19 research outputs found
Pre-Silicon Power Leakage Assessment Framework using Generative Adversarial Networks
No full textAs of 2021, the world economic forum deems cyber-security failures as one of the most potent threats to the world. According to a McAfee report, the cost of cybercrimes in 2020 reached nearly 1 trillion US dollars, which was around 50 percent more than what it was in 2018. Exacerbating the already mammoth financial implication of such a failure is the ever-growing diversity in cyber attacks. Side-channel analysis is one such attack type wherein the information leaked via the implementation of a cryptographic algorithm is leveraged to obtain secret data, rather than any weaknesses in the cryptographic algorithm itself. This leaked information, amongst others, can be in terms of power, EM radiation, or the time taken to perform a cryptographic operation. Countermeasures against such side-channel attacks aim at reducing the amount of information leaked via the side channels or reducing the correlation between the secret operations and the information leaked. Manufacturing the chip is often a prerequisite for evaluating the efficacy of such countermeasures, which is a costly and time-consuming process. Thus, the security evaluation of a design has a substantial impact on the design cost and the time to market. In case the design does not meet minimum security requirements, it has to be redesigned and manufactured, increasing not only costs but also the design time considerably. Hence, there is a need for pre-silicon leakage assessment tools that can provide designers a sense of certainty about the security aspects of their design. However, the existing pre-silicon leakage assessment tools are either deemed unreliable or too slow to be used to perform power leakage assessment, which is the problem this thesis aims to ameliorate. This thesis explores the use of generative adversarial networks (GANs) for generating synthetic power traces. Generative deep learning has been used in various domains like computer vision, audio, and even for medical data like ECG. GANs have been introduced in the context of side-channel attacks to enlarge the size of the profiling dataset for carrying out profiled side-channel attacks. In this work, we propose a robust methodology to condition and train GANs to generate power traces that can be used to carry out leakage assessment. This methodology can even be extended to support the design space exploration of countermeasures by providing reliable leakage assessment at design time. The generated power traces are not only indistinguishable but also as attackable as the real traces. The conditioning technique helps the GAN to generalize to various scenarios and the proposed framework provides a speed-up of around 140 times over traditional CAD methods to simulate power traces while maintaining their structure and accuracy.Computer Engineerin
Pathways to adoption of CCS in India
No full textThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 83-85).India is the world's second most populous country with a rapidly growing economy and increasing emissions. With the imminent threat of anthropogenic climate change in the coming decades, helping to control India's emissions will have to be a global priority. Carbon capture and sequestration (CCS) can play a pivotal role in reducing India's emissions in the future, given India's reliance on coal power and the large coal reserves. The motivation for this dissertation is the need to ascertain the current situation and conditions relevant to carbon capture in India so as to help guide the processes to prepare for large scale adoption if desired in the future. For carbon capture to be undertaken at a significant scale, various pieces will have to fall in to place in sync with each other. The technological capability would have to be complemented by adequate geological capacity under the umbrella of the right policies. Adoption of carbon capture would need a tailored approach for each country and for a diverse country the size of India, these approaches may need to be customized even locally to each region.(cont.) The objective of this thesis is to increase the understanding of the opportunities, issues and challenges amongst the stakeholders regarding CCS in India regarding the capacity, political structures and policies. To address the objective, this dissertation analyzes the current power and coal sector situations, geological capacity for sequestration in India, the political decision making structures and the current views of the relevant civil servants in this field. At the end, there are some recommendations for the government of India and the international climate and CCS community to make conditions conducive for CCS in India.by Mudit Narain.S.M
Total productive maintenance (TPM); as a vital function in manufacturing systems
