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Theoretical prediction and validation of global horizontal solar irradiance for a tropical climate in India

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Frontiers in Energy 2015年第3期9卷 311-321页

作者： Sivasankari SUNDARAM Jakka sarat.chandra BABU Department of Chemical Engineering National Institute of Technology Tiruchirappalli 620015 India

This paper aims to propose monthly models responsible for the theoretical evaluation of the global horizontal irradiance of a tropical region in India which is Sivagangai situated in Tamilnadu. The actual measured global horizontal irradiance hails from a 5 MW solar power plant station located at Sivagangai in Tamilnadu. The data were monitored from May 2011 to April 2013. The theoretical assessment was conducted differently by employing a programming platform called Microsoft Visual Basic 2010 Express. A graphical user interface was created using Visual Basic 2010 Express, which provided the evaluation of empirical parameters for model formulation such as daily sunshine duration （5）, maximum possible sunshine hour duration （S0）, extra terrestrial horizontal global irradiance （H0） and extra terrestrial direct normal irradiance （G0）. The proposed regression models were validated by the significance of statistical indicators such as mean bias error, root mean square error and mean percentage error from the predicted and the actual values for the region considered. Comparison was made between the proposed monthly models and the existing normalized models for global horizontal irradiance evaluation.

关键词： global horizontal irradiance (GHI) mean biaserror root mean square error mean percentage error,coefficient of regression Visual Basic 2010 Express

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

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Financial Innovation 2019年第1期5卷 645-678页

作者： sarat.chandra Nayak Bijan Bihari Misra Department of Computer Science and Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Information Technology Silicon Institute of TechnologyBhubaneswar 751024India

Accurate prediction of stock market behavior is a challenging issue for financial *** neural networks,such as multilayer perceptron have been established as better approximation and classification models for this *** study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock *** input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this *** was chosen because it requires fewer control parameters and has a faster convergence *** statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock *** performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be *** conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be *** model can be used as a promising tool for financial forecasting.

关键词： Artificial neural network Neuro-fuzzy network Multilayer perceptron Chemical reaction optimization Stock market forecasting Financial time series forecasting

Extreme learning with chemical reaction optimization for stock volatility prediction

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Financial Innovation 2020年第1期6卷 290-312页

Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based ***,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network ***,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned *** this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application *** is characterized by faster convergence capability and requires fewer tunable *** study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction *** evaluate its performance by predicting the daily volatility and closing prices of BSE ***,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm *** signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed ***,this model can be used as a promising tool for financial forecasting.

关键词： Extreme learning machine Single layer feed-forward network Artificial chemical reaction optimization Stock volatility prediction Financial time series forecasting Artificial neural network Genetic algorithm Particle swarm optimization

Estimating stock closing indices using a GA-weighted condensed polynomial neural network

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Financial Innovation 2018年第1期4卷 311-332页

作者： sarat.chandra Nayak Bijan Bihari Misra Department of Computer Science and Engineering CMR College of Engineering&Technology(Autonomous)501401HyderabadIndia Department of Information Technology Silicon Institute of Technology751024BhubaneswarIndia.

Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable ***,predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high volatility and *** paper proposes a novel condensed polynomial neural network(CPNN)for the task of forecasting stock closing price *** developed a model that uses partial descriptions(PDs)and is limited to only two layers for the PNN *** outputs of these PDs along with the original features are fed to a single output neuron,and the synaptic weight values and biases of the CPNN are optimized by a genetic *** proposed model was evaluated by predicting the next day’s closing price of five fast-growing stock indices:the BSE,DJIA,NASDAQ,FTSE,and *** comparative testing,the proposed model proved its ability to provide closing price predictions with superior ***,the Deibold-Mariano test justified the statistical significance of the model,establishing that this approach can be adopted as a competent financial forecasting tool.

关键词： Stock market forecasting Polynomial neural network Partial description Genetic algorithm Multilayer perceptron

Graphene derivatives as efficient hole transport materials for lead-free double perovskite（Cs2SnI6） solar cells: a numerical study

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Optoelectronics Letters 2025年第3期21卷 155-159页

作者： Sarita YADAV Saral K.GUPTA chandra Mohan Singh NEGI Department of Physical Science Banasthali Vidyapith

The double perovskite Cs2SnI6has notable optical and electrical characteristics, rendering it a highly prospective candidate for deployment as the absorber layer in perovskite solar cells （PSCs）. We simulated the performance of PSCs using lead-free Cs2SnI6double perovskite absorber layer and graphene derivatives, namely graphene oxide （GO） and reduced graphene oxide （rGO）, as hole transport layers （HTLs）. Our findings show that r GO offers an excellent hole extraction property with minimal interfacial recombination compared to GO.

