5G Network in Content Based Emotion Detection by Sentimental Analysis Integrated with Opinion Mining and Deep Learning Architectures

The rapid growth of social networking sites in the Internet era has made them a necessary tool for sharing emotions with the entire world. To extract emotions from text, a variety of tools and approaches are available in fields of opinion mining as well as sentiment analysis. These researches propose novel technique opinion mining based emotion detection from the input social content using deep learning architectures. Here the input has been obtained as social media content based on opinion miningby 5G networks. The input has been processed for noise removal, smoothening and normalization. This processed input has been segmented using Markov model based convolutional neural networks (MMCNN). The segmented data has been classified using Canonical Correlation AnalysisBayesian neural network.An opinion mining method that analyzes statements regarding computer programming and predicts or recognizes their polarity was implemented, along with an earlier module that was integrated into an intelligent learning environment. These three steps made up the creation of the module. We assessed the corpus, text polarity precision, and emotion recognition. Experimental analysis has been carried out for various social media content collected by opinion mining in terms of accuracy, precision, recall, F-1 score, AUC.Proposed technique attained accuracy of 99%, precision of 96%, recall of 96%, F-1 score of 95%, AUC of 89%

Performance and Emissions of Nanoadditives in Diesel Engine: A review

Nowadays, the demand for energy and fossil fuels has widely increased as a result of the continuous growth of the population. However, the continued use of traditional fuels as the primary source of energy has resulted in various environmental challenges related to climate change and global warming. This has prompted researchers to look for more eco-friendly and sustainable fuel alternatives with a minimal amount of engine modification and emission treatment techniques. Amongst the suggested alternative fuels, biofuels, biofuel/diesel blends, and the incorporation of nanoparticles into fuels. The nanoparticle diesel additives played a vital role in increasing engine performance as well as retarding harmful emissions such as nitrogen oxides (NOx), carbon monoxide (CO), unburned hydrocarbon (UHC), and particulate matter (PM). Metal-oxides nanoadditive such as aluminum oxide (Al2O3), ceric oxide (CeO2), and titanium dioxide (TiO2) act as oxygen catalysts and promote proper mixing of fuel and air, resulting in more efficient combustion and decreased emissions. The incorporation of nanometal-based additives, including iron (Fe), copper (Cu), and aluminum (Al) accelerated the fuel evaporation rate and increased the probability of fuel ignition. Carbon-based nanoparticles such as carbon nanotubes (CNTs), graphene nanoplatelets (GNPs), and graphene oxide (GO) are promising fuel nanoadditives owing to their metal-free composition. In addition, carbon-based additives enhanced the thermal conductivity of fuel and increased active sites available for chemical reactions, which led to improved engine performance

Determination of Noise Caused by Ventilated Brake Disc with Respect to the Rib Shape and Material Properties Using Taguchi Method

Ventilated brake discs may have various configurations of ribs and can be manufactured from different materials. In order to improve the performance in extreme exploitation conditions, it is necessary that they heat up and wear as little as possible, and that they have good heat dissipation capacity and generate low noise. To achieve this, optimization of the influential parameters is required. In this study, the optimization and the analysis of the frequency value were made on the basis of the influential parameters, such as brake disc vane shape, density, Young’s modulus, and Poisson\u27s coefficient. A numerical investigation was conducted using the ANSYS software package in the MODAL module. In order to better understand which parameter has the greatest influence on the noise formation, the Taguchi method was applied. By applying the Analysis of Variance – ANOVA, the influence of each parameter on frequency, expressed as a percentage, was determined. The obtained results show that the most influential parameter is the shape of the ribs (90.82%), followed by Young’s modulus (8.26%) and density (0.89%)

Optimization of robust and LQR control parameters for half car model using genetic algorithm

To test the performance of the half car system, two types of controller are used, namely Robust H-infinity control and LQR control. Robust H-infinity and LQR controller is designed to control the suspension system and to reduce the vibrations in the car and to improve handling. A half car model is considered in this research to study the effects in passenger owing to different road profiles. The weights of Robust H-infinity and LQR controller are obtained using Genetic Algorithm on a half car model with two different types of usually existing road disturbance.The design parameters of both the active controller varies with various road profiles. This proves that particular design parameters in Robust and LQR controller do not have the ability to adapt to the variations in road surface. Furthermore, active controllers significantly improve the performance of the system in all aspects when compared to passive system. © 2019 SERSC

Vibration Signals of Small Vertical Axis Wind Turbines

In recent years, progress has been made in increasing the renewable energy share in the power sector particularly in the wind. The experimental study conducted in this paper aims to investigate the effects of number of blades and inflow wind speed on vibration signals of a vertical axis Savonius type wind turbine. The operation of the model of Savonius type wind turbine is conducted to compare two, three and four blades wind turbines to show vibration amplitudes related with wind speed. It is found that the increase of the number of blades leads to decrease of the vibration magnitude. Furthermore, inflow wind speed has reduced effect on the vibration level for higher number of blades

Control of using robust H∞ control with genetic algorithm

Better ride comfort and controllability of vehicles are pursued by automotive industries by considering the use of suspension system which plays a very important role in handling and ride comfort characteristics. Comprehensive comparison on half car model was conducted to analyze the effect of active suspension system, using Robust H-infinity on the model. Passive suspension system is also compared with active suspension technique for the purpose of benchmarking. Parametric uncertainties are used to model the non-linearities associated in the system. Genetic Algorithm is used to develop weighting function for robust control design purpose. Comparison of all models also shows that in spite of adding uncertainties in the system, the designed Robust H-infinity controller achieved better settling time than the traditional passive suspension system. © 2019 SERSC

Tribological Properties of Al2O3 Nanoparticles as Lithium Grease Additives

The tribological properties of Lithium grease specimens with different concentrations of Al2O3 nanoparticles were investigated using a pin on disc apparatus under different sliding speeds and normal loads. Results showed that Al2O3 nanoparticles enhanced the tribological properties of lithium grease and reduced the COF and wear scar width by approximately 57.9% and 47.5% respectively