• Steel and Composite Structures
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• 제3권4호
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• pp.243-260
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• 2003

This paper reports on the development of a new composite slab system that uses a large- lipped steel channel and reinforced concrete. The advantages of this new system are that it serves as both a structural unit and an unsupported form and it has a secondary structural barrier function. A concrete pouring test was carried out for the large-lipped steel channel. Full-scale tests were carried out to assess the flexural strength-deformation characteristics and structural mechanics of the composite slab. The barrier mechanics of the steel channel concrete element (referred to as the SC subunit) of the composite slab are examined. The test results indicate that the new composite slab has excellent strength, ductility characteristics, and a structural barrier function in its SC subunit that is highly effective against severe loading.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2101-2123
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• 2023

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.502-507
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• 2008

An entire composite structure satellite is developing for the first time in Korea. All of the structure is made of CFRP-composite faced aluminum honeycomb sandwich structure. Here the random and sinusoidal spectrum analysis of the satellite was carried out by using the finite element method. The general spectrum analysis was herein performed but also the PSD (power spectrum density) function for random vibration analysis had been transformed into equivalent time domain function and then transient analysis is conducted. The time history of displacement, acceleration, stress and velocity responses with respect to the PSD input has been achieved by the time dependent transient function transformed from frequency PDS function. It enables one to perform dynamic durability analysis and then expect the life time of the composite structure. The composite faced sandwich structure's spectrum analysis of a domestically-developed satellite, STSAT-3, has been discussed in the present study.

• 대한수학회논문집
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• 제23권2호
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• pp.295-305
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• 2008

This paper demonstrates how composite-exponential-fitting interpolation rules can be constructed to fit an oscillatory function using not only pointwise values of that function but also of that functions's derivative on a closed and bounded interval of interest. This is done in the framework of exponential-fitting techniques. These rules extend the classical composite cubic Hermite interpolating polynomials in the sense that they become the classical composite polynomials as a parameter tends to zero. Some examples are provided to compare the newly constructed rules with the classical composite cubic Hermite interpolating polynomials (or recently developed interpolation rules).

• Structural Engineering and Mechanics
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• 제14권5호
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• pp.575-593
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• 2002

Based on the Mindlin plate theory, a refined discrete 15-DOF triangular laminated composite plate finite element RDTMLC with the re-constitution of the shear strain is proposed. For constituting the element displacement function, the exact displacement function of the Timoshenko's laminated composite beam as the displacement on the element boundary is used to derive the element displacements. The proposed element can be used for the analysis of both moderately thick and thin laminated composite plate, and the convergence for the very thin situation can be ensured theoretically. Numerical examples presented show that the present model indeed possesses the properties of higher accuracy for anisotropic laminated composite plates and is free of locking even for extremely thin laminated plates.

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 1999년도 추계학술발표대회 논문집
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• pp.268-271
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• 1999

Fatigue life Prediction is investigated analytically based on the fatigue modulus concept. Fatigue modulus degradation rate at any fatigue cycle was assumed as a power function of number of fatigue cycles. New stress function describing the relation of initial fatigue modulus and elastic modulus was used to account for material non-linearity at the first cycle. It was assumed that fatigue modulus at failure is proportional to applied stress level. A new fatigue life prediction equation as a function of applied stress is proposed. The prediction was verified experimentally using cross-ply carbon/epoxy laminate (CFRP) tube.

• Structural Engineering and Mechanics
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• 제70권1호
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• pp.1-11
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• 2019

This study deals with a method based on the modified bivariate gamma function for reconstructions of dynamic behavior of delaminated composite plates subjected to impact loads. The proposed bivariate gamma function is associated with micro-genetic algorithms, which is capable of solving inverse problems to determine the stiffness reduction associated with delamination. From computing the unknown parameters, it is possible for the entire dynamic response data to develop a prediction model of the dynamic response through a regression analysis based on the measurement data. The validity of the proposed method was verified by comparing with results employing a higher-order finite element model. Parametric results revealed that the proposed method can reconstruct dynamic responses and the stiffness reduction of delaminated composite plates can be investigated for different measurements and loading locations.

• Korean Journal of Mathematics
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• 제26권3호
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• pp.405-424
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• 2018

In this paper we study some comparative growth properties of composite entire functions on the basis of relative (p, q)-th order and relative (p, q)-th lower order of entire function with respect to another entire function where p and q are any two positive integers.

• Steel and Composite Structures
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• 제6권5호
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• pp.435-457
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• 2006

The paper presents the cost optimization of composite floor trusses composed from a reinforced concrete slab of constant depth and steel trusses consisting of hot rolled channel sections. The optimization was performed by the nonlinear programming approach, NLP. Accordingly, a NLP optimization model for composite floor trusses was developed. An accurate objective function of the manufacturing material, power and labour costs was proposed to be defined for the optimization. Alongside the costs, the objective function also considers the fabrication times, and the electrical power and material consumption. Composite trusses were optimized according to Eurocode 4 for the conditions of both the ultimate and the serviceability limit states. A numerical example of the optimization of the composite truss system presented at the end of the paper demonstrates the applicability of the proposed approach.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.