checked the feasability of such a parallel method for the fixed channel assignment with uniform, and nouniform channel requirements, and for the dynamic channel assignment with considering continously varying channel requirements.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5A
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• pp.317-324
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• 2012

Physical layer network coding (PNC) was first introduce by Zhang et al. for two-way relay channels (TWRCs). By utilizing the PNC, we can complete two-way communications within two time slots, instead of three time slots required in non-PNC systems. Recently, the upper and lower bounds for a bit error rate (BER) of PNC have been analyzed for fading channels. In this paper, we derive an exact BER of the PNC for the TWRC over fading channels. We determine decision regions based on the nearest neighbor rule and partition them into several wedge areas to apply the Craig's polar coordinate form for computing the BER. We confirm that our derived analysis accurately matches with the simulation results.

• International Journal of Quality Innovation
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• v.3 no.2
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• pp.113-131
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• 2002

The relationships among multi-dimensional data (such as medical examination data) with ambiguity and variation are difficult to explore. The traditional approach to building a data classification system requires the formulation of rules by which the input data can be analyzed. The formulation of such rules is very difficult with large sets of input data. This paper first describes two classification approaches using back-propagation (BP) neural network and Mahalanobis distance (MD) classifier, and then proposes two classification approaches for multi-dimensional feature selection. The first one proposed is a feature selection procedure from the trained back-propagation (BP) neural network. The basic idea of this procedure is to compare the multiplication weights between input and hidden layer and hidden and output layer. In order to simplify the structure, only the multiplication weights of large absolute values are used. The second approach is Mahalanobis-Taguchi system (MTS) originally suggested by Dr. Taguchi. The MTS performs Taguchi's fractional factorial design based on the Mahalanobis distance as a performance metric. We combine the automatic thresholding with MD: it can deal with a reduced model, which is the focus of this paper In this work, two case studies will be used as examples to compare and discuss the complete and reduced models employing BP neural network and MD classifier. The implementation results show that proposed approaches are effective and powerful for the classification.

• Korean Journal of Hydrosciences
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• v.5
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• pp.1-16
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• 1994

In this paper distributed models for simulating spatially and temporally varied moving storm in a watershed were developed. The complete simulation in a watershed is achieved through two sequential flow simulations which are overland flow simulation and channel network flow simulation. Two dimensional continuity equation and momentum equation of kinematic approximation were used in the overland flow simulation. On the other hand, in the channel network simulation two types of governing equations which are one dimensional continuity and momentum equations between two adjacent sections in a channel, and continuity and energy equations at a channel junction were applied. The finite difference formulations were used in the channel network model. Macks Creek Experimental Watershed in Idaho, USA was selected as a target watershed and the moving storm on August 23, 1965, which continued from 3:30 P.M. to 5:30 P.M., was utilized. The rainfall intensity fo the moving storm in the watershed was temporally varied and the storm was continuously moved from one place to the other place in a watershed. Furthermore, runoff parameters, which are soil types, vegetation coverages, overland plane slopes, channel bed slopes and so on, are spatially varied. The good agreement between the hydrograph simulated using distributed models and the hydrograph observed by ARS are Shown. Also, the conservations of mass between upstreams and downstreams at channel junctions are well indicated and the wpatial and temporal vaiability in a watershed is well simulated using suggested distributed models.

• Advances in robotics research
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• v.1 no.1
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• pp.101-126
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• 2014

In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions are achieved by a phase switching network (PSN) module. The combination of these modules generates various locomotion patterns and a reactive obstacle avoidance behavior. The behavior is driven by sensor inputs, to which additional neural preprocessing networks are applied. The complete neural circuitry is developed and tested using a physics simulation environment. This study verifies that the neural modules can serve a general purpose regardless of the robot's specific embodiment. We also believe that our neural modules can be important components for locomotion generation in other complex robotic systems or they can serve as useful modules for other module-based neural control applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3151-3168
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• 2021

Internet of Things (IoT) connects several objects with embedded sensors and they are capable of exchanging information between devices to create a smart environment. IoT smart devices have limited resources, such as batteries, computing power, and bandwidth, but comprehensive sensing causes severe energy restrictions, lowering data quality. The main objective of the proposal is to build a hybrid protocol which provides high data quality and reduced energy consumption in IoT sensor network. The hybrid protocol gives a flexible and complete solution for sensor selection problem. It selects a subset of active sensor nodes in the network which will increase the data quality and optimize the energy consumption. Since the unused sensor nodes switch off during the sensing phase, the energy consumption is greatly reduced. The hybrid protocol uses Dijkstra's algorithm for determining the shortest path for sensing data and Ant colony inspired variable path selection algorithm for selecting active nodes in the network. The missing data due to inactive sensor nodes is reconstructed using enhanced belief propagation algorithm. The proposed hybrid method is evaluated using real sensor data and the demonstrated results show significant improvement in energy consumption, data utility and data reconstruction rate compared to other existing methods.

• Molecules and Cells
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• v.42 no.2
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• pp.166-174
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• 2019

Bacterial species in the genus Xanthomonas infect virtually all crop plants. Although many genes involved in Xanthomonas virulence have been identified through molecular and cellular studies, the elucidation of virulence-associated regulatory circuits is still far from complete. Functional gene networks have proven useful in generating hypotheses for genetic factors of biological processes in various species. Here, we present a genome-scale co-functional network of Xanthomonas oryze pv. oryzae (Xoo) genes, XooNet (www.inetbio.org/xoonet/), constructed by integrating heterogeneous types of genomics data derived from Xoo and other bacterial species. XooNet contains 106,000 functional links, which cover approximately 83% of the coding genome. XooNet is highly predictive for diverse biological processes in Xoo and can accurately reconstruct cellular pathways regulated by two-component signaling transduction systems (TCS). XooNet will be a useful in silico research platform for genetic dissection of virulence pathways in Xoo.