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采用解析和数值方法分析浮动式矩形浮箱的波浪诱导载荷研究

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哈尔滨工程大学学报（英文版） 2024年第1期23卷 113-126页

作者： sarat.chandra mohapatra Iuri Baldaconi da Silva Bispo Yuchan Guo C.Guedes Soares Center for Marine Technology and Ocean Engineering(CENTEC) Instituto Superior TécnicoUniversidade de LisboaLisboa 1049-001Portugal

A three-dimensional mathematical hydrodynamic model associated with surface wave radiation by a floating rectangular box-type structure due to heave,sway,and roll motions in finite water depth is investigated based on small amplitude water wave theory and linear structural *** analytical expressions for the radiation potentials,wave forces,and hydrodynamic coefficients are presented based on matched eigenfunction expansion method(MEFEM).The correctness of the analytical results of wave forces is compared with the construction of a numerical model using the open-source boundary element method code *** addition,the present result is compared with the existing published experimental results available in the *** effects of the different design parameters on the floating box-type rectangular structure are studied by analyzing the vertical wave force,horizontal wave force,torque,added mass,and damping coefficients due to the heave,sway,and roll motions,and the comparison analysis between the forces is also analyzed in ***,the effect of reflection and transmission coefficients by varying the structural width and drafts are analyzed.

关键词： Mathematical model MEFEM Box-type structure BEM code NEMOH Wave forces

Dynamic Mechanical Behavior of <i>Luffa cylindrica</i>Fiber-Resorcinol Composites

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Open Journal of Composite Materials 2015年第1期5卷 22-29页

作者： Chhatrapati Parida Chinmay Pradhan sarat Kumar Dash sarat.chandra Das Depatment of Physics Orissa University of Agriculture and Technology Bhubaneswar India Post Graduate Department of Botany Vani Vihar Utkal University Bhubaneswar India Department of Education in Science and Mathematics National Council of Educational Research and Training Bhubaneswar India Department of Chemistry Salipur College Salipur India

Dynamic mechanical behaviour of resorcinol-formaldehyde matrix and its composites reinforced with natural fibers of Luffa cylindrica (LC) has been studied. The effects of fiber loading, alkali treatment on fiber, temperature and frequency on storage modulus and mechanical-loss factor of the composites were studied. The dynamic mechanical behaviour of the composites and pure matrix has been investigated in the frequency range from 0.1 Hz to 10 Hz and temperature range from 26℃ to 100℃. The experimental results show that the values of storage modulus of the composites increase with increase in fiber loading. The storage modulus of treated LC fiber composites were found to be enhanced when compared with the untreated fiber composites. It was also found that mechanical-loss factor was more when untreated LC fibers were incorporated in the composites and decreased with the incorporation of treated LC fiber. The storage modulus of all the composites increased with frequency but decreased with rise of temperature. The glass transition temperature of the composites was evaluated from the peaks of tan delta variations.

关键词： LC Storage Modulus Mechanical Loss Factor Tan Delta Visco-Elastic Nature

Ion Conduction in Superionic Glassy Electrolytes:An Overview

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Journal of Materials Science & Technology 2013年第3期29卷 193-208页

作者： Angesh chandra Alok Bhatt Archana chandra Department of Applied Physics Shri Shankaracharya Institute of Prof.Manag.& Tech.Raipur 492015C.G.India Department of Applied Physics M.P Christian College of Engg.& Tech.Bhilai 490026C.G.India

The various theoretical and experimental models for ion conduction mechanism of fast ion conducting （FIC） glass electrolytes have been reported in the present review paper. Some characterization techniques of FIC glasses are presented. The experimental methods for determination of some ion transport parameters viz ionic conductivity （σ）, ionic mobility （μ）, mobile ion concentration （n）, ionic drift velocity （Vd）, ionic transference number （tion） and activation energies of FIC glasses are explained. The solid state battery fabrication by using some FIC glasses is also reported.

关键词： Ion conduction Glassy solid electrolyte Fast ion conducting (FIC) glass Superionic solid Relaxation phenomenon

PEO-PVP BLENDED Na^+ION CONDUCTING SOLID POLYMERIC MEMBRANES

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Chinese Journal of Polymer Science 2013年第11期31卷 1538-1545页

作者： Angesh chandra Department of Applied Physics Shri Shankaracharya Institute of Professional Management & Technology

Polyethylene oxide （PEO）-polyvinylpyrrolidone （PVP） blended Na＋ ion conducting solid polymeric membranes：（1-x） [75PEO：25NaPO3] ＋ x PVP, where 0 〈 x 〈 12 wt%, are reported. The polymeric blending was done using a solvent- free hot-press method. Two orders of conductivity enhancement （σca. 1.07× 10^-5 S·cm^-1） have been achieved with 3 wt% of PVP （i.e. the composition： [97（75PEO：25NaPO3）＋ 3PVP]）, from that of the pure host：（75PEO：25NaPO3）. The conductivity enhancement in PEO-PVP blended solid polymeric membranes have been explained by the ionic conductivity, ionic mobility and mobile ion concentration measurements. Materials characterization and polymer-salt complexation were done with the help of X-ray diffraction （XRD）, scanning electron microscopy （SEM）, differential scanning calorimetry （DSC） and thermo gravimetric analysis （TGA） studies. The temperature dependent conductivity studies have also been done to compute the activation energy （Ea） values from lgσ-1/TArrhenius plots. A solid state polymeric battery was fabricated by using optimum conducting composition of solid polymer electrolyte （SPE OCC）, and some important cell parameters were also calculated from the discharge profile of the cell.

关键词： Polymer blending Activation energy Ionic conductivity SEM XRD DSC TGA.