• 품질경영학회지
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• 제50권3호
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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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• 제6권2호
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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.

• 청정기술
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• 제17권4호
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• pp.406-411
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• 2011

포항철강산업단지에서 발생하는 폐열을 회수하기 위하여 주요 기업의 폐에너지 발생 규모에 대한 데이터베이스를 설문조사를 통해 구축하고, 가시적인 기법을 적용하여 에너지 교환망 구축방안을 고려하였다. 폐열의 온도 수준, 유효에너지 발생량, 폐에너지 발생 기업과 예상 수요 기업의 거리, 구축비용 등의 관점에서 잠재성이 높은 에너지 교환망을 제시하고 경제성을 평가하였다. 최종 제안된 4개 기업의 에너지 교환망의 투자비 회수기간은 평균 2.8년이고, 에너지 절감량은 연간 4,778 TOE로 분석되었다. 현재 사용되는 LNG를 폐열 회수로 대체하면 연간 약 11,160 $CO_2$ ton을 감축할 수 있다.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제22권8호
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• pp.357-362
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• 2016

소셜 네트워크나 바이오 네트워크는 인터랙션이 가능한 오브젝트들이 관계를 맺음으로써 형성되는 복잡 네트워크이다. 실세계에 존재하는 복잡 네트워크는 커뮤니티 구조로 구성되어 있으며, 이 커뮤니티 구조를 자동으로 발견하는 것은 그 네트워크를 제어하고 이해하는데 있어서 중요한 기술이다. 하지만 이런 네트워크들은 시간에 따라 오브젝트들의 인터랙션에 의해 그 네트워크의 구조와 위상이 불특정하게 변화한다. 이런 동적 네트워크에서 노드들 간에 인터랙션을 기반으로 한 커뮤니티 구조를 발견하는 것은 높은 시간 복잡도 연산이 요구되며, 반복된 계산을 비효율적으로 처리하는 문제점이 있다. 따라서 본 연구에서는 동적 네트워크에서 인터랙션 기반 커뮤니티 구조를 점진적으로 발견하는 기법을 제안한다. 제안하는 기법은 이전 네트워크에서 변화한 요소들을 인지하고, 이전 커뮤니티 그룹 구조를 점진적으로 재활용함으로써 효율적인 커뮤니티 발견이 가능하다.

• 한국국방경영분석학회지
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• 제5권1호
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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.

• 한국농공학회논문집
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• 제58권3호
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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.

• 산업기술연구
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• 제22권A호
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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.

• 한국지능시스템학회논문지
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• 제10권5호
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• pp.442-448
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• 2000

전통적인 동적 시스템의 제어에는 제어대상의 정확한 수학적 모델링이 필요하다. 그러나 동적 시스템의 모델링은 복잡한 상태방정식과 많은 제어파라메터들에 의해 매우 복잡한 계산과정을 필요로 한다. 그러므로, 본 논문에서는 신경회로망을 이용한 동적 시스템의 상태 공간 인식 모델을 제안하였으며, 제안된 신경회로망을 학습시키기 위하여 가우스-뉴턴 방법을 사용하였다. 본 논문에서 제안된 신경회로망 모델은 시소 시스템 인식문제를 컴퓨터 모이실험을 통해 효과적임을 보였다.

• 대한전기학회논문지:전력기술부문A
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• 제48권11호
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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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• 제49권1호
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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.