• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.333-347
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• 2022

Purpose: The purpose of this study was intended to examine the importance and role of social capital in the local community, and empirically identify the relationships among the social capital, community spirit, resident satisfaction, and community participation of the residents of urban-rural complex areas. Methods: This study conducted a survey was conducted with residents of the urban-rural complex areas to collect data, and the data were statistically and empirically analyzed to verify the hypothesis. Results: As a result of the study, first, networks and trust as local social capital were found to have positive effects on local attachment. Second, networks and trust were found to have positive effects on social ties. Third, local attachment and social ties were found to have positive effects on resident satisfaction. Finally, community participation was found to have moderating effects on the relationship between social ties and resident satisfaction. Conclusion: Through this study, the importance and role of local social capital in urban-rural complex areas, where regional problems are highly likely to occur, were reviewed, and basic data necessary to solve the social problems at hand in urban-rural complex areas and promote continuous development were provided can be said to be the significance of this study.

• Advances in materials Research
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• v.6 no.2
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• pp.169-184
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• 2017

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism. This method works by forming of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena can arise and adversely affect the surface integrity of EDMed workpieces. These have to be taken into account and studied in order to optimize the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique that can provide reliable results and readily, be integrated into several technological areas. In this paper, we use an ANN, namely, the multi-layer perceptron and the back propagation network (BPNN) to predict the mean surface roughness of electro-discharge machined surfaces. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for getting a reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components.

• Clean Technology
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• v.17 no.4
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• pp.406-411
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• 2011

A detailed database of waste heat is built to propose energy exchange networks to recover waste energy in Pohang Steel Industrial Complex. A visualized technique is used to figure out the status of waste heat energy and to suggest potential energy exchange networks. Several energy networks are proposed in terms of temperature level, the amount of available energy, distance, and construction cost. A simple economical assessment is applied to the energy exchange networks which have higher economic potential. Their average payback period is estimated to be 2.8 years. The total amount of energy saving by constructing the proposed energy exchange networks is 4,778 TOE per year. It corresponds to 11,160 ton $CO_2$ reduction with the assumption that the recycled waste energy replaces the use of LNG in energy-demanding companies.

• KIISE Transactions on Computing Practices
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• v.22 no.8
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• pp.357-362
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• 2016

A social network or bio network is one of the complex networks that are formed by connecting specific relationships between interacting objects. Usually, these networks consist of community structures. Automatically detecting the structures is an important technique to understand and control the interaction objects. However, the topologies and structures of the networks change by interactions of the objects, with respect to time. Conventional techniques for finding the community structure have a high computational complexity. Additionally, the methods inefficiently deal with repeated computation concerning graph operation. In this paper, we propose an incremental technique for detecting interaction-based communities in dynamic networks. The proposed technique is able to efficiently find the communities, since there is an awareness of changed objects from the previous network, and it can incrementally reuse the previous community structure.

• Journal of the military operations research society of Korea
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• v.5 no.1
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• pp.155-162
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• 1979

GERT modeling is in a dynamic stage of development. One of the most exciting and useful new developments in GERT modeling and Simulation is the modeling technology and computer package called Q-GERT. As the name implies, this provides the capability to analyze complex networks of queueing systems. The modeling approach is quite similar to GERT, but includes queue nodes called 'Select' nodes, which allow a considerable amount of logic to be included in the analysis of complex networks of multichannel, multiphase queueing systems should find the Q-OERT package of considerable interest.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.3
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• pp.13-19
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• 2016

Element-focused network analysis method for truss structure is proposed. The propagation process of loads from external loads to connected other elements is similar to that of connections between nodes in accordance with attachment rule in a network. Here nodes indicate elements in a truss structure and edges represent propagated loads. Therefore, the flows of loads in a truss structure can be calculated using the network analysis method, and consequently the structure can also be analyzed. As a first step to analyze a truss structure as a network, we propose a local load transfer rule in accordance with the topology of elements, and then analyze the loads of the truss elements. Application of this method reveal that the internal loads and reactions caused by external loads can be accurately estimated. Consequently, truss structures can be considered as networks and network analysis method can be applied to further complex truss structures.

• Journal of Industrial Technology
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• v.22 no.A
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• pp.73-82
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• 2002

It is difficult to find the shortest paths using existing algorithms (Dijkstra, Floyd-Warshall algorithm, and etc) in high complex and large size real traffic networks The objective of this paper is to develop an evolution program to find the multiple shortest paths within reasonable time in these networks including turn-restrictions, U-turns, and etc.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.442-448
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• 2000

The conventional control of dynamic systems needs accurate mathematical modeling of control systems. But the modeling of dynamic systems require very complex computation process due to complex state equation and many control parameters. Accordingly this paper proposes a state space identification model of the dynamic system using neural networks. The Gauss-Newton method is used to train the proposed neural network and the effectiveness of proposed method is verified through the computer simulation of the Seesaw system identification problem.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.11
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• pp.1464-1471
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• 1999

This paper introduces complex-valued competitive learning neural network for spatio-temporal pattern recognition. There have been quite a few neural networks for spatio-temporal pattern recognition. Among them, recurrent neural network, TDNN, and avalanche model are acknowledged as standard neural network paradigms for spatio-temporal pattern recognition. Recurrent neural network has complicated learning rules and does not guarantee convergence to global minima. TDNN requires too many neurons, and can not be regarded to deal with spatio-temporal pattern basically. Grossberg's avalanche model is not able to distinguish long patterns, and has to be indicated which layer is to be used in learning. In order to remedy drawbacks of the above networks, unsupervised competitive learning using complex umber is proposed. Suggested neural network also features simultaneous recognition, time-shift invariant recognition, stable categorizing, and learning rate modulation. The network is evaluated by computer simulation with randomly generated patterns.

• Steel and Composite Structures
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• v.49 no.1
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• pp.31-45
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• 2023

The network theory studies interconnection between discrete objects to find about the behavior of a collection of objects. Also, nanomaterials are a collection of discrete atoms interconnected together to perform a specific task of mechanical or/and electrical type. Therefore, it is reasonable to use the network theory in the study of behavior of super-molecule in sport nano-scale. In the current study, we aim to examine vibrational behavior of spherical nanostructured composite with different geometrical and materials properties. In this regard, a specific shear deformation displacement theory, classical elasticity theory and analytical solution to find the natural frequency of the spherical nano-composite sport structure equipment. The analytical results are validated by comparison to finite element (FE). Further, a detail comprehensive results of frequency variations are presented in terms of different parameters. It is revealed that the current methodology provides accurate results in comparison to FE results. On the other hand, different geometrical and weight fraction have influential role in determining frequency of the structure.