Showing papers by "Indian Institute of Technology Bhubaneswar published in 2009"

Silver(I)-catalyzed dual activation of propargylic alcohol and aziridine/azetidine: triggering ring-opening and endo-selective ring-closing in a cascade.

Abstract: [Ag(COD)2]PF6 catalyzes the reaction between propargyl alcohols and N-tosylaziridines/azetidines leading to a diverse range of N,O-heterocycles, namely, oxazines, oxazepines, and oxazocines via ring-opening and ring-closing in a cascade

Comparative performance evaluation of multiobjective optimization algorithms for portfolio management

Abstract: The objective of portfolio optimization is to find an optimal set of assets to invest on, as well as to determine the optimal investment for each asset. This optimal selection and weighting of assets is a multi-objective problem where total profit of investment has to be maximized and total risk is to be minimized. In this paper the portfolio optimization problem is solved using three different multi-objective algorithms and their performance have been compared in terms of Pareto fronts, the delta, C and S metrics. Exhaustive simulation study of various portfolios clearly demonstrates the superior portfolio management capability of non-dominated sorting genetic algorithm II (NSGA II) based method compared to other two methods.

Efficient sales forecasting using PSO based adaptive ARMA model

Abstract: The paper proposes a new hybrid forecasting model using auto regressive moving average (ARMA) as basic architecture and particle swarm optimization (PSO) as learning algorithm. These two combinations have yielded an efficient prediction model for retail sales volumes. To facilitate comparison ARMA, functional link artificial neural network (FLANN) and MLP models are also simulated. The performance of the new model has been evaluated through simulation study and the results demonstrate the best prediction performance both for long and short ranges.

A Recurrent Neural Network Approach to Pulse Radar Detection

Abstract: Matched filtering of biphase coded radar signals create unwanted sidelobes which may mask some of the desired information. This paper presents a new approach for pulse compression using recurrent neural network (RNN). The 13-bit and 35-bit barker codes are used as input signal codes to RNN. The pulse radar detection system is simulated using RNN. The results of the simulation are compared with the results obtained from the simulation of pulse radar detection using Multilayer Perceptron (MLP) network. The number of input layer neurons is same as the length of the signal code and three hidden neurons are taken in the present systems. The Simulation results show that RNN yields better signal-to-sidelobe ratio (SSR) and doppler shift performance than neural network (NN) and some traditional algorithms like auto correlation function (ACF) algorithm. It is also observed that RNN based system converges faster as compared to the MLP based system. Hence the proposed RNN provides an efficient means for pulse radar detection. Keywords—RNN, pulse compression, ACF, SSR, biphase code.

Generation of Pulse Compression Codes Using NSGA-II

Abstract: Pulse compression technique avoids the transmission of a signal having small pulse width and high peak power for better range resolution by transmitting phase or frequency modulated large pulse width signal having comparatively low peak power signal. This paper demonstrates an application of non dominated sorting genetic algorithm-II (NSGA-II), a multiobjective algorithm, to generate biphase pulse compression codes from length 49 to 59. But the aperiodic autocorrelation function(ACF) of the phase coded signals contains range sidelobes which act as self clutter in radar target detection. The efficiency of these codes depends upon the energy content in the range sidelobes of their autocorrelation functions. Peak sidelobe level (PSL) and integrated side lobe level (ISL) are the two performance measures for pulse compression codes. A code is chosen for an application if the ISL and PSL value is within tolerable limits. In this work PSL and ISL are chosen as the objective functions for generating biphase code. keywords—NSGA-II, pulse compression, PSL, ISL, biphase code.

Development of a Novel Improved Differential Approximation (NIDA) for Analysis of Combined Conduction-Radiation Heat Transfer (CCR) in a Two-Dimensional Enclosure with Participating Medium: An Experimental Validation

Abstract: This numerical investigation addresses the applicability and adaptability of the novel improved differential approximation (NIDA) for combined conduction-radiation heat transfer (CCR) in a two-dimensional enclosure with radiatively participating gray medium. The NIDA is developed by blending the spherical harmonic method with the ray emission model and the zonal method. The first-order spherical harmonics, i.e., P1 approximation results, are improved and validated with a similar type of analysis as in Kim's discrete ordinates method (DOM), the hybrid method, and other CCR models. The application and robustness of the NIDA model for a wide range of radiative properties of the medium has been examined, and results are presented for benchmarking the solution. Furthermore, the favorable agreement of numerically obtained isotherms with interferograms captured by a Mach-Zehnder interferometer demonstrates the realistic nature of the NIDA.

Forecasting of retail sales data using differential evolution

Abstract: The paper aims to develop an efficient forecasting model using differential evolution (DE) based learning rule. The structure chosen is an adaptive linear combiner whose weights are trained using DE. The prediction performance of the resulting model is evaluated by feeding features of retail sales data for different months' ahead prediction. These results are compared with those obtained by GA based approach. The comparison demonstrates improved prediction of sales data by the proposed DE method.

Simulation of granulation index and its utility for predicting percentage granules in UASB reactors

Abstract: This paper researches on the development of a dimensionless number for predicting percentage granulation in Upflow Anaerobic Sludge Blanket (UASB) reactors for treating biodegradable wastewaters. A Biomass Granulation Index (BGI) has been framed based on the experimental work conducted using laboratory scale UASB reactors. This index depends on mixing in the sludge bed, induced due to biogas and superficial upflow velocity of wastewater in UASB reactors. For obtaining proper granulation (percentage granules greater than 50%) resulting in higher Chemical Oxygen Demand (COD) removal efficiency, this paper recommends to design UASB reactors with BGI in the range of 240-560.