Query: incremental statistical analysis

Candidate papers:

abstract: As the semiconductor technology node scales into the deep submicrometer regime, it has become very difficult to obtain high IC yields because the process voltage temperature variations induce large spreads in delay and power. In this paper, we propose a new framework, called GenFin, which is, as far as we know, the first to target the multiobjective yield optimization of logic circuits. Since FinFETs are a promising substitute for CMOS at 22 nm technology node and beyond, we evaluate the framework with a 22 nm FinFET logic library. By combining the power of genetic algorithm (GA) and adaptive multiobjective optimization, GenFin produces a set of nondominated logic circuits whose timing, leakage power, and dynamic power yields are simultaneously optimized. This can help designers make tradeoff decisions wisely and avoid suboptimal solutions. We also propose an incremental statistical circuit analyzer, called incremental FinPrin, that speeds up the statistical static timing analysis by up to 9.6x and the statistical power analysis by up to 2235.7x, while incurring errors of only up to 0.031% in mean and 0.74% in standard deviation relative to nonincremental analysis. We use heuristics based on the deterministic timing analysis and gate criticality to reduce the GA search space and also improve the quality of its solutions. We present extensive experimental results to demonstrate the efficacy of GenFin.
author_names: Aoxiang Tang

Niraj Kumar Jha
title: GenFin: Genetic Algorithm Based Multiobjective Statistical Logic Circuit Optimization Using Incremental Statistical Analysis
venue: IEEE Transactions on Very Large Scale Integration (VLSI) Systems
year: 2016

abstract: Abstract To assess the economic benefits of battery reuse, the consistency and aging characteristics of a retired LiFePO4 battery pack are studied in this paper. The consistency of battery modules is analyzed from the perspective of the capacity and the internal resistance. Test results indicate that battery module parameter dispersion increases along with battery aging. However, battery modules with better capacity consistency doesn't ensure better resistance consistency. Then the aging characteristics of the battery pack are analyzed and the main results are as follow: (1) Weibull and normal distribution are feasible to fit the capacity and resistance distribution of battery modules respectively; (2) SOC imbalance is the dominating factor in the capacity fading process of the battery pack; (3) By employing the incremental capacity (IC) and IC peak area analysis, a consistency evaluation method representing the aging mechanism variations of the battery modules is proposed and then an accurate battery screening strategy is put forward. This study not only provides data support for evaluating economic benefits of retired batteries but also presents a method to recognize the battery aging variations, which is helpful for rapid evaluation and screening of retired batteries for 2nd use.
author_names: Yan Jiang

Jiuchun Jiang

Caiping Zhang

Weige Zhang

Yang Jun Gao

Guo Qipei
title: Recognition of battery aging variations for LiFePO 4 batteries in 2nd use applications combining incremental capacity analysis and statistical approaches
year: 2017

abstract: Traditionally, software development processes have relied on the use of the "Waterfall" and "Vee" models. Later, Agile methodologies were used to handle the challenges of managing complex projects during the development phase. Agile methodologies are a group of incremental and iterative methods that are more effective, and have been used in project management. Kanban and Scrum are two powerful Agile project management approaches in software development. The objective of Scrum and Kanban is achieved by optimizing the development process by identifying the tasks, managing time more effectively, and setting up teams. A review of the literature reveals that there is a lack of statistical evidence to conclude which methodology is more effective in dealing with the traditional project management factors of budget handling, risk control, quality of the project, available resources, having clear project scope, and schedule handling. This research statistically compares the effectiveness of the Scrum and Kanban methods in terms of their effects on the project management factors for software development projects. Numerical analysis is performed based on survey responses from those with experience in the Scrum and Kanban methods. Results suggest that both Scrum and Kanban lead to the development of successful projects, and that the Kanban method can be better than the Scrum method in terms of managing project schedule. Statistical analysis of Scrum vs. Kanban according to project management factors.Data obtained from surveys of people involved in software development.Analysis suggests that Kanban is slightly better than Scrum for the Schedule factor.
author_names: Howard Lei

