The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G ***,during a flood,sensor nodes may be washed up or become fault...
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The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G ***,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network *** address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood *** light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river *** framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a ***,an algorithm hybridized with group method data handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative *** proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River *** performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.
Membrane filtration techniques are distinguished among methods for wastewater treatment and fully correspond to the requirements of the green concept of chemistry and production. The limiting factor for greater applic...
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Membrane filtration techniques are distinguished among methods for wastewater treatment and fully correspond to the requirements of the green concept of chemistry and production. The limiting factor for greater application of these methods is the phenomenon of fouling and the decline of the permeate flux. In this study, polynomial neural network based on group method data handling (GMDH) algorithm was applied to predict the performance of the complexation-microfiltration process for the removal of Pb(II), Zn(II), and Cd(II) from synthetic wastewater. The influence of working parameters such as pH, initial concentration of metal ions, type of complexing agent, and pressure on flux was experimentally determined. The data obtained were used as input parameters for the GMDH model as well as for the multiple linear regression (MLR) model. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) were used for evaluation purposes. Results showed that the developed model has excellent performance in flux prediction with R-2 of 0.9648.
Additive manufacturing (AM) was developed initially as a technique for rapid prototyping, to visualize, test and authenticate a design, before end-user production of the design. In recent years, Additive Manufacturing...
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Additive manufacturing (AM) was developed initially as a technique for rapid prototyping, to visualize, test and authenticate a design, before end-user production of the design. In recent years, Additive Manufacturing (AM) technique Fused Deposition modeling (FDM), has developed to become a rapid manufacturing technique because of the ability to produce complex parts layer-by-layer in lesser production cycle time than as compared to conventional machining processes. FDM also offers the advantage of the lowest cost because of no tooling requirements. Despite these advantages, building parts by utilizing FDM for end-use is still a demanding endeavor. This is because FDM has multiple processing parameters, which affect the part quality, mechanical properties, build time and dimensional accuracy. These FDM processing parameters include air gap, build orientation, infill percentage, raster angle, layer thickness, etc. Depending upon the application, for which the part is manufactured, careful selection of these process parameters needs to be done. For a specific output requirement, some of the process parameters are significant than the rest, these significant process parameters need to be identified and optimized. Due to this, researchers have explored and utilized various experimental or statistical Design of Experiment (DOE) techniques for optimizing the FDM process parameters to improve the mechanical properties or part quality or both. Some of these DOE techniques include the Taguchi method, Genetic algorithm (GA), gray relational, Response surface method (RSM), fractional factorial, Artificial Neural networks (ANN), Fuzzy logic, ANOVA, etc. This article aims at reviewing the current research on the statistical and experimental design techniques for different applications or output responses such as enhancing mechanical properties, build time, part quality, etc.
This paper proposes the group method data handling (GMDH) algorithm and applies it to estimate the slump of high-performance concrete (HPC). It is known that HPC is a highly complex material whose behaviour is difficu...
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This paper proposes the group method data handling (GMDH) algorithm and applies it to estimate the slump of high-performance concrete (HPC). It is known that HPC is a highly complex material whose behaviour is difficult to model, especially for slump. To estimate the slump, it is a nonlinear function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, superplasticizer, and coarse and fine aggregate. Therefore, slump estimation is set as a function of the content of these seven concrete ingredients and additional four important ratios. The GMDH algorithm presented in this paper has the advantage of a heuristic self-organized and gradually complicated model for the complicated multi-variable HPC slump estimation. The model establishes the input-output relationship of a complex system using a multilayered perception-type structure that is similar to a feed-forward multilayer artificial neural network (ANN), but it expresses relationships using more explicit functions than ANN. Moreover, the GMDH has the ability to select significant variables and combine them properly and automatically. The results show that GMDH obtains a more accurate mathematical equation through learning procedures which outperforms the traditional multiple linear regression analysis (RA) and ANN, with lower estimating errors for predicting the HPC slump.
This paper presents a new methodology for reliability evaluation of composite generation and transmission systems, based on nonsequential Monte Carlo simulation (MCS) and artificial neural network (ANN) concepts. ANN ...
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This paper presents a new methodology for reliability evaluation of composite generation and transmission systems, based on nonsequential Monte Carlo simulation (MCS) and artificial neural network (ANN) concepts. ANN techniques are used to classify the operating states during the Monte Carlo sampling. A polynomial network, named group method data handling (GMDH), is used, and the states analyzed during the beginning of the simulation process are adequately selected as input data for training and test sets. Based on this procedure, a great number of success states are classified by a simple polynomial function, given by the ANN model, providine siginificant reductions in the computational cost. Moreover, all types of composite reliability indices (i.e., loss of load probability, frequency, duration, and energy/power not supplied) can be assessed not only for the overall system but also for areas and buses. The proposed methodology is applied to the IEEE Reliability Test System (IEEE-RTS), to the IEEE-RTS 96, and to a configuration of the Brazilian South-Southeastern System.
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