Full text link[EN] Maintenance is a vital function in manufacturing systems for maintaining quality. During the coming period two ideas are evolved Total Productive Maintenance and Total Quality Management simultaneously with other facts to attain excellence in production systems. Various feature of putting into practice Total Productive Maintenance (TPM) is expressed and examined in this study. Total productive maintenance is explained thoroughly, the philosophy, planning, improvements, goal setting and developments of implementation plans. The eight pillars of TPM are explained. Various aspect of implementation of TPM is elaborated. Also the benefits of TPM are explained.Saxena, MM. (2022). Total productive maintenance (TPM); as a vital function in manufacturing systems. Journal of Applied Research in Technology & Engineering. 3(1):19-27. https://doi.org/10.4995/jarte.2022.15934OJS192731Ahuja, I.P.S., & Khamba, J.S. (2008). Total productive maintenance: literature review and directions. International Journal of Quality & Reliability Management, 25(7), 709-756. https://doi.org/10.1108/02656710810890890Mad Lazim, H., Ramayah, T., & Ahmad, N. (2008). Total Productive Maintenance and Performance: A Malaysian SME Experience. International Review of Business Research Papers, 4(4), 237-250.Ireland, F., & Dale, B.G. (2001). A study of total productive maintenance implementation. Journal of Quality in Maintenance Engineering, 7(3), 183-191. https://doi.org/10.1108/13552510110404495McKone, K.E., Schroeder, R.G., & Cua, K.O. (2001). The impact of total productive maintenance practices on manufacturing performance, Journal of Operations Management, 19. https://doi.org/10.1016/S0272-6963(00)00030-9Pomorski, T.R. (2004). Total Productive Maintenance (TPM) Concepts and Literature Review. Principal Consulting Engineer Brooks Automation, Inc.Robbins, R. (2008). Overall Equipment Effectiveness. Control Engineering, 55(1), 64.Haddad, T.H., & Jaaron, A.A.M. (2007). Lean tpm for healthcare facilities: an implementation methodology. Proceedings of the Third POMS-HK International Conference, 2007
Six Sigma Methodologies and its Application in Manufacturing Firms
No full textSix Sigma is a methodology for process improvement as well as a statistical concept that looks for to determine the variation intrinsic in any process. Six Sigma represents process, that is having 3.4 defects per million opportunities. i.e. 99.99966 % of the products from a Six Sigma process are perfect. Firms can impact their sigma level by combining main principles from the Six Sigma methodology into leadership styles, process management, and improvement activities. Main principle of the technique is a focus on the customer. There are many challenges in the implementation of Six Sigma. A well-run manufacturing team can make the entire firm more successful through cost-saving measures, increased quality and a larger inventory of products that the company can market. The Six Sigma objective is to make sure the process has minimum defects(3.4 defects per million chances). Every aspect of the process must be carefully planned and documented in detail in order for manufacturing to go efficiently. The main aspect of Six Sigma for enhancement in the manufacturing industry is to maximize the financial returns
Study on the effect of trailing edge serrations: For laminar boundary layer instability noise
No full textWings operated at low and moderate Reynolds number such as the ones of UAVs or the blades of small turbine and of compressors, can be the source of an aero-acoustic phenomenon called laminar boundary layer instability noise. The narrow band noise can be attenuated using trailing edge (TE) serrations (Chong et al. 2013, Moreau et al. 2012) but the mechanism behind tonal attenuation is yet to be investigated in detail. An experimental study (1.32X105 < Re < 5.30X105) is conducted in an open-jet low turbulence wind tunnel using NACA 0018 airfoil (c = 0.20m) with modified TE. The acoustic emission in the far field is recorded using far-field microphones whereas the developing field near the TE is studied using Time resolved planar particle image velocimetry and oil flow visualization techniques. Study showed the serration-3 (2h = 20mm, λ = 10mm) has maximum tonal noise attenuation and effect the point of separation of laminar separation bubble. The span-wise change in flow characteristics in case of serrations can be attributed to the effective chord-length of the wing at each span-wise position. Further span-wise correlation of chord-wise flow fluctuations show the flow being turbulent upstream of serration-3 TE modification.Aerospace Engineerin
Multiorgan Failure Associated with Epstein-Barr Viremia-An Elusive Diagnosis: A Case Series
No full text
Analyzing Bagging Methods for Language Models
Full text linkModern language models leverage increasingly large numbers of parameters to
achieve performance on natural language understanding tasks. Ensembling these
models in specific configurations for downstream tasks show even further
performance improvements. In this paper, we perform an analysis of bagging
language models and compare single language models to bagged ensembles that are
roughly equivalent in terms of final model size. We explore an array of model
bagging configurations for natural language understanding tasks with final
ensemble sizes ranging from 300M parameters to 1.5B parameters and determine
that our ensembling methods are at best roughly equivalent to single LM
baselines. We note other positive effects of bagging and pruning in specific
scenarios according to findings in our experiments such as variance reduction
and minor performance improvements
