• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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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.

• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.370-384
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• 2016

Computer-aided materials design requires new modeling approaches to characterize and represent fine-grained geometric structures and material compositions at multiple scales. Recently, a dual-Rep approach was developed to model materials microstructures based on a new basis function, called surfacelet. As a combination of implicit surface and wavelets, surfacelets can efficiently identify and represent planar, cylindrical, and ellipsoidal geometries in material microstructures and describe the distribution of compositions and properties. In this paper, these primitive surfacelets are extended and composite surfacelets are proposed to model more complex geometries. Composite surfacelets are constructed by Boolean operations on the primitives. The surfacelet transform is applied to match geometric features in three-dimensional images. The composition of the material near the identified features can then be modeled. A cubic surfacelet and a v-joint surfacelet are developed to demonstrate the reverse engineering process of retrieving material compositions from material images.

• Structural Engineering and Mechanics
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• v.13 no.3
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• pp.261-276
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• 2002

A novel, 6-node, two-dimensional mixed finite element (FE) model has been developed to analyze laminated composite beams by using the minimum potential energy principle. The model has been formulated by considering four degrees of freedom (two displacement components u, w and two transverse stress components ${\sigma}_z$, $\tau_{xz}$) per node. The transverse stress components have been invoked as nodal degrees of freedom by using the fundamental elasticity equations. Thus, the present mixed finite element model not only ensures the continuity of transverse stress and displacement fields through the thickness of the laminated beams but also maintains the fundamental elasticity relationship between the components of stress, strain and displacement fields throughout the elastic continuum. This is an important feature of the present formulation, which has not been observed in various mixed formulations available in the literature. Results obtained from the model have been shown to be in excellent agreement with the elasticity solutions for thin as well as thick laminated composite beams. A few results for a cross-ply beam under fixed support conditions are also presented.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.03a
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• pp.61-66
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• 1996

An overall feature to simulate composite behavior and to predict closed solution has been performed for the application to the stress analysis in a discontinuous composite solid. To obtain the internal field quantities of composite, the micromechanics analysis and finite element analysis (FEA) were implemented. For the numerical illustration, an aligned axisymmetric single fiber model has been employed to assess field quantities. Further, a micromechanics model to describe the elastic behavior of fiber or whisker reinforced metal matrix composites has been developed and the stress concentrations between reinforcements were investigated using the modified shear lag model with the comparions between reinforcements were investigated using the modified shear lag model with the comparison of finite element analysis (FEA). The rationale is based on the replacement of the matrix between fiber ends with the fictitious fiber to maintain the compatibility of displacement and traction. It was found that the new model gives a good agreement with FEA results in the small fiber aspect ratio regime as well as that in the large fiber aspect ratio regime. It was found that the proposed simulation methodology for stress analysis is applicable to the complicated inhomogeneous solid for the investigation of micromechanical behavior.

• Steel and Composite Structures
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• v.26 no.6
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• pp.733-743
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• 2018

This paper presents post-buckling responses of a simply supported laminated composite beam subjected to a non-follower axially compression loads. In the nonlinear kinematic model of the laminated beam, total Lagrangian approach is used in conjunction with the Timoshenko beam theory. In the solution of the nonlinear problem, incremental displacement-based finite element method is used with Newton-Raphson iteration method. There is no restriction on the magnitudes of deflections and rotations in contradistinction to von-Karman strain displacement relations of the beam. The distinctive feature of this study is post-buckling analysis of Timoshenko Laminated beams full geometric non-linearity and by using finite element method. The effects of the fibber orientation angles and the stacking sequence of laminates on the post-buckling deflections, configurations and stresses of the composite laminated beam are illustrated and discussed in the numerical results. Numerical results show that the above-mentioned effects play a very important role on the post-buckling responses of the laminated composite beams.

• Journal of the Korean Society of Safety
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• v.16 no.1
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• pp.88-93
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• 2001

This study discusses frequency analysis based on autoregressive (AR) time series model, and process characterization in orthogonal cutting of a fiber-matrix composite materials. A sparsely distributed idealized composite material, namely a glass reinforced polyester (GFRP) was used as workpiece. Analysis method employs a force sensor and the signals from the sensor are processed using AR time series model. The resulting pattern vectors of AR coefficients are then passed to the feature extraction block. Inside the feature extraction block, only those features that are most sensitive to different types of cutting mechanisms are selected. The experimental correlations between the different chip formation mechanisms and AR model coefficients are established.

