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TL;DR: The fundamental equations of the general three-dimensional problem of magneto-thermoelasticity have been written in the form of an inhomogeneous vector matrix differential equation and solved in the Laplace-Fourier transform-domain by eigenfunction method.
9 citations
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TL;DR: In this paper, the forced vibration of non-uniform axially functionally graded (AFG) Timoshenko beam on elastic foundation is performed under harmonic excitation, and a separate free vibration analysis is undertaken to include backbone curves with the frequency response curves in the non-dimensional plane.
Abstract: Abstract Forced vibration of non-uniform axially functionally graded (AFG) Timoshenko beam on elastic foundation is performed under harmonic excitation. A linear elastic foundation is considered with three different classical boundary conditions. AFG materials are an advanced class of materials that have potential for application in various engineering fields. In the present work, variation of material properties along the longitudinal axis of the beam are considered according to power-law forms. Five values of material gradation parameter provides different functional variation and their effect on the frequency response of the system is studied. The present approximate method is displacement based and Von-Karman type of geometric nonlinearity is considered with rotational component to incorporate transverse shear. Hamilton’s principle is used to derive nonlinear set of governing equation and Broyden method is implemented to solve the nonlinear equations numerically. The results are successfully validated with previously published article. Frequency vs. amplitude curve corresponding to different combinations of system parameters are presented and are capable of serving as benchmark results. A separate free vibration analysis is undertaken to include backbone curves with the frequency response curves in the non-dimensional plane.
9 citations
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08 Mar 2009TL;DR: This paper has proposed an algorithmic technique to detect the edge of any kind of true gray scale images considering the artificial features of the image as the feature set which is fed to K-Means algorithm for clustering the image and there to detect clearly the edges of the objects present in the considered image.
Abstract: Edge detection is a problem of fundamental importance in image analysis. Many approaches for edge detection have already revealed more are waiting to be. But edge detection using K-means algorithm is the most heuristic and unique approach. In this paper, we have proposed an algorithmic technique to detect the edge of any kind of true gray scale images considering the artificial features of the image as the feature set which is fed to K-Means algorithm for clustering the image and there to detect clearly the edges of the objects present in the considered image. The artificial features, which we have considered here, are mean, standard deviation, entropy and busyness of pixel intensity values.
9 citations
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TL;DR: In this paper, a multi-item periodic review probabilistic inventory model in fuzzy environment is investigated, where a single source of the inventory model with zero lead time and varying order cost under two limitations, such as holding cost and safety stock.
Abstract: This paper investigates a multi-item periodic review probabilistic inventory model in fuzzy environment. Here, we have considered a single source of the inventory model with zero lead time and varying order cost under two limitations, such as holding cost and safety stock. By employing the fuzzy expectation and possibility/necessity measure, the fuzzy model is transformed into an equivalent deterministic non-linear programming problem. Finally, the model is illustrated with the help of numerical example and few sensitivity analyses are also presented for different parameter to show the validity of the proposed model.
9 citations
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TL;DR: A neural network model whose architecture combines several multilayer perceptron networks (MLPs) to realize better performance after capturing the seasonality effect in the atmospheric data is proposed.
Abstract: In this paper a neural network based method of local rainfall prediction is proposed. This method is developed based on past observations on various atmospheric parameters such as temperature, relative humidity, vapor presser, etc. We propose a neural network model whose architecture combines several multilayer perceptron networks (MLPs) to realize better performance after capturing the seasonality effect in the atmospheric data. We also demonstrate that the use of appropriate features can further improve the performance in prediction accuracy. These observations inspired us to use a feature selection MLP, FSMLP, (instead of MLP) which can select good features on-line while learning the prediction task. The FSMLP is used as a preprocessor to select good features. The combined use of FSMLP and SOFM-MLP results in a network system that uses only very few inputs but can produce good prediction.
9 citations
Authors
Showing all 581 results
Name | H-index | Papers | Citations |
---|---|---|---|
Debnath Bhattacharyya | 39 | 578 | 6867 |
Samiran Mitra | 38 | 198 | 5108 |
Dipankar Chakravorty | 35 | 369 | 5288 |
S. Saha Ray | 34 | 217 | 3888 |
Tai-hoon Kim | 33 | 526 | 4974 |
Anindya Sen | 29 | 109 | 3472 |
Ujjal Debnath | 29 | 335 | 3828 |
Anirban Mukhopadhyay | 29 | 169 | 3200 |
Avijit Ghosh | 28 | 121 | 2639 |
Mrinal K. Ghosh | 26 | 64 | 2243 |
Biswanath Bhunia | 23 | 75 | 1466 |
Jayati Datta | 23 | 55 | 1520 |
Nabarun Bhattacharyya | 23 | 136 | 1960 |
Pinaki Bhattacharya | 19 | 114 | 1193 |
Dwaipayan Sen | 18 | 71 | 1086 |