• International Journal of Aerospace System Engineering
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• 제2권2호
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• pp.19-23
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• 2015

This work deals with the study of buckling and post buckling characteristics of laminated composite plates with and without localized regions of damage. The need of a detailed study on Finite Element Analysis of buckling and post buckling of laminated composite structures considering various aspects enhances the interest among researchers. Mathematical formulation is developed for damaged composite plates using a finite element technique based on Inverse Hyperbolic Shear Deformation Theory. This theory satisfies zero transverse shear stresses conditions at the top and bottom surfaces of the plate and provides a non-linear transverse shear stress distribution. Damage modeling is done using an anisotropic damage formulation, which is based on the concept of stiffness change. The structural elements are subjected to in-plane loading. The computer program is developed in MATLAB environment. The numerical results are presented after through validation of developed finite element code. The effect of damage on buckling and post buckling has been carried out for various parameters such as amount of percentage of damaged area, damage intensity, etc. The results show that the presence of internal flaws will significantly affect the buckling characteristics of laminated composite plates. The outcomes and remarks from this work will assist to address some key issues concerning composite structures.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 추계학술대회논문집
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• pp.1101-1106
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• 2004

This study presents the reconstruction of sound field radiated from an automotive engine using equivalent sources. Basic concept of the method presented is to replace the engine noise source with elementary sources of multipoles, e.g., monopoles and dipoles. The so-called Helmholtz equation least-squares (HELS) method can reconstruct the sound radiation fields from spherical geometries in a series expansion of spherical Hankel functions and spherical harmonics. In this paper, multi-Point, multipole equivalent sources are employed to reconstruct the sound field radiated from an automotive engine with a fixed rotation speed. To ensure and improve the accuracy of reconstruction, the spatial filters of multipole coefficients and wave-vectors are adopted for suppressing the adverse effect of high-order multipoles. Optimal filter shapes are designed with regularization parameters minimizing the generalized cross validation (GCV) function between actual and reproduced model. After regeneration of field pressures using the proposed method as many as necessary, the vibro-acoustic field of an engine could be reconstructed by using the BEM-based near-field acoustic holography (NAH) technique in a cost-effective manner.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.317-323
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• 2021

Social media is increasingly becoming a part of our daily life for communicating each other. There are various tools and applications for communication and therefore, identity theft is a common issue among users of such application. A new style of identity theft occurs when cybercriminals break into WhatsApp account, pretend as real friends and demand money or blackmail emotionally. In order to prevent from such issues, data mining can be used for text classification (TC) in analysis authorship attribution (AA) to recognize original sender of the message. Arabic is one of the most spoken languages around the world with different variants. In this research, we built a machine learning model for mining and analyzing the Arabic messages to identify the author of the messages in Saudi dialect. Many points would be addressed regarding authorship attribution mining and analysis: collect Arabic messages in the Saudi dialect, filtration of the messages' tokens. The classification would use a cross-validation technique and different machine-learning algorithms (Naïve Baye, Support Vector Machine). Results of average accuracy for Naïve Baye and Support Vector Machine have been presented and suggestions for future work have been presented.

• Journal of Information Processing Systems
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• 제15권3호
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• pp.694-706
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• 2019

In this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study showed that the LSTM model outperforms the gradient boosting model, a general machine learning model known to have relatively good prediction performance, for the time series classification of the cryptocurrency price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB model.

• 전력전자학회논문지
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• 제26권3호
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• pp.183-191
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• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.

• Geomechanics and Engineering
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• 제31권5호
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• pp.453-460
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• 2022

It is essential for geotechnical engineers to conduct studies and make predictions about the stability of slopes, since collapse of a slope may result in catastrophic events. The Gaussian process regression (GPR) approach was carried out for the purpose of predicting the factor of safety (FOS) of the slopes in the study that was presented here. The model makes use of a total of 327 slope cases from Iran, each of which has a unique combination of geometric and shear strength parameters that were analyzed by PLAXIS software in order to determine their FOS. The K-fold (K = 5) technique of cross-validation (CV) was used in order to conduct an analysis of the accuracy of the models' predictions. In conclusion, the GPR model showed excellent ability in the prediction of FOS of slope stability, with an R² value of 0.8355, RMSE value of 0.1372, and MAPE value of 6.6389%, respectively. According to the results of the sensitivity analysis, the characteristics (friction angle) and (unit weight) are, in descending order, the most effective, the next most effective, and the least effective parameters for determining slope stability.

