• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.251-257
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• 1998

Spherical space frame structure with triangular network pattern, which has the various characteristics for the mechanic property, a funtional property, an aesthetic property and so on, has often been used as one of the most efficient space structures. It is expected that this type will be used widely in large-span structural roofs. But because this structure is made of network by combination of line elements there me many nodes therefore, the structure behavior is very complicated and there can be an overall collapse of structure by buckling phenomenon if the external force reaches a limitation. This kind of buckling is due to geometric shape, network pattern, the number of layer and so on, of structure. Therefore spherical space frame with triangle network pattern have attracted many designers and researchers attention all over the world. The number of layer of space frame is divided in to the simgle, double, multi layer. That is important element which is considered deeply in the beginning of structural design. The buckling characteristics of single-layer model and double-layer model for the spherical space frame structure with triangular network pattern are evaluated and the buckling loads of these types are compared with investigation their structural efficiency in this study.

• Steel and Composite Structures
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• v.46 no.6
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• Advanced Composite Materials
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• v.18 no.3
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• pp.233-249
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• 2009

Safe installation and operation of high-pressure composite cylinders for hydrogen storage are of primary concern. It is unavoidable for the cylinders to experience temperature variation and significant thermal input during service. The maximum failure pressure that the cylinder can sustain is affected due to the dependence of composite material properties on temperature and complexity of cylinder design. Most of the analysis reported for high-pressure composite cylinders is based on simplifying assumptions and does not account for complexities like thermo-mechanical behavior and temperature dependent material properties. In the present work, a comprehensive finite element simulation tool for the design of hydrogen storage cylinder system is developed. The structural response of the cylinder is analyzed using laminated shell theory accounting for transverse shear deformation and geometric nonlinearity. A composite failure model is used to evaluate the failure pressure under various thermo-mechanical loadings. A back-propagation neural network (NNk) model is developed to predict the maximum failure pressure using the analysis results. The failure pressures predicted from NNk model are compared with those from test cases. The developed NNk model is capable of predicting the failure pressure for any given loading condition.

• Journal of Korea Water Resources Association
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• v.32 no.5
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• pp.565-577
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• 1999

The geometric patterns of a stream network in a drainage basin can be viewed as a "fractal" with fractal dimensions. Fractals provide a mathematical framework for treatment of irregular, ostensively complex shapes that show similar patterns or geometric characteristics over a range of scale. GIUH (Geomorphological Instantaneous Unit Hydrograph) is based on the hydrologic response of surface runoff in a catchment basin. This model incorporates geomorphologic parameters of a basin using Horton's order ratios. For an ordered drainage system, the fractal dimensions can be derived from Horton's laws of stream numbers, stream lengths and stream areas. In this paper, a fractal approach, which is leading to representation of a 2-parameter Gamma distribution type GIUH, has been carried out to incorporate the self similarity of the channel networks based on the high correlations between the Horton's order ratios. The shape and scale parameter of the GIUH-Nash model of IUH in terms of Horton's order ratios of a catchment proposed by Rosso(l984J are simplified by applying the fractal dimension of main stream length and channel network of a river basin. basin.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.311-318
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• 2022

Tobacco sales enterprises often need to summarize and verify the daily sales bills, which may consume substantial manpower, and manual verification is prone to occasional errors. The use of artificial intelligence technology to realize the automatic identification and verification of such bills offers important practical significance. This study presents a novel multi-branch residual network for tobacco sales bills to improve the efficiency and accuracy of tobacco sales. First, geometric correction and edge alignment were performed on the input sales bill image. Second, the multi-branch residual network recognition model is established and trained using the preprocessed data. The comparative experimental results demonstrated that the correct recognition rate of the proposed method reached 98.84% on the China Tobacco Bill Image dataset, which is superior to that of most existing recognition methods.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.8
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• pp.70-75
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• 2003

It is very important to inspect the color of printed texture in the textile process. The standard colorimetric system used for the recognition of the color in the textile industry. It uses XYZ color system defined by CIE (Commission Internationale de 1Eclairage), but is too expensive. Therefore, in this paper, we propose a color inspection system of the printed texture using a color scanner. Because the scanner uses RGB value for color, it is necessary the mapping from RGB to XYZ. However, the mapping is not simple, and the scanner has even positional deviation because of the geometric characteristics. To transform from RGB to XYZ, we used a NN (neural network) model and also compensated the positional deviation. In real experiments, we could get fairly exact XYZ value from the proposed color inspection system in spite of using a color scanner with large measuring area.

