• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1963-1978
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• 2015

With the advancement of mobile web environments, identification and analysis of the user behavior play a significant role and remains a challenging task to implement with variations observed in the model. This paper presents an efficient method for mining optimized user behavior prediction model using genetic algorithm on mobile web structure. The framework of optimized user behavior prediction model integrates the temporary and permanent register information and is stored immediately in the form of integrated logs which have higher precision and minimize the time for determining user behavior. Then by applying the temporal characteristics, suitable time interval table is obtained by segmenting the logs. The suitable time interval table that split the huge data logs is obtained using genetic algorithm. Existing cluster based temporal mobile sequential arrangement provide efficiency without bringing down the accuracy but compromise precision during the prediction of user behavior. To efficiently discover the mobile users' behavior, prediction model is associated with region and requested services, a method called optimized user behavior Prediction Model using Genetic Algorithm (PM-GA) on mobile web structure is introduced. This paper also provides a technique called MAA during the increase in the number of models related to the region and requested services are observed. Based on our analysis, we content that PM-GA provides improved performance in terms of precision, number of mobile models generated, execution time and increasing the prediction accuracy. Experiments are conducted with different parameter on real dataset in mobile web environment. Analytical and empirical result offers an efficient and effective mining and prediction of user behavior prediction model on mobile web structure.

• Structural Engineering and Mechanics
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• v.72 no.3
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• pp.355-366
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• 2019

Self-centering wall (SCW) is a resilient and sustainable structural system which incorporates unbonded posttensioning (PT) tendons to provide self-centering (SC) capacity along with supplementary dissipators to dissipate seismic energy. Hysteretic energy dissipators are usually placed at two sides of SCWs to facilitate ease of postearthquake examination and convenient replacement. To achieve a good prediction for the skeleton curve of the wall, this paper firstly developed an analytical investigation on lateral load responses of self-centering walls with distributed vertical dampers (VD-SCWs) using the concept of elastic theory. A simplified method for the calculation of limit state points is developed and validated by experimental results and can be used in the design of the system. Based on the analytical results, parametric analysis is conducted to investigate the influence of damper and tendon parameters on the performance of VD-SCWs. The results show that the proposed approach has a better prediction accuracy with less computational effects than the Perez method. As compared with previous experimental results, the proposed method achieves up to 60.1% additional accuracy at the effective linear limit (DLL) of SCWs. The base shear at point DLL is increased by 62.5% when the damper force is increased from 0kN to 80kN. The wall stiffness after point ELL is reduced by 69.5% when the tendon stiffness is reduced by 75.0%. The roof deformation at point LLP is reduced by 74.1% when the initial tendon stress is increased from $0.45f_{pu}$ to $0.65f_{pu}$.

• Geomechanics and Engineering
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• v.30 no.1
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• pp.11-26
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• 2022

One of the most important issues in tunneling, is the squeezing phenomenon. Squeezing can occur during excavation or after the construction of tunnels, which in both cases could lead to significant damages. Therefore, it is important to predict the squeezing and consider it in the early design stage of tunnel construction. Different empirical, semi-empirical and theoretical-analytical methods have been presented to determine the squeezing. Therefore, it is necessary to examine the ability of each of these methods and identify the best method among them. In this study, squeezing in a part of the Alborz service tunnel in Iran was estimated through a number of empirical, semi- empirical and theoretical-analytical methods. Among these methods, the most robust model was used to obtain a database including 300 data for training and 33 data for testing in order to develop a machine learning (ML) method. To this end, three ML models of Gaussian process regression (GPR), artificial neural network (ANN) and support vector regression (SVR) were trained and tested to propose a robust model to predict the squeezing phenomenon. A comparative analysis between the conventional and the ML methods utilized in this study showed that, the GPR model is the most robust model in the prediction of squeezing phenomenon. The sensitivity analysis of the input parameters using the mutual information test (MIT) method showed that, the most sensitive parameter on the squeezing phenomenon is the tangential strain (ε_θ^α) parameter with a sensitivity score of 2.18. Finally, the GPR model was recommended to predict the squeezing phenomenon in tunneling projects. This work's significance is that it can provide a good estimation of the squeezing phenomenon in tunneling projects, based on which geotechnical engineers can take the necessary actions to deal with it in the pre-construction designs.