Farnaz Ganjeizadeh

P Jayachandran

Pinar Ozcan
title: A statistical analysis of the effects of Scrum and Kanban on software development projects
year: 2017

abstract: Statistical measures provide essential and valuable information about data and are needed for any kind of data analysis. Statistical measures can be used in a purely exploratory context to describe properties of the data, but also as estimators for model parameters or in the context of hypothesis testing. For example, the mean value is a measure for location, but also an estimator for the expected value of a probability distribution from which the data are sampled. Statistical moments of higher order than the mean provide information about the variance, the skewness, and the kurtosis of a probability distribution. The Pearson correlation coefficient is a measure for linear dependency between two variables. In robust statistics, quantiles play an important role, since they are less sensitive to outliers. The median is an alternative measure of location, the interquartile range an alternative measure of dispersion. The application of statistical measures to data streams requires online calculation. Since data come in step by step, incremental calculations are needed to avoid to start the computation process each time new data arrive and to save memory so that not the whole data set needs to be kept in the memory. Statistical measures like the mean, the variance, moments in general, and the Pearson correlation coefficient render themselves easily to incremental computations, whereas recursive or incremental algorithms for quantiles are not as simple or obvious. Nonstationarity is another important aspect of data streams that needs to be taken into account. This means that the parameters of the underlying sampling distribution might change over time. Change detection and online adaptation of statistical estimators is required for nonstationary data streams. Hypothesis tests like the kh2 or the t test can be a basis for change detection, since they can also be calculated in an incremental fashion. Based on change detection strategies, one can derive information on the sampling strategy, for instance the optimal size of a time window for parameter estimations of nonstationary data streams.
author_names: Katharina Tschumitschew

Frank Klawonn
title: Incremental Statistical Measures
year: 2012

abstract: Modeling and accuracy difficulties exist with traditional SSTA analysis and optimization methods. In this paper we describe methods to improve the efficiency of Monte Carlo based statistical static timing analysis. We propose a Stratification Hybrid Quasi Monte Carlo (SH QMC) approach to reduce the number of samples required for Monte Carlo based SSTA. Our simulations on benchmark circuits up to 90 K gates show that the proposed method requires 23.8X fewer samples on average to achieve comparable accuracy in timing estimation as a random sampling approach. Results on benchmark circuits also show that when SH QMC is performed with multiple parallel threads on a quad core processor, the approach is faster than traditional SSTA with comparable accuracy. SH QMC scales better than traditional SSTA with circuit size. We also propose an incremental approach to recompute a percentile delay metric after ECO. The results show that on average only 1.4% and 0.7% of original samples need to be evaluated for exact recomputation of the 95 percentile and 99 percentile delays, after sample size reduction using SH QMC.
author_names: Vineeth Veetil

Dennis Sylvester

David Blaauw
title: Efficient Monte Carlo based incremental statistical timing analysis
venue: 2008 45th ACM/IEEE Design Automation Conference
year: 2008

abstract: Incremental analysis is indispensible for efficient circuit optimization, as it analyzes the effect by the modified circuit part only instead of analyzing a whole circuit again from beginning. This paper presents a new incremental statistical static timing analysis (SSTA) method, called timing yield based incremental analysis (TYIA) TYIA uses the probability that the gate timing slack is non negative to prune the timing change propagation after a gate replacement. In the experimental results using ISCAS 85 benchmarks, TYIA showed 2~5 times better accuracy in timing yield analysis at comparable efficiency, when compared to the existing incremental SSTA methods.
author_names: Jin Wook Kim

Wook Kim

Hyoun Soo Park

Young Hwan Kim
title: Incremental statistical static timing analysis with gate timing yield emphasis
venue: APCCAS 2008 2008 IEEE Asia Pacific Conference on Circuits and Systems
year: 2008

