Browsing by Author "Saminathan, R."

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Improving linearity by introducing Al in HfO2 as a memristor synapse device
(2019-11-01)
Chandrasekaran, Sridhar
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Simanjuntak, Firman Mangasa
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Saminathan, R.
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Panda, Debashis
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Tseng, Tseung-Yuen
Artificial synapse having good linearity is crucial to achieve an efficient learning process in neuromorphic computing. It is found that the synaptic linearity can be enhanced by engineering the doping region across the switching layer. The nonlinearity of potentiation and depression of the pure device is 36% and 91%, respectively; meanwhile, the nonlinearity after doping can be suppressed to be 22% (potentiation) and 60% (depression). Henceforth, the learning accuracy of the doped device is 91% with only 13 iterations; meanwhile, the pure device is 78%. A detailed conduction mechanism to understand this phenomenon is proposed.
Quantifying uncertainty of emission estimates in National Greenhouse Gas Inventories using bootstrap confidence intervals
(2012-09-01)
Tong, Lee-Ing
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Chang, Chih-Wei
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Jin, Shin-En
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Saminathan, R.
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交大名義發表
"Greenhouse gas (GHG) emissions have exacerbated global warming, and consequently are the focus of worldwide reduction efforts. Reducing emissions involves accurately estimating GHG emissions and the uncertainty associated with such estimates. The uncertainty of GHG emission estimates is often assessed using the 95% confidence interval. Given a small sample size and non-normal distribution of the underlying population, the uncertainty estimate obtained using the 95% confidence interval may lead to significant bias. Bootstrap confidence interval is an effective means of reducing bias. This work presents a procedure for estimating the uncertainty of GHG emission estimation using bootstrap confidence intervals. Numerical simulation is performed for GHG emission estimates under three distributions (namely normal, log-normal and uniform) to find the 95% confidence intervals and bootstrap confidence intervals. Finally, the accuracy and sensitivity of the uncertainty of various interval estimations are examined by comparing the coverage performance, interval mean and interval standard deviation. Simulation results indicate that the bootstrap intervals are more applicable than the 95% confidence interval given non-normal dataset and small sample size. Moreover, when sample size n is less than 30, the bootstrap confidence interval has a smaller interval length with a smaller deviation than that of the classical 95% confidence interval regardless of whether the data distribution is normal or non-normal. This study recommends a sample size greater than or equal to 9 for estimating the uncertainty of emission estimates. When the sample size n exceeds 30, either the normality-based 95% confidence interval or bootstrap confidence intervals may be used regardless of whether the data distribution is normal or non-normal. A case study of carbon stock from Taiwan demonstrates the feasibility of the proposed procedure. (C) 2012 Elsevier Ltd. All rights reserved."
Uncertainty Assessment of Non-normal Emission Estimates Using Non-Parametric Bootstrap Confidence Intervals
(2016-09)
Tong, L. I.
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Saminathan, R.
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Chang, C. W.
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工業工程與管理學系
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Department of Industrial Engineering and Management
Mitigating global warming problems initially involves reducing greenhouse gas (GHG) emissions, therefore the uncertainty of GHG emission estimates needs to be assessed concisely. Although the uncertainty of GHG emission estimates is generally evaluated using classical confidence interval, quantifying the uncertainty based on non-normal GHG emission estimates or small dataset may lead to a significant bias. Using bootstrap confidence intervals is an effective means of reducing such a bias. This study presents a procedure for constructing four bootstrap confidence intervals to assess the uncertainty of GHG emission estimates for three non-normal distributions (namely, Weibull, Gamma and Beta). These bootstrap confidence intervals are standard bootstrap (SB) confidence interval, percentile bootstrap (PB) confidence interval, Bias-corrected percentiles bootstrap (BCPB) confidence interval and bias-corrected and accelerated (BCa) confidence interval. The sensitivity of bootstrap intervals for emission data is examined under various combinations of sample size and parameters of underlying non-normal distributions using three indices: coverage performance, interval mean, and interval standard deviation. Simulation results indicate that the bootstrap confidence intervals for assessing the uncertainty of emission estimates are highly applicable with small sample size and the data distribution is non-normal. Compared with the classical confidence interval, bootstrap confidence intervals have smaller interval mean and smaller interval standard deviation for small sample size under non-normal distributions. This study recommends BCa confidence interval to assess the uncertainty of the emission estimates as long as sample size is 15 or more and the distribution is non-normal. A case study with emission data of the High-Bleed Pneumatic controllers demonstrates the effectiveness of the proposed procedure