• Design & Manufacturing
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• v.17 no.4
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• pp.1-7
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• 2023

In the fabrication of curved multi-display glass for automotive use, the surface roughness of the mold is a critical quality factor. However, the difficulty in detecting micro-cutting signals in a micro-machining environment and the absence of a standardized model for predicting micro-cutting forces make it challenging to intuitively infer the correlation between cutting variables and actual surface roughness under machining conditions. Consequently, current practices heavily rely on machining condition optimization through the utilization of cutting models and experimental research for force prediction. To overcome these limitations, this study employs a surface roughness prediction formula instead of a cutting force prediction model and converts the surface roughness prediction formula into experimental data. Additionally, to account for changes in surface roughness during machining runtime, the theory of position variables has been introduced. By leveraging artificial neural network technology, the accuracy of the surface roughness prediction formula model has improved by 98%. Through the application of artificial neural network technology, the surface roughness prediction formula model, with enhanced accuracy, is anticipated to reliably perform the derivation of optimal machining conditions and the prediction of surface roughness in various machining environments at the analytical stage.

• Journal of the Microelectronics and Packaging Society
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• v.8 no.4
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• pp.35-41
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• 2001

In this paper, several methods to predict the solder joint shape are studied. Although there are various methods to predict the solder joint shape, such as truncated sphere method. force-balanced analytical solution, and energy-based methods like surface evolver developed by Ken Brakke, we calculate solder joint shape of $\mu$BGA by two solder joint shape prediction methods(truncated sphere method and surfaceevolver) and then compare results of each method. The results indicate that two methods can accurately predict the solder Joint shape in an accurate range. After that, we calculate reliability solder joint shape under thermal cycle test by FEA program ANSYS(version 5.62). As a result, it could be found that optimal solder joint shape calculated by solder joint prediction method has best reliability in thermal cycle test.

• Structural Engineering and Mechanics
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• v.54 no.3
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• pp.501-520
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• 2015

Presence of torsional loadings can significantly affect the flow of internal forces and deformation capacity of reinforced concrete (RC) columns. It increases the possibility of brittle shear failure leading to catastrophic collapse of structural members. This necessitates accurate prediction of the torsional behaviour of RC members for their safe design. However, a review of previously published studies indicates that the torsional behaviour of RC members has not been studied in as much depth as the behaviour under flexure and shear in spite of its frequent occurrence in bridge columns. Very few analytical models are available to predict the response of RC members under torsional loads. Softened truss model (STM) developed in the University of Houston is one of them, which is widely used for this purpose. The present study shows that STM prediction is not sufficiently accurate particularly in the post cracking region when compared to test results. An improved analytical model for RC square columns subjected to torsion with and without axial compression is developed. Since concrete is weak in tension, its contribution to torsional capacity of RC members was neglected in the original STM. The present investigation revealed that, disregard to tensile strength of concrete is the main reason behind the discrepancies in the STM predictions. The existing STM is extended in this paper to include the effect of tension stiffening for better prediction of behaviour of square RC columns under torsion. Three different tension stiffening models comprising a linear, a quadratic and an exponential relationship have been considered in this study. The predictions of these models are validated through comparison with test data on local and global behaviour. It was observed that tension stiffening has significant influence on torsional behaviour of square RC members. The exponential and parabolic tension stiffening models were found to yield the most accurate predictions.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.48-54
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• 2013