• Structural Engineering and Mechanics
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• v.62 no.2
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• pp.237-246
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• 2017

In the present paper a new Neuro-Wavelet control algorithm is proposed based on a cost function to actively control the vibrations of structures under earthquake loads. A wavelet neural network (WNN) was developed to train the control algorithm. This algorithm is designed to control multi-degree-of-freedom (MDOF) structures which consider the geometric and material non-linearity, structural irregularity, and the incident direction of an earthquake load. The training process of the algorithm was performed by using the El-Centro 1940 earthquake record. A numerical model of a three dimensional (3D) three story building was used to accredit the control algorithm under three different seismic loads. Displacement responses and hysteretic behavior of the structure before and after the application of the controller showed that the proposed strategy can be applied effectively to suppress the structural vibrations.

• Structural Engineering and Mechanics
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• v.89 no.3
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• pp.309-321
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• 2024

In this paper, the effect of semi-rigid connections on the stability bearing capacity of cross-bracings in steel tubular transmission towers is investigated. Herein, a prediction method based on the hybrid model which is a combination of particle swarm optimization (PSO) and backpropagation neural network (BPNN) is proposed to accurately predict the stability bearing capacity of cross-bracings with semi-rigid connections and to efficiently conduct its probabilistic assessment. Firstly, the establishment of the finite element (FE) model of cross-bracings with semi-rigid connections is developed on the basis of the development of the mechanical model. Then, a dataset of 7425 samples generated by the FE model is used to train and test the PSO-BPNN model, and the accuracy of the proposed method is evaluated. Finally, the probabilistic assessment for the stability bearing capacity of cross-bracings with semi-rigid connections is conducted based on the proposed method and the Monte Carlo simulation, in which the geometric and material properties including the outer diameter and thickness of cross-sections and the yield strength of steel are considered as random variables. The results indicate that the proposed method based on the PSO-BPNN model has high accuracy in predicting the stability bearing capacity of cross-bracings with semi-rigid connections. Meanwhile, the semi-rigid connections could enhance the stability bearing capacity of cross-bracings and the reliability of cross-bracings would significantly increase after considering semi-rigid connections.

• Korean Journal of Computational Design and Engineering
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• v.18 no.4
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• pp.275-282
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• 2013

This paper proposes a method for human identification using teeth contours extracted from dental images that are captured from the frontal views of subjects each of who opens his or her mouth slightly. Each dental image has a black-colored region containing the subject's teeth contours which are usually different from subject to subject. This means that this black-colored region has bio-mimetic information useful for human identification. The basic idea of the method is to extract the upper and lower teeth contours from the dental image of each subject and to encode their geometric patterns using a back-propagation neural network model. After acquiring 400 teeth images form 10 university students, we used 300 images for the training data of the neural network model and 100 images for its verification. Experimental results have shown that the proposed neural network-based method can be used as an alternative solution for identification among a small group of humans with a low cost and simple setup.

• The Journal of the Korea Contents Association
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• v.6 no.2
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• pp.34-43
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• 2006

This paper shows modeling methods for VBR video trace. It is well known that VBR video trace is characterized as longterm correlated and highly intermittent burst data. To analyze this, we attempt to model it using neural network with auxiliary linear structures derived from residual threshold. For testing purpose, we generate VBR video trace from chaotic nonlinear function combined with the geometric random noise. The modeling result of the generated data shows that the attempted method represents more accurately than the traditional neural network. However, we also found that combining hRU to the attempted modeling method can yield a closer agreement to statistical features of the generated data than the attempted modeling method alone.