• Coupled systems mechanics
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• v.7 no.6
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• pp.691-705
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• 2018

In this paper, nonlinear displacements of laminated composite beams are investigated under non-uniform temperature rising with temperature dependent physical properties. Total Lagrangian approach is used in conjunction with the Timoshenko beam theory for nonlinear kinematic model. Material properties of the laminated composite beam are temperature dependent. In the solution of the nonlinear problem, incremental displacement-based finite element method is used with Newton-Raphson iteration method. The distinctive feature of this study is nonlinear thermal analysis of Timoshenko Laminated beams full geometric non-linearity and by using finite element method. In this study, the differences between temperature dependent and independent physical properties are investigated for laminated composite beams for nonlinear case. Effects of fiber orientation angles, the stacking sequence of laminates and temperature on the nonlinear displacements are examined and discussed in detail.

• Steel and Composite Structures
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• v.49 no.1
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• pp.109-123
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• 2023

Headed studs welded to steel beams and embedded within the concrete of deck slabs are vital components of modern composite floor systems, where safety and economy depend on the accurate predictions of the stud shear resistance. The multitude of existing deck profiles and the complex behavior of studs in deck slab ribs makes developing accurate and reliable mechanical or empirical design models challenging. The paper addresses this issue by presenting a machine learning (ML) model developed from the natural gradient boosting (NGBoost) algorithm capable of producing probabilistic predictions and a database of 464 push-out tests, which is considerably larger than the databases used for developing existing design models. The proposed model outperforms models based on other ML algorithms and existing descriptive equations, including those in EC4 and AISC 360, while offering probabilistic predictions unavailable from other models and producing higher shear resistances for many cases. The present study also showed that the stud shear resistance is insensitive to the concrete elastic modulus, stud welding type, location of slab reinforcement, and other parameters considered important by existing models. The NGBoost model was interpreted by evaluating the feature importance and dependence determined with the SHapley Additive exPlanations (SHAP) method. The model was calibrated via reliability analyses in accordance with the Eurocodes to ensure that its predictions meet the required reliability level and facilitate its use in design. An interactive open-source web application was created and deployed to the cloud to allow for convenient and rapid stud shear resistance predictions with the developed model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2030-2052
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• 2023

Analysis of a bipartite (two-mode) network is a significant research area to understand the formation of social communities, economic systems, drug side effect topology, etc. in complex information systems. Most of the previous works talk about a projection-based model or latent feature model, which predicts the link based on singular similarity. The projection-based models suffer from the loss of structural information in the projected network and the latent feature is hardly present. This work proposes a novel method for link prediction in the bipartite network based on an ensemble of composite similarities, overcoming the issues of model-based and latent feature models. The proposed method analyzes the structure, neighborhood nodes as well as latent attributes between the nodes to predict the link in the network. To illustrate the proposed method, experiments are performed with five real-world data sets and compared with various state-of-art link prediction methods and it is inferred that this method outperforms with ~3% to ~9% higher using area under the precision-recall curve (AUC-PR) measure. This work holds great significance in the study of biological networks, e-commerce networks, complex web-based systems, networks of drug binding, enzyme protein, and other related networks in understanding the formation of such complex networks. Further, this study helps in link prediction and its usability for different purposes ranging from building intelligent systems to providing services in big data and web-based systems.

• Smart Structures and Systems
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• v.30 no.4
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• pp.371-383
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• 2022

A novel impact localization method is presented based on impact induced elastic waves in sensorized composite structure subjected to temperature fluctuations. In real practices, environmental and operational conditions influence the acquired signals and consequently make the feature (particularly Time of Arrival (TOA)) extraction process, complicated and troublesome. To overcome this complication, a robust TOA estimation method is proposed based on the times in which the absolute amplitude of the signal reaches to a specific amplitude value. The presented method requires prior knowledge about the normalized wave velocity in different directions of propagation. To this aim, a finite element model of the plate was built in ABAQUS/CAE. The impact location is then highlighted by calculating an error value at different points of the structure. The efficiency of the developed impact localization technique is experimentally evaluated by dropping steel balls with different energies on a carbon fiber composite plate with different temperatures. It is demonstrated that the developed technique is able to localize impacts with different energies even in the presence of noise and temperature fluctuations.