Articles written in Sadhana
Volume 38 Issue 6 December 2013 pp 1369-1391
Submerged arc welding (SAW) is a fusion joining process, known for its high deposition capabilities. This process is useful in joining thick section components used in various industries. Besides joining, SAW can also be used for surfacing applications. Heat Affected Zone (HAZ) produced within the base metal as a result of tremendous heat of arc is of big concern as it affects the performance of welded/surfaced structure in service due to metallurgical changes in the affected region. This work was carried out to investigate the effect of polarity and other SAW parameters on HAZ size and dilution and to establish their correlations. Influence of heat input on dilution and heat affected zone was then carried out. Four levels of heat input were used to study their effect on % dilution and HAZ area at both the electrode positive and electrode negative polarities. Proper management of heat input in welding is important, because power sources can be used more efficiently if one knows how the same heat input can be applied to get the better results. Empirical models have been developed using statistical technique.
Volume 39 Issue 4 August 2014 pp 879-900
In image denoising algorithms, the noise is handled by either modifying term-by-term, i.e., individual pixels or block-by-block, i.e., group of pixels, using suitable shrinkage factor and threshold function. The shrinkage factor is generally a function of threshold and some other characteristics of the neighbouring pixels of the pixel to be thresholded (denoised). The threshold is determined in terms of the noise variance present in the image and its size. The VisuShrink, SureShrink, and NeighShrink methods are important denoising methods that provide good results. The first two, i.e., VisuShrink and SureShrink methods follow term-by-term approach, i.e., modify the individual pixel and the third one, i.e., NeighShrink and its variants: ModiNeighShrink, IIDMWD, and IAWDMBMC, follow block-by-block approach, i.e., modify the pixels in groups, in order to remove the noise. The VisuShrink, SureShrink, and NeighShrink methods however do not give very good visual quality because they remove too many coefficients due to their high threshold values. In this paper, we propose an image denoising method that uses the local parameters of the neighbouring coefficients of the pixel to be denoised in the noisy image. In our method, we propose two new shrinkage factors and the threshold at each decomposition level, which lead to better visual quality. We also establish the relationship between both the shrinkage factors. We compare the performance of our method with that of the VisuShrink and NeighShrink including various variants. Simulation results show that our proposed method has high peak signal-to-noise ratio and good visual quality of the image as compared to the traditional methods:Weiner filter, VisuShrink, SureShrink, NeighBlock, NeighShrink, ModiNeighShrink, LAWML, IIDMWT, and IAWDMBNC methods.
Volume 43 Issue 3 March 2018 Article ID 0037
Clustering has been recognized as a very important approach for data analysis that partitions the data according to some (dis)similarity criterion. In recent years, the problem of clustering mixed-type data has attracted many researchers. The k-prototypes algorithm is well known for its scalability in this respect. In thispaper, the limitations of dissimilarity coefficient used in the k-prototypes algorithm are discussed with some illustrative examples. We propose a new hybrid dissimilarity coefficient for k-prototypes algorithm, which can be applied to the data with numerical, categorical and mixed attributes. Besides retaining the scalability of the k prototypes algorithm in our method, the dissimilarity functions for either-type attributes are defined on the same scale with respect to their dimensionality, which is very beneficial to improve the efficiency of clustering result. The efficacy of our method is shown by experiments on real and synthetic data sets.
Volume 44 Issue 5 May 2019 Article ID 0110
In this paper, a novel multi-document summarization scheme based on metaheuristic optimization is introduced that generates a summary by extracting salient and relevant sentences from a collection of documents. The proposed work generates optimal combinations of sentence scoring methods and theirrespective optimal weights to extract the sentences with the help of a metaheuristic approach known as teaching–learning-based optimization. In addition, the proposed scheme is compared to two summarization methods that use different metaheuristic approaches. The experimental results show the efficacy of the proposed summarization scheme.