abstract: Abstract This work analyzes a database of 31 existing CO2 enhanced oil recovery (EOR) projects that was compiled for the estimation of oil reserves to better understand the CO2 retention, incremental oil recovery, and net CO2 utilization for these oil fields. The measured data begin at the start date of the CO2 flood and extend through the year 2007. Cumulative CO2 retention (in the formation) incremental oil recovery factors, and net CO2 utilization factors were calculated for each of the sites. To express all site data on a common dimensionless scale, the data were extrapolated to 300% cumulative hydrocarbon pore volume (HCPV) by fitting nonlinear functions. Summary statistics were then calculated from 0% to 300% HCPV. Across all 31 sites, the 10th, 50th (median) and 90th percentile values for the three factors at 300% HCPV were: CO2 retention: 23.1% 48.3% and 61.8% retained; incremental oil recovery: 5.3% 12.2% and 21.5% of OOIP (original oil in place) and net CO2 utilization: 4.8, 8.7, and 10.5 Mscf/STB (stock tank barrel) This work employs a novel approach that incorporates nonlinear functions to quantify uncertainty in the estimated values as a function of HCPV and to describe the shape of the CO2 retention or incremental oil recovery response with a handful of parameters, providing insight into the behavior of the reservoir across the entire timeline of the CO2 flood. These nonlinear curve fits are focused on statistical inference i.e. what is the likely outcome and uncertainty ranges for CO2 retention, incremental oil recovery, and net CO2 utilization given the historical data from the 31 CO2 EOR sites? However, the approach described in this work also provides useful information for prediction i.e. given a set of inputs from another site with similar geology, what are plausible ranges in outcomes for each of these factors? Consequently, the results of this work can be used to estimate the potential range of expected performance for similar candidate oil fields that are not currently undergoing CO2 injection, including estimates of the associated CO2 storage potential of these candidate fields. The results of this work allow estimation of CO2 storage capacity in CO2 EOR operations with various degrees of confidence. The sites in the dataset reflect water alternating gas CO2 floods all within the continental United States and heavily dominated by the West Texas carbonate floods. Other floods outside of this region, where the data were available, are also included in this study (i.e. the Rocky Mountain region and the State of Oklahoma)
author_names: Nicholas A Azzolina

David V Nakles

Charles D Gorecki

Wesley D Peck

Scott C Ayash

L Stephen Melzer

Sumon Chatterjee
title: CO 2 storage associated with CO 2 enhanced oil recovery: A statistical analysis of historical operations
year: 2015

author_names: Rafael Geraldeli Rossi

Ricardo Marcondes Marcacini

Solange Oliveira Rezende
title: Analysis of Domain Independent Statistical Keyword Extraction Methods for Incremental Clustering
year: 2014

abstract: We propose the superiorization of incremental algorithms for tomographic image reconstruction. The resulting methods follow a better path in its way to finding the optimal solution for the maximum likelihood problem in the sense that they are closer to the Pareto optimal curve than the non superiorized techniques. A new scaled gradient iteration is proposed and three superiorization schemes are evaluated. Theoretical analysis of the methods as well as computational experiments with both synthetic and real data are provided.
author_names: Elias Salomao Helou

Marcelo Victor Wust Zibetti

Eduardo X Miqueles
title: Superiorization of incremental optimization algorithms for statistical tomographic image reconstruction
year: 2016

abstract: Article history: Ultrasonic vibration assisted single point incremental forming (UVaSPIF) is based on localized plastic deformation in a sheet metal blank. It consists of deforming gradually and locally the sheet metal using vibrating hemispherical head tool controlled by a CNC milling machine. The ultrasonic excitation of the forming tool reduces the vertical component of the forming force. In addition, application of ultrasonic vibration reduces the surface roughness of the specimen. Surface roughness is one of the quantitative and qualitative parameters, which is used to assess the quality of the final product. In the present paper, a statistical analysis and optimization of the effective factors on this parameter are performed in the UVaSPIF process. For this purpose, response surface methodology (RSM) is selected as the experiment design technique. The controllable factors such as vertical step size, sheet thickness, tool diameter, wall inclination angle, and feed rate are specified as input variables of the process. The obtained results from the analysis of variance (ANOVA) and regression analysis of the experimental data confirm the accuracy of the mathematical model. Furthermore, it is shown that the linear, quadratic, and interactional terms of the variables are effective on the surface roughness parameter. To optimize the surface roughness parameter, the most appropriate conditions of the experiment are determined using desirability method, and statistical optimization is subsequently verified by conducting the confirmation test.
author_names: Mehdi Vahdati

Ramezanali Mahdavinejad

Saeid Amini

Mahmoud Moradi
title: Statistical Analysis and Optimization of Factors Affecting the Surface Roughness in the UVaSPIF Process Using Response Surface Methodology
year: 2015