• Elastomers and Composites
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• v.43 no.1
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• pp.25-30
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• 2008

A bushing is a device used in automotive suspension systems to reduce the load transmitted from the wheel to the frame of the vehicle. A bushing is a hollow cylinder, which is bonded to a solid steel shaft at its inner surface and a steel sleeve at its outer surface. The relation between the force and moment applied to the shaft and the relative deformation and rotational angle of a bushing exhibits features of viscoelasticity. Since a moment-rotational angle relation for a bushing is important for multibody dynamics numerical simulations, the simple relation between the moment and rotational angle has been derived from experiment. It is shown that the predictions by the proposed moment-rotational angle relation are in very good agreement with the experimental results.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• Journal of the Korean Society of Propulsion Engineers
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• v.26 no.3
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• pp.10-21
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• 2022

The propellant tank, which is a space launch vehicle structure, must have structural integrity as various static and dynamic loads are applied during ground transportation, launch standby, take-off and flight processes. Because of these characteristics, the propellant tank cylinder, the structural object of this study, has a thin thickness, so buckling due to compressive load is considered important in the cylinder design. However, the existing buckling design standards such as NASA and Europe are fairly conservative and do not reflect the latest design and manufacturing technologies. In this study, nonlinear buckling analysis is performed using various analysis models that reflect initial defects, and a method for establishing new buckling design standards for cylinder structures is presented. In conclusion, it was confirmed that an effective lightweight design of the cylinder structure for common bulkhead propulsion tank could be realized.

• Smart Structures and Systems
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• v.29 no.1
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.12
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• pp.1043-1053
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• 2016

In this paper, the application of Parallel Distributed Compensation (PDC) controller for fixed pitch rotary wing system was studied. For nonlinear modeling, T-S fuzzy model was utilized to advance system control including the tilt type UAV. PDC controller was designed through the Linear Matrix Inequality (LMI). Experiments for determining the applicability and feasibility of PDC were performed using the 1 axis attitude control equipment and simulation. To verify the performance and characteristics of the controller, Mathworks Co. Simulink was used. After then, the PDC controller performance was verified and the results with developed controller using a 1 axis attitude control equipment were compared. Verification of the feasibility of PDC controller for the fixed pitch rotary wing system and identification of the overall performance and improvement analysis was conducted based on the experimental results.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.1
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• pp.51-58
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• 2012

The objective of this paper is to predict the buckling pressure of a composite cylindrical shell using buckling formulas (ASME 2007, NASA SP 8007) and finite element analysis. The model in this study uses a stacking angle of [0/90]12t and USN 125 composite material. All specimens were made using a prepreg method. First, finite element analysis was conducted, and the results were verified through comparison with the hydrostatic pressure buckling experiment results. Second, the values obtained from the buckling formula and the buckling pressure values obtained from the finite element analysis were compared as the stacking angle was changed in $5^{\circ}$ increments from $20^{\circ}$ to $90^{\circ}$. The linear and nonlinear results of the finite element analysis were consistent with the results of the experiment, with a safety factor of 0.85-1. Based on the above result, the ASME 2007 formula, a simplified version of the NASA SP-8007 formula, is regarded as a buckling formula that provides a reliable safety factor.