Search Results

You are looking at 1 - 4 of 4 items for

  • Author: Om Prakash x
Clear All Modify Search
Open access

Tarlochan Singh Mahajan and Om Prakash Pandey

Abstract

Effect of static magnetic field on germination of mung beans is described. Seeds of mung beans, were exposed in batches to static magnetic fields of 87 to 226 mT intensity for 100 min. Magnetic time constant - 60.743 Th (Tesla hour) was determined experimentally. High value of magnetic time constant signifies lower effect of magnetic field on germination rate as this germination was carried out at off-season (13°C). Using decay function, germination magnetic constant was calculated. There was a linear increase in germination magnetic constant with increasing intensity of magnetic field. Calculated values of mean germination time, mean germination rate, germination rate coefficient, germination magnetic constant, transition time, water uptake, indicate that the impact of applied static magnetic field improves the germination of mung beans seeds even in off-season

Open access

Anshika Srivastava, Ram Krishna Pandey and Om Prakash

Abstract

This paper concerns the problem of determining or estimating the maximal upper density of the sets of nonnegative integers S whose elements do not differ by an element of a given set M of positive integers. We find some exact values and some bounds for the maximal density when the elements of M are generalized Fibonacci numbers of odd order. The generalized Fibonacci sequence of order r is a generalization of the well known Fibonacci sequence, where instead of starting with two predetermined terms, we start with r predetermined terms and each term afterwards is the sum of r preceding terms. We also derive some new properties of the generalized Fibonacci sequence of order r. Furthermore, we discuss some related coloring parameters of distance graphs generated by the set M.

Open access

Om Prakash, K.P. Sudheer and K. Srinivasan

Abstract

This paper presents a novel framework to use artificial neural network (ANN) for accurate forecasting of river flows at higher lead times. The proposed model, termed as sequential ANN (SANN), is based on the heuristic that a mechanism that provides an accurate representation of physical condition of the basin at the time of forecast, in terms of input information to ANNs at higher lead time, helps improve the forecast accuracy. In SANN, a series of ANNs are connected sequentially to extend the lead time of forecast, each of them taking a forecast value from an immediate preceding network as input. The output of each network is modified by adding an expected value of error so that the residual variance of the forecast series is minimized. The applicability of SANN in hydrological forecasting is illustrated through three case examples: a hypothetical time series, daily river flow forecasting of Kentucky River, USA and hourly river flow forecasting of Kolar River, India. The results demonstrate that SANN is capable of providing accurate forecasts up to 8 steps ahead. A very close fit (>94% efficiency) was obtained between computed and observed flows up to 1 hour in advance for all the cases, and the deterioration in fit was not significant as the forecast lead time increased (92% at 8 steps ahead). The results show that SANN performs much better than traditional ANN models in extending the forecast lead time, suggesting that it can be effectively employed in developing flood management measures.

Open access

Mukesh Prasad, Yu-Ting Liu, Dong-Lin Li, Chin-Teng Lin, Rajiv Ratn Shah and Om Prakash Kaiwartya

Abstract

A novel data knowledge representation with the combination of structure learning ability of preprocessed collaborative fuzzy clustering and fuzzy expert knowledge of Takagi- Sugeno-Kang type model is presented in this paper. The proposed method divides a huge dataset into two or more subsets of dataset. The subsets of dataset interact with each other through a collaborative mechanism in order to find some similar properties within each-other. The proposed method is useful in dealing with big data issues since it divides a huge dataset into subsets of dataset and finds common features among the subsets. The salient feature of the proposed method is that it uses a small subset of dataset and some common features instead of using the entire dataset and all the features. Before interactions among subsets of the dataset, the proposed method applies a mapping technique for granules of data and centroid of clusters. The proposed method uses information of only half or less/more than the half of the data patterns for the training process, and it provides an accurate and robust model, whereas the other existing methods use the entire information of the data patterns. Simulation results show the proposed method performs better than existing methods on some benchmark problems.