Rajasthan Technical University

About: Rajasthan Technical University is a education organization based out in Kota, Rajasthan, India. It is known for research contribution in the topics: Photovoltaic system & PID controller. The organization has 716 authors who have published 1084 publications receiving 4530 citations. The organization is also known as: RTU.

Travelling Salesman Problem Optimization Using Hybrid Genetic Algorithm

Abstract: In present, travelling salesman problem (TSP) has given more priority due to its analogy real applications like transportation of goods, distribution of the supplies, power cable distribution, tourism, routing of public transport, laser printing, drilling operation Genetic algorithm (GA) class is used to find the optimal solution but as problem increases, robustness decreases due to very slow convergence rate In this study, a hybrid GA is proposed in order to optimize the TSP efficiently The robustness was increased at higher distance by the developed hybrid GA

Use of 2–methoxyethyl ether and nitromethane as oxygenated additives for performance improvement and emission reduction of CI engine: experimental investigation and numerical simulation

Abstract: Diesel engines are playing a vital responsibility in the field of automobile, agriculture, construction, and power generation. In the present world, much research is going on in the field of renewable energy to replace conventional sources of energy. But it is not very easy to replace diesel engines with other sources due to the better power output and reliability. The emissions from CI engines are very harmful for human health and for the environment. The major emissions are smoke and NOx which need to be controlled in an effective manner. In this work, direct injection variable compression ratio CI engine was used in experimental investigations for determining the combustion characteristics for D–MXEE–NM blends at different compression ratios. By performance analysis and exhaust emission of engine at peak load, D–MXEE5–NM2.5 (diesel 92.5%, 2–methoxyethyl ether 5%, and nitromethane 2.5%) blend was identified as best blend among all tested fuel blends and pure diesel at normal compression ratio (17.5). Further, all considered fuels with different CR values at peak load were ranked by Entropy–VIKOR method. From the analysis, D–MXEE5–NM2.5 at CR 19.5 was found as best fuel blend (ranked first) among all fuel blends and different compression ratios considered with same experimental conditions. By comparison of best fuel blend D–MXEE5–NM2.5 (at advanced compression ratio 19.5) with diesel (at standard CR 17.5), emission decline (HC 66.66%, CO 70.00%, and smoke 16.09%) and performance improvement (decrement in BSFC 7.07% and increment in BTE 4.41%) were obtained significantly at peak load. However, negligible increment in NOx (3.58%) was observed.

Stock Market Predictions Using FastRNN, CNN, and Bi-LSTM-Based Hybrid Model

Abstract: Accurate predictions of the stock values in fast fluctuating high-frequency financial data is always a challenging task. In this work, we aim to develop deep learning-based hybrid model for live predictions of stock values. The proposed model is a hybrid deep learning model, utilizing the best features of Fast Recurrent Neural Networks (FastRNN), Convolutional Neural Networks (CNN), and Bi-Directional Long Short-Term Memory (Bi-LSMT) models, to predict abrupt changes in the stock prices of a company. For training and validation, we have considered the 1-min time interval stocks data of four companies for a period of one day. The model is aimed to have a low computational complexity so that it can be run for live predictions as well. The model's performance is measured by Root Mean Square Error (RMSE) along with computation time. The model outperforms ARIMA, FBProphet, and other hybrid systems for live predictions of stock values.

The effect of rotation on the rapidly rotating stars under relativistic drag

Abstract: In this short-note, I have discussed spherical deformation in rotationally perturbed spacetime which is valid for strong gravitational fields assuming star is homogenous and spherically sym...

Novel Approach For Spectrum Sensing of Primary use by Hybrid Swarm Intelligence in Cognitive Radios

Abstract: In cognitive radio spectrum sensing is important challenge for secondary user but not challenge for primary user because primary give bandwidth and secondary user acquired bandwidth, when two or more user acquired same bandwidth which increase the error.so reduce the false detection and increase band width is main objective of this paper. In this paper comparison with Particle swarm optimization and hybrid swarm and ANT colony method, In our experiment show the PSO with Ant colony optimisation better throughput and true detection rate.