Purpose: This study evaluated the feasibility of using a discrete wavelet transform (DWT) method as a preprocessing tool for visible/near-infrared spectroscopy (VIS/NIRS) with a spectroscopic transmittance dataset for predicting the internal quality of cherry tomatoes. Methods: VIS/NIRS was used to acquire transmittance spectrum data, to which a DWT was applied to generate new variables in the wavelet domain, which replaced the original spectral signal for subsequent partial least squares (PLS) regression analysis and prediction modeling. The DWT concept and its importance are described with emphasis on the properties that make the DWT a suitable transform for analyzing spectroscopic data. Results: The $R^2$ values and root mean squared errors (RMSEs) of calibration and prediction models for the firmness, sugar content, and titratable acidity of cherry tomatoes obtained by applying the DWT to a PLS regression with a set of spectra showed more enhanced results than those of each model obtained from raw data and mean normalization preprocessing through PLS regression. Conclusions: The developed DWT-incorporated PLS models using the db5 wavelet base and selected approximation coefficients indicate their feasibility as good preprocessing tools by improving the prediction of firmness and titratable acidity for cherry tomatoes with respect to $R^2$ values and RMSEs.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.4
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• pp.262-271
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• 2011

In this work, we present a unified analytical surface potential model, valid for both PD and FD SOI MOSFETs. Our model is based on a simplified one dimensional and purely analytical approach, and builds upon an existing model, proposed by Yu et al. [4], which is one of the most recent compact analytical surface potential models for SOI MOSFETs available in the literature, to improve its accuracy and remove its inconsistencies, thereby adding to its robustness. The model given by Yu et al. [4] fails entirely in modeling the variation of the front surface potential with respect to the changes in the substrate voltage, which has been corrected in our modified model. Also, [4] produces self-inconsistent results due to misinterpretation of the operating mode of an SOI device. The source of this error has been traced in our work and a criterion has been postulated so as to avoid any such error in future. Additionally, a completely new expression relating the front and back surface potentials of an FD SOI film has been proposed in our model, which unlike other models in the literature, takes into account for the first time in analytical one dimensional modeling of SOI MOSFETs, the contribution of the increasing inversion charge concentration in the silicon film, with increasing gate voltage, in the strong inversion region. With this refinement, the maximum percent error of our model in the prediction of the back surface potential of the SOI film amounts to only 3.8% as compared to an error of about 10% produced by the model of Yu et al. [4], both with respect to MEDICI simulation results.

• Structural Engineering and Mechanics
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• v.78 no.4
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• pp.485-496
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• 2021

So far analytical methods on collapse assessment of three-dimensional (3-D) steel frames have mainly focused on a single-column-removal scenario. However, the collapse of the Federal Building in the US due to car bomb explosion indicated that the loss of multiple columns may occur in the real structures, wherein the structures are more vulnerable to collapse. Meanwhile, the General Services Administration (GSA) in the US suggested that the removal of side columns of the structure has a great possibility to cause collapse. Therefore, this paper analytically deals with the robustness of 3-D steel frames in a two-side-column-removal (TSCR) scenario. Analytical method is first proposed to determine the collapse resistance of the frame during this column-removal procedure. The reliability of the analytical method is verified by the finite element results. Moreover, a design-based methodology is proposed to quickly assess the robustness of the frame due to a TSCR scenario. It is found the analytical method can reasonably predict the resistance-displacement relationship of the frame in the TSCR scenario, with an error generally less than 10%. The parametric numerical analyses suggest that the slab thickness mainly affects the plastic bearing capacity of the frame. The rebar diameter mainly affects the capacity of the frame at large displacement. However, the steel beam section height affects both the plastic and ultimate bearing capacity of the frame. A case study on a six-storey steel frame shows that the design-based methodology provides a conservative prediction on the robustness of the frame.

• Structural Engineering and Mechanics
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• v.32 no.3
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• pp.459-475
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• 2009

This paper presents analytical methodologies for remaining life prediction of plain concrete structural components considering tension softening and size effects. Non-linear fracture mechanics principles (NLFM) have been used for crack growth analysis and remaining life prediction. Various tension softening models such as linear, bi-linear, tri-linear, exponential and power curve have been presented with appropriate expressions. Size effect has been accounted for by modifying the Paris law, leading to a size adjusted Paris law, which gives crack length increment per cycle as a power function of the amplitude of a size adjusted stress intensity factor (SIF). Details of tension softening effects and size effect in the computation of SIF and remaining life prediction have been presented. Numerical studies have been conducted on three point bending concrete beams under constant amplitude loading. The predicted remaining life values with the combination of tension softening & size effects are in close agreement with the corresponding experimental values available in the literature for all the tension softening models.