• Electrical & Electronic Materials
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• v.10 no.7
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• pp.651-658
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• 1997

In this paper, effect of interpenetrating polymer network(IPN) introduction on the dielectric properties, heat proof properties, internal structure and defects of the Epoxy/SiO$_2$composite materials, were investigated. we reported a relation between network structures and electrical properties, especially dielectric characteristics with variation of network structures for epoxy composite materials. According to experimental results, the specimens which have single network structures have lower dielectric constant than interpenetrating polymer network(IPN) specimens, but have relatively larger dependency to variation of temperature and frequency. It was confirmed that change of structures is attained by introducing of IPN to insulating materials. Therefore it is counted that introduction of multiple structure including IPN is necessary to improve heat proof and electrical properties.

• Smart Structures and Systems
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• v.32 no.1
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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.

• Computers and Concrete
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• v.32 no.6
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• pp.615-623
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• 2023

Crack detection is an essential method to ensure the safety of dam concrete structures. Low-quality crack images of dam concrete structures limit the application of neural network methods in crack detection. This research proposes a modified attentional mechanism model to reduce the disturbance caused by uneven light, shadow, and water spots in crack images. Also, the focal loss function solves the small ratio of crack information. The dataset collects from the network, laboratory and actual inspection dataset of dam concrete structures. This research proposes a novel method for crack detection of dam concrete structures based on the U-Net neural network, namely AF-UNet. A mutual comparison of OTSU, Canny, region growing, DeepLab V3+, SegFormer, U-Net, and AF-UNet (proposed) verified the detection accuracy. A binocular camera detects cracks in the experimental scene. The smallest measurement width of the system is 0.27 mm. The potential goal is to achieve real-time detection and localization of cracks in dam concrete structures.

• Smart Structures and Systems
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• v.24 no.5
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• pp.587-595
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• 2019

Deformation measurement of large scale structures, such as the ground beds of high-rise buildings, tunnels, bridge, and railways, are important for insuring service quality and safety. The pose-relay videometrics method and displacement-relay videometrics method have already presented to measure the pose of non-intervisible objects and vertical subsidence of unstable areas, respectively. Both methods combine the cameras and cooperative markers to form the camera series networks. Based on these two networks, we propose two novel videometrics methods with closed-loop camera series network for deformation measurement of large scale structures. The closed-loop camera series network offers "closed-loop constraints" for the camera series network: the deformation of the reference points observed by different measurement stations is identical. The closed-loop constraints improve the measurement accuracy using camera series network. Furthermore, multiple closed-loops and the flexible combination of camera series network are introduced to facilitate more complex deformation measurement tasks. Simulated results show that the closed-loop constraints can enhance the measurement accuracy of camera series network effectively.

• 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.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.399-414
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• 2013

The smart sensor technology has opened new horizons for assessing and monitoring structural health of civil infrastructure. Smart sensor's unique features such as onboard computation, wireless communication, and cost effectiveness can enable a dense network of sensors that is essential for accurate assessment of structural health in large-scale civil structures. While most research efforts to date have been focused on realizing wireless smart sensor networks (WSSN) on bridge structures, relatively less attention is paid to applying this technology to buildings. This paper presents a decentralized damage detection using the WSSN for building structures. An existing flexibility-based damage detection method is extended to be used in the decentralized computing environment offered by the WSSN and implemented on MEMSIC's Imote2 smart sensor platform. Numerical simulation and laboratory experiment are conducted to validate the WSSN for decentralized damage detection of building structures.

• Journal of Ocean Engineering and Technology
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• v.23 no.6
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• pp.136-145
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• 2009

There are two purposes for the marine structures used for fossil fuel: transporting huge amounts of crude oil and petroleum products and producing petroleum resources on the ocean in an isolated operational environment. Both types of structures are exposed to dangerous situations by sea conditions. Such marine structures are greatly affected by ocean climate conditions and its changes. Because of such ocean climate changes, it has been necessary to monitor marine structures. This research discusses the difficulties with adopting a new methodology based on a ubiquitous sensor network and develops an optimized sensor network management system design for a marine structure.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1996.05a
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• pp.202-205
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• 1996

In this paper it is researched a relation between network structures and electrical properties - especially dielectric characteristics with changing of network structure. It is resulted that the specimens which have single network structures have smaller dielectric loss than SIN specimens but have relatively larger dependency to variation of temperature and frequency. For that reason formation of structures is attained by introducing of SIN to insulating materials. therefore it is counted that introduction of multiple structure including SIN is necessary to improve heat proof and electrical properties.

• Structural Engineering and Mechanics
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• v.26 no.3
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• pp.251-262
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• 2007

This paper examines the application of artificial neural networks (ANN) to the response prediction of geometrically nonlinear truss structures. Two types of analysis (deterministic and probabilistic analyses) are considered. A three-layer feed-forward backpropagation network with three input nodes, five hidden layer nodes and two output nodes is firstly developed for the deterministic response analysis. Then a back propagation training algorithm with Bayesian regularization is used to train the network. The trained network is then successfully combined with a direct Monte Carlo Simulation (MCS) to perform a probabilistic response analysis of geometrically nonlinear truss structures. Finally, the proposed ANN is applied to predict the response of a geometrically nonlinear truss structure. It is found that the proposed ANN is very efficient and reasonable in predicting the response of geometrically nonlinear truss structures.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.321-329
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• 2007

This paper reports on the application of network analysis approaches to investigate the characteristics of graph representations of Japanese word associations. Two semantic networks are constructed from two separate Japanese word association databases. The basic statistical features of the networks indicate that they have scale-free and small-world properties and that they exhibit hierarchical organization. A graph clustering method is also applied to the networks with the objective of generating hierarchical structures within the semantic networks. The method is shown to be an efficient tool for analyzing large-scale structures within corpora. As a utilization of the network clustering results, we briefly introduce two web-based applications: the first is a search system that highlights various possible relations between words according to association type, while the second is to present the hierarchical architecture of a semantic network. The systems realize dynamic representations of network structures based on the relationships between words and concepts.