• Computers and Concrete
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• 제32권3호
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• pp.233-246
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• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

• 한국전기전자재료학회논문지
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• 제21권12호
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• pp.1063-1070
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• 2008

The performance of an acoustic transducer is determined by the effects of many design variables, and mostly the influences of these design variables are not linearly independent of each other. To achieve the optimal performance of an acoustic transducer, we must consider the cross-coupled effects of the design variables. In this study, the variation of the performances of underwater acoustic transducer in relation to its structural variables was analyzed. In addition, the new optimal design scheme of an acoustic transducer that could reflect not only individual but also all the cross-coupled effects of multiple structural variables, and could determine the detailed geometry of the transducer with great efficiency and rapidity was developed. The validation of the new optimal design scheme was verified by applying the optimal structure design of a flextensional transducer which are the most common use for high power underwater acoustic transducer. With the finite element analysis(FEA), we analyzed the variation of the resonance frequency, sound pressure, and working depth of a flextensional transducer in relation to its design variables. Through statistical multiple regression analysis of the results, we derived functional forms of the resonance frequency, sound pressure, and working depth in terms of the design variables. By applying the constrained optimization technique, Sequential Quadratic Programming Method of Phenichny and Danilin(SQP-PD), to the derived function, we designed and verified the optimal structure of the Class IV flextensional transducer that could provide the highest sound pressure level and highest working depth at a given operation frequency of 1 kHz.

• Nuclear Engineering and Technology
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• 제42권5호
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• pp.474-499
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• 2010

This paper presents the mostortant developments implemented in the APOLLO2 spectral code since its last general presentation at the 1999 M&C conference in Madrid. APOLLO2 has been provided with new capabilities in the domain of cross section self-shielding, including mixture effects and transfer matrix self-shielding, new or improved flux solvers (CPM for RZ geometry, heterogeneous cells for short MOC and the linear-surface scheme for long MOC), improved acceleration techniques ($DP_1$), that are also applied to thermal and external iterations, and a number of sophisticated modules and tools to help user calculations. The method of characteristics, which took over the collision probability method as the main flux solver of the code, allows for whole core two-dimensional heterogeneous calculations. A flux reconstruction technique leads to fast albeit accurate solutions used for industrial applications. The APOLLO2 code has been integrated (APOLLO2-A) within the $ARCADIA^{(R)}$ reactor code system of AREVA as cross section generator for PWR and BWR fuel assemblies. APOLLO2 is also extensively used by Electricite de France within its reactor calculation chain. A number of numerical examples are presented to illustrate APOLLO2 accuracy by comparison to Monte Carlo reference calculations. Results of the validation program are compared to the measured values on power plants and critical experiments.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1510-1510
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• 2001

NIRS uses reflectance signals resulting from bending and stretching vibrations in chemical bonds between carbon, nitrogen, hydrogen, sulfur and oxygen. These reflectance signals are used to measure the concentration of major chemical composition and other descriptors of homogenized and freeze-dried whole broiler carcasses. Six strains of chicken were analyzed and the NIRS model predictions compared to reference data. The results of this comparison indicate that NIRS is a rapid tool for predicting dry matter (DM), fat, crude protein (CP) and ash content in the broiler carcass. Males and females of six commercial strain crosses of broiler chicken (Gallus domesticus) were used in this study (6$\times$2 factorial design). Each strain was grown to 16 weeks of age, and duplicate serial samples were taken for body composition analysis. Each whole carcass was pressure-cooked, homogenized, and a representative sample was freeze-dried. Body composition determined as follows: DM by oven dried method at 105$^{\circ}C$ for 3 hours, fat by Mojonnier diethyl ether extraction, CP by measuring nitrogen content using an auto-analyzer with Kjeldhal digest and ash by combustion in a muffle furnace for 24 hour at 55$0^{\circ}C$. These homogenized and freeze-dried carcass samples were then scanned with a Foss NIR Systems 6500 visible-NIR spectrophotometer (400-2500nm) (Foss NIR Systems, Silver Spring, MD., US) using Infra-Soft-International, ISI, WinISl software (ISI, Port Matilda, US). The NIRS spectra were analyzed using principal component (PC) analysis. This data was corrected for scatter using standard normal “Variate” and “Detrend” technique. The accuracy of the NIRS calibration equations developed using Partial Least Squares (PLS) for predicting major chemical composition and carcass descriptors- such as body mass (BM), bird dry matter and moisture content was tested using cross validation. Discrimination analysis was also used for sex and strain identification. According to Dr John Shenk, the creator of the ISI software, the calibration equations with the correlation coefficient, $R^2$, between reference data and NIRS predicted results of above 0.90 is excellent and between 0.70 to 0.89 is a good quantifying guideline. The excellent calibration equations for DM ($R^2$= 0.99), fat (0.98) and CP (0.92) and a good quantifying guideline equation for ash (0.80) were developed in this study. The results of cross validation statistics for carcass descriptors, body composition using reference methods, inter-correlation between carcass descriptors and NIRS calibration, and the results of discrimination analysis for sex and strain identification will also be presented in the poster. The NIRS predicted daily gain and calculated daily gain from this experiment, and true daily gain (using data from another experiment with closely related broiler chicken from each of the six strains) will also be discussed in the paper.