Electric Network Frequency (ENF) estimation is a critical component in various applications, including forensic audio analysis and synchronization of multimedia recordings. Previous research has primarily focused on e...
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Change detection (CD) is a critical task that involves assessing alterations between bi-temporal images. Hyperspectral images (HSIs), renowned for their extensive spectral information, excel in capturing subtle change...
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Total Knee Arthroplasty (TKA) aims to relieve knee dysfunction caused by osteoarthritis or injury, but its success is hindered by implant failure. This study examines the impact of implant porosity on the biomechanica...
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In this work,a new methodology is presented to mainly solve the fluid–solid interaction(FSI)*** methodology combines the advantages of the Newmark precise integral method(NPIM)and the dual neural network(DNN)*** NPIM...
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In this work,a new methodology is presented to mainly solve the fluid–solid interaction(FSI)*** methodology combines the advantages of the Newmark precise integral method(NPIM)and the dual neural network(DNN)*** NPIM is employed to modify the exponential matrix and loading vector based on the DNN integral *** involves incorporating the basic assumption of the Newmark-βmethod into the dynamic equation and eliminating the acceleration term from the dynamic equilibrium *** a result,the equation is reduced to a first-order linear equation ***,the PIM is applied to integrate the system step by step within the *** DNN method is adopted to solve the inhomogeneous term through fitting the integrand and the original function with a pair of neural networks,and the integral term is solved using the Newton–Leibniz *** examples demonstrate that the proposed methodology significantly improves computing efficiency and provides sufficient precision compared to the DNN *** is particularly evident when analyzing large-scale structures under blast loading conditions.
The long-term durability of glass fiber reinforced polymer(GFRP)bars in harsh alkaline environments is of great importance in engineering,which is reflected by the environmental reduction factor in vari-ous structural...
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The long-term durability of glass fiber reinforced polymer(GFRP)bars in harsh alkaline environments is of great importance in engineering,which is reflected by the environmental reduction factor in vari-ous structural *** calculation of this factor requires robust models to predict the residual tensile strength of GFRP ***,three robust metaheuristic algorithms,namely particle swarm optimiza-tion(PSO),genetic algorithm(GA),and support vector machine(SVM),were deployed in this study for achieving the best hyperparameters in the adaptive neuro-fuzzy inference system(ANFIS)in order to obtain more accurate prediction *** optimized models were developed to predict the tensile strength retention(TSR)of degraded GFRP rebars in typical alkaline environments(e.g.,seawater sea sand concrete(SWSSC)environment in this study).The study also proposed more reliable model to predict the TSR of GFRP bars exposed to alkaline environmental conditions under accelerating laboratory aging.A to-tal number of 715 experimental laboratory samples were collected in a form of extensive database to be trained.K-fold cross-validation was used to assess the reliability of the developed models by dividing the dataset into five equal *** order to analyze the efficiency of the metaheuristic algorithms,multiple statistical tests were *** was concluded that the ANFIS-SVM-based model is robust and accu-rate in predicting the TSR of conditioned GFRP *** the meantime,the ANFIS-PSO model also yielded reasonable results concerning the prediction of the tensile strength of GFRP bars in alkaline concrete *** sensitivity analysis revealed GFRP bar size,volume fraction of fibers,and pH of solution were the most influential parameters of TSR.
Convolution Neural Network (CNN) has been utilized to automate time-consuming and manually detecting wafer map failure root causes to improve productivity and yield enhancement in the semiconductor industry. This rese...
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This study introduces an advanced image enhancement model that seamlessly integrates attention mechanisms within an autoencoder architecture. The model demonstrates superior performance in denoising and color preserva...
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Welding defects are a crucial problem in the manufacturing industry. However, the industry faces enormous losses for these defects. Conditional monitoring and quality control can reduce this loss. In Industry 4.0, art...
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Historical architecture has different special styles attributed to each era, dynasty, or region. These styles are common features such as geometric properties, ratios, scales, colors, and artistic techniques. Historic...
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Historical architecture has different special styles attributed to each era, dynasty, or region. These styles are common features such as geometric properties, ratios, scales, colors, and artistic techniques. Historical geometric ornaments have an enormous capability for classification based on their geometric characteristics. Smart pattern recognition allows researchers to classify huge databases of heritage for useful internet searches. So, our main goal in this paper is to implement the detection of categories in geometric patterns for classification and documentation in which by the photography of ornaments in every monument, the type of patterns and the number of every type of pattern would be estimated quickly. Furthermore, due to occurring deterioration in these patterns, our method also contributes to recognizing the deteriorated patterns. When we encounter numerous pieces of deteriorated patterns, manual recognition in order to reassemble and reconstruct is usually impossible or time-consuming. With the aid of artificial intelligence, in this paper, our aim is to seek to solve the automatic recognition of historical geometric patterns, even patterns having deterioration as an occlusion via image processing and machine learning methods. A challenging issue that researchers would tackle in detecting historical geometric pattern’s types is the variety in geometric textures, especially when they have occlusion such as deterioration. This issue leads to limited success in classifying via extracting only one feature. The other issue is that the extracted feature must be invariant to the transformation, such as scale, rotation, and noise variation. To cope with the challenges mentioned above and accurately classify, we plan to use the fusion method based on extracting global and local features. So, the features extracted from images in this research are based on local and global. In other words, the proposed fusion strategy lies both in feature and decision level,
Produced water is reported to have the largest volume of waste stream associat-ed with hydrocarbon recovery. It was estimated to increase from 250 million B/D in 2007 to more than 300 million B/D between 2010 and 2012...
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Produced water is reported to have the largest volume of waste stream associat-ed with hydrocarbon recovery. It was estimated to increase from 250 million B/D in 2007 to more than 300 million B/D between 2010 and 2012. Market research conducted by Adroit put the globally produced water treatment market at a value of USD 5.10 billion in 2022. This value is anticipated to be USD 9.80 billion in 2032 at a compound annual growth rate (CAGR) of 5.80% over the prediction period. Oil and gas companies have been mandated to comply with the newly enacted environmental regulations that require extensive treatment of this water before discharge or reuse. The limited quantity of fresh-water resources coupled with the increasing oil and gas production activities has made it necessary for all stakeholders to look for sustainable management of this water. Presently, a certain percentage of produced water is reused while the rest is discharged into the ocean. In both cases, the water needs to be thoroughly treated. The choice of technologies for produced water treatment depends on numerous factors, such as the chemical composition of the water and the level of purity that must be attained before disposal, recycling, or re-use. Some of the technologies used for produced water treatment include physical separation methods such as gravity, adsorption, filtration, coalescence, cyclones, flotation, centrifug-es, membranes, and oxidation. There are also chemical and biological separation methods. Contaminants such as small droplets of dispersed oil and dissolved hydrocarbons (DODHs) are very challenging to remove using the above-listed water treatment technolo-gies. Moreover, the use of membrane technology has been limited only to the use of re-verse osmosis and membrane filtration for removing salinity, metals, and other inorganics. This article highlights the opportunities for the use of membrane vapor permeation and pervaporation for the removal of the small droplets of DODHs, which h
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