The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform
Sarah M. Ryan - One of the best experts on this subject based on the ideXlab platform.
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Statistical Reliability of wind power scenarios and stochastic unit commitment cost
Energy Systems, 2018Co-Authors: Didem Sari, Sarah M. RyanAbstract:Probabilistic wind power scenarios constitute a crucial input for stochastic day-ahead unit commitment in power systems with deep penetration of wind generation. To minimize the cost of implemented solutions, the scenario time series of wind power amounts available should accurately represent the stochastic process for available wind power as it is estimated on the day ahead. The high computational demands of stochastic programming motivate a search for ways to evaluate scenarios without extensively simulating the stochastic unit commitment procedure. The Statistical Reliability of wind power scenario sets can be assessed by approaches extended from ensemble forecast verification. We examine the relationship between the Statistical Reliability metrics and the results of stochastic unit commitment when implemented solutions encounter the observed available wind power. Lack of uniformity in a mass transportation distance rank histogram can eliminate scenario sets that might lead to either excessive no-load costs of committed units or high penalty costs for violating energy balance when the committed units are dispatched. Event-based metrics can help to predict results of implementing solutions found with the remaining scenario sets.
Yanjie Mao - One of the best experts on this subject based on the ideXlab platform.
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A Statistical Reliability Model for Single-Electron Threshold Logic
IEEE Transactions on Electron Devices, 2008Co-Authors: Chunhong Chen, Yanjie MaoAbstract:As one of the most promising candidates for future digital circuit applications, single-electron tunneling (SET) technology has been used to ensure further feature size reduction and ultralow power dissipation. However, this technology raises very serious concerns about reliable functioning, particularly due to random background charges and tight fabrication tolerances. Accurate evaluation of Reliability for SET circuits has thus become a crucial step toward their Reliability analysis and improvement. This brief proposes a Statistical Reliability model for SET logic gates, which takes into account the actual process variations and input probabilities. In particular, we study two typical SET logic gates (two-input nor and nand gates) for gate Reliability evaluation. Instead of assuming a constant failure rate for logic gates as in most previous work, we show how logic inputs affect the Reliability of the individual gates with discussions on the overall Reliability of the system consisting of logic gates. This model can be used in future computer-aided design tools to estimate tunneling events, energy consumption, and Reliability of SET-based digital logic circuits.
Didem Sari - One of the best experts on this subject based on the ideXlab platform.
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Statistical Reliability of wind power scenarios and stochastic unit commitment cost
Energy Systems, 2018Co-Authors: Didem Sari, Sarah M. RyanAbstract:Probabilistic wind power scenarios constitute a crucial input for stochastic day-ahead unit commitment in power systems with deep penetration of wind generation. To minimize the cost of implemented solutions, the scenario time series of wind power amounts available should accurately represent the stochastic process for available wind power as it is estimated on the day ahead. The high computational demands of stochastic programming motivate a search for ways to evaluate scenarios without extensively simulating the stochastic unit commitment procedure. The Statistical Reliability of wind power scenario sets can be assessed by approaches extended from ensemble forecast verification. We examine the relationship between the Statistical Reliability metrics and the results of stochastic unit commitment when implemented solutions encounter the observed available wind power. Lack of uniformity in a mass transportation distance rank histogram can eliminate scenario sets that might lead to either excessive no-load costs of committed units or high penalty costs for violating energy balance when the committed units are dispatched. Event-based metrics can help to predict results of implementing solutions found with the remaining scenario sets.
Xuan Zeng - One of the best experts on this subject based on the ideXlab platform.
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Statistical Reliability analysis under process variation and aging effects
Design Automation Conference, 2009Co-Authors: Li Shang, Hai Zhou, Hengliang Zhu, Fan Yang, Xuan ZengAbstract:Circuit Reliability is affected by various fabrication-time and run-time effects. Fabrication-induced process variation has significant impact on circuit performance and Reliability. Various aging effects, such as negative bias temperature instability, cause continuous performance and Reliability degradation during circuit run-time usage. In this work, we present a Statistical analysis framework that characterizes the lifetime Reliability of nanometer-scale integrated circuits by jointly considering the impact of fabrication-induced process variation and run-time aging effects. More specifically, our work focuses on characterizing circuit threshold voltage lifetime variation and its impact on circuit timing due to process variation and the negative bias temperature instability effect, a primary aging effect in nanometer-scale integrated circuits. The proposed work is capable of characterizing the overall circuit lifetime Reliability, as well as efficiently quantifying the vulnerabilities of individual circuit elements. This analysis framework has been carefully validated and integrated into an iterative design flow for circuit lifetime Reliability analysis and optimization.
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DAC - Statistical Reliability analysis under process variation and aging effects
Proceedings of the 46th Annual Design Automation Conference on ZZZ - DAC '09, 2009Co-Authors: Li Shang, Hai Zhou, Hengliang Zhu, Fan Yang, Xuan ZengAbstract:Circuit Reliability is affected by various fabrication-time and run-time effects. Fabrication-induced process variation has significant impact on circuit performance and Reliability. Various aging effects, such as negative bias temperature instability, cause continuous performance and Reliability degradation during circuit run-time usage. In this work, we present a Statistical analysis framework that characterizes the lifetime Reliability of nanometer-scale integrated circuits by jointly considering the impact of fabrication-induced process variation and run-time aging effects. More specifically, our work focuses on characterizing circuit threshold voltage lifetime variation and its impact on circuit timing due to process variation and the negative bias temperature instability effect, a primary aging effect in nanometer-scale integrated circuits. The proposed work is capable of characterizing the overall circuit lifetime Reliability, as well as efficiently quantifying the vulnerabilities of individual circuit elements. This analysis framework has been carefully validated and integrated into an iterative design flow for circuit lifetime Reliability analysis and optimization.
Yutaka Akiyama - One of the best experts on this subject based on the ideXlab platform.
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Assessing Statistical Reliability of phylogenetic trees via a speedy double bootstrap method.
Molecular phylogenetics and evolution, 2013Co-Authors: Aizhen Ren, Takashi Ishida, Yutaka AkiyamaAbstract:Evaluating the Reliability of estimated phylogenetic trees is of critical importance in the field of molecular phylogenetics, and for other endeavors that depend on accurate phylogenetic reconstruction. The bootstrap method is a well-known computational approach to phylogenetic tree assessment, and more generally for assessing the Reliability of Statistical models. However, it is known to be biased under certain circumstances, calling into question the accuracy of the method. Several advanced bootstrap methods have been developed to achieve higher accuracy, one of which is the double bootstrap approach, but the computational burden of this method has precluded its application to practical problems of phylogenetic tree selection. We address this issue by proposing a simple method called the speedy double bootstrap, which circumvents the second-tier resampling step in the regular double bootstrap approach. We also develop an implementation of the regular double bootstrap for comparison with our speedy method. The speedy double bootstrap suffers no significant loss of accuracy compared with the regular double bootstrap, while performing calculations significantly more rapidly (at minimum around 371 times faster, based on analysis of mammalian mitochondrial amino acid sequences and 12S and 16S rRNA genes). Our method thus enables, for the first time, the practical application of the double bootstrap technique in the context of molecular phylogenetics. The approach can also be used more generally for model selection problems wherever the maximum likelihood criterion is used.
