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531 results on '"Das, Subhasis"'

3. Fuzzy logic

Author

Ghosh, Anindya, primary, Das, Subhasis, additional, and Saha, Bapi, additional

Published
2024
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5. Rough set

Author

Ghosh, Anindya, primary, Das, Subhasis, additional, and Saha, Bapi, additional

Published
2024
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6. Preface

Author

Ghosh, Anindya, primary, Das, Subhasis, additional, and Saha, Bapi, additional

Published
2024
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8. Introduction

Author

Ghosh, Anindya, primary, Das, Subhasis, additional, and Saha, Bapi, additional

Published
2024
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29. HoLiSwap: Reducing Wire Energy in L1 Caches

Author

Turakhia, Yatish, Das, Subhasis, Aamodt, Tor M., and Dally, William J.

Subjects
Computer Science - Hardware Architecture
Abstract

This paper describes HoLiSwap a method to reduce L1 cache wire energy, a significant fraction of total cache energy, by swapping hot lines to the cache way nearest to the processor. We observe that (i) a small fraction (<3%) of cache lines (hot lines) serve over 60% of the L1 cache accesses and (ii) the difference in wire energy between the nearest and farthest cache subarray can be over 6$\times$. Our method exploits this difference in wire energy to dynamically identify hot lines and swap them to the nearest physical way in a set-associative L1 cache. This provides up to 44% improvement in the wire energy (1.82% saving in overall system energy) with no impact on the cache miss rate and 0.13% performance drop. We also show that HoLiSwap can simplify way-prediction.

Published
2017

38. SNN: Stacked Neural Networks

Author

Mohammadi, Milad and Das, Subhasis

Subjects
Computer Science - Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing
Abstract

It has been proven that transfer learning provides an easy way to achieve state-of-the-art accuracies on several vision tasks by training a simple classifier on top of features obtained from pre-trained neural networks. The goal of this work is to generate better features for transfer learning from multiple publicly available pre-trained neural networks. To this end, we propose a novel architecture called Stacked Neural Networks which leverages the fast training time of transfer learning while simultaneously being much more accurate. We show that using a stacked NN architecture can result in up to 8% improvements in accuracy over state-of-the-art techniques using only one pre-trained network for transfer learning. A second aim of this work is to make network fine- tuning retain the generalizability of the base network to unseen tasks. To this end, we propose a new technique called "joint fine-tuning" that is able to give accuracies comparable to finetuning the same network individually over two datasets. We also show that a jointly finetuned network generalizes better to unseen tasks when compared to a network finetuned over a single task., Comment: 8pages

Published
2016

43. The Development of Poly(lactic acid) (PLA)-Based Blends and Modification Strategies: Methods of Improving Key Properties towards Technical Applications—Review.

Author

Andrzejewski, Jacek, Das, Subhasis, Lipik, Vitali, Mohanty, Amar K., Misra, Manjusri, You, Xiangyu, Tan, Lay Poh, and Chang, Boon Peng

Subjects
*THERMOMECHANICAL properties of metals, *LACTIC acid, *PLASTICS engineering, *THERMAL resistance, *IMPACT strength
Abstract

The widespread use of poly(lactic acid) (PLA) from packaging to engineering applications seems to follow the current global trend. The development of high-performance PLA-based blends has led to the commercial introduction of various PLA-based resins with excellent thermomechanical properties. The reason for this is the progress in the field of major PLA limitations such as low thermal resistance and poor impact strength. The main purpose of using biobased polymers in polymer blends is to increase the share of renewable raw materials in the final product rather than its possible biodegradation. However, in the case of engineering applications, the focus is on achieving the required properties rather than maximizing the percentage of biopolymer. The presented review article discusses the current strategies to optimize the balance of the key features such as stiffness, toughness, and heat resistance of PLA-based blends. Improving of these properties requires molecular structural changes, which together with morphology, crystallinity, and the influence of the processing conditions are the main subjects of this article. The latest research in this field clearly indicates the high potential of using PLA-based materials in highly demanding applications. In the case of impact strength modification, it is possible to obtain values close to 800 J/m, which is a value comparable to polycarbonate. Significant improvement can also be confirmed for thermal resistance results, where heat deflection temperatures for selected types of PLA blends can reach even 130 °C after modification. The modification strategies discussed in this article confirm that a properly conducted process of selecting the blend components and the conditions of the processing technique allows for revealing the potential of PLA as an engineering plastic. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Selection of raw material parameters for multi-response optimization of cotton yarn qualities.

Author

Das, Subhasis and Ghosh, Anindya

Abstract

In this work, a multi-response optimization of cotton yarn quality using desirability function approach has been attempted. Being a natural product, cotton yarn qualities are primarily governed by raw material characteristics. This work aims to resolve the complexity of simultaneous optimization of raw material properties using a hybrid multi-response optimization model, where predictive power of support vector regression and optimization capability of genetic algorithm are employed with desirability function. The individual desirability of cotton fibre qualities is assessed from the six properties, such as fibre strength, elongation, fineness, upper half mean length, uniformity index and short fibre content. The yarn quality parameters, such as yarn strength, yarn elongation, hairiness and unevenness, are combined together to express overall desirability. The optimum cotton quality parameters essential to produce good quality yarn can be determined from the proposed multi-response optimization model. [ABSTRACT FROM AUTHOR]

Published
2024
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