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Results for “empirical analysis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Empirical Analysis and Automated Classification of Security Bug Reports

With the ever expanding amount of sensitive data being placed into computer systems, the need for effective cybersecurity is of utmost importance. However, there is a shortage of detailed empirical studies of security vulnerabilities from which cybersecurity metrics and best practices could be determined. This thesis has two main research goals: (1) to explore the distribution and characteristics of security vulnerabilities based on the information provided in bug tracking systems and (2) to develop data analytics approaches for automatic classification of bug reports as security or non-security related. This work is based on using three NASA datasets as case studies. The empirical analysis showed that the majority of software vulnerabilities belong only to a small number of types. Addressing these types of vulnerabilities will consequently lead to cost efficient improvement of software security. Since this analysis requires labeling of each bug report in the bug tracking system, we explored using machine learning to automate the classification of each bug report as a security or non-security related (two-class classification), as well as each security related bug report as specific security type (multiclass classification). In addition to using supervised machine learning algorithms, a novel unsupervised machine learning approach is proposed. An ac- curacy of 92%, recall of 96%, precision of 92%, probability of false alarm of 4%, F-Score of 81% and G-Score of 90% were the best results achieved during two-class classification. Furthermore, an accuracy of 80%, recall of 80%, precision of 94%, and F-score of 85% were the best results achieved during multiclass classification.

Cybersecurity↗
DOE OSTI2023

pvOps: a Python package for empirical analysis of photovoltaic field data

The purpose of pvOps is to support empirical evaluations of data collected in the field related to the operations and maintenance (O&M) of photovoltaic (PV) power plants. pvOps presently contains modules that address the diversity of field data, including text-based maintenance logs, current-voltage (IV) curves, and timeseries of production information. The package functions leverage machine learning, visualization, and other techniques to enable cleaning, processing, and fusion of these datasets. These capabilities are intended to facilitate easier evaluation of field patterns and extraction of relevant insights to support reliability-related decision-making for PV sites. The open-source code, examples, and instructions for installing the package through PyPI can be accessed through the GitHub repository.

14 SOLAR ENERGY↗
DOE OSTI2024

An empirical analysis of supply offers in the ERCOT operating reserves markets

Here, this paper seeks to improve theoretical and empirical understanding of supplier dynamics in wholesale markets for operating reserves, which have been understudied compared to energy markets. We begin by identifying several economic factors that unit owners may consider when submitting offers into operating reserves auctions in two-stage, co-optimized markets common across much of North America. Next, we analyze historical offer data from the Electric Reliability Council of Texas (ERCOT) market to assess whether actual reserve market behavior aligns with expectations based on economic theory, as well as with commonly used assumptions in electricity market modeling efforts. We find that the aggregate supply of operating reserves in ERCOT varies meaningfully over time, becoming more expensive during summer afternoons, which is consistent with theoretical expectations but contradicts the typical modeling assumption of temporally invariant reserve offers. Analysis of offers made by individual units uncovers additional insights, such as the existence of large offer pattern differences by unit owner and the tendency of battery storage units to submit very low offer prices. We conclude by discussing how our findings can be integrated into electricity market modeling assumptions to improve alignment with observed operating reserve offer inputs and pricing outcomes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2017

Security Vulnerability Profiles of Mission Critical Software: Empirical Analysis of Security Related Bug Reports

While some prior research work exists on characteristics of software faults (i.e., bugs) and failures, very little work has been published on analysis of software applications vulnerabilities. This paper aims to contribute towards filling that gap by presenting an empirical investigation of application vulnerabilities. The results are based on data extracted from issue tracking systems of two NASA missions. These data were organized in three datasets: Ground mission IVV issues, Flight mission IVV issues, and Flight mission Developers issues. In each dataset, we identified security related software bugs and classified them in specific vulnerability classes. Then, we created the security vulnerability profiles, i.e., determined where and when the security vulnerabilities were introduced and what were the dominating vulnerabilities classes. Our main findings include: (1) In IVV issues datasets the majority of vulnerabilities were code related and were introduced in the Implementation phase. (2) For all datasets, around 90 of the vulnerabilities were located in two to four subsystems. (3) Out of 21 primary classes, five dominated: Exception Management, Memory Access, Other, Risky Values, and Unused Entities. Together, they contributed from 80 to 90 of vulnerabilities in each dataset.

Goseva-Popstojanova, Katerina↗
DOE OSTI2026

Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Addressing the "Red-AI" trend of rising energy consumption by large-scale neural networks, this study investigates the measured energy consumption of training various fully connected neural network architectures. We introduce the BUTTER-E dataset, an augmentation to the BUTTER Empirical Deep Learning dataset, containing energy consumption and performance data from 41,129 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (trainable parameters), 8 "shapes", and 14 depths on both CPUs and GPUs using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use. Our analysis uncovers a surprising, hardware-mediated non-linear relationship between energy efficiency and network design, challenging the assumption that reducing the number of parameters or FLOPs is the best way to achieve greater energy efficiency. We propose a straightforward and effective energy model that accounts for network size, computing, and memory hierarchy. Highlighting the need for cache-considerate algorithm development, we suggest a codesign approach to energy efficient network, algorithm, and hardware design. This work contributes to the fields of sustainable computing and Green AI, offering practical guidance for creating more energy-efficient neural networks and promoting sustainable AI.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1974

An empirical analysis of the quantitative effect of data when fitting quadratic and cubic polynomials

A study is made of the extent to which the size of the sample affects the accuracy of a quadratic or a cubic polynomial approximation of an experimentally observed quantity, and the trend with regard to improvement in the accuracy of the approximation as a function of sample size is established. The task is made possible through a simulated analysis carried out by the Monte Carlo method in which data are simulated by using several transcendental or algebraic functions as models. Contaminated data of varying amounts are fitted to either quadratic or cubic polynomials, and the behavior of the mean-squared error of the residual variance is determined as a function of sample size. Results indicate that the effect of the size of the sample is significant only for relatively small sizes and diminishes drastically for moderate and large amounts of experimental data.

Canavos, G. C.↗
NASA NTRS1973

An empirical analysis of the distribution of overshoots in a stationary Gaussian stochastic process

The frequency distribution of overshoots in a stationary Gaussian stochastic process is analyzed. The primary processes involved in this analysis are computer simulation and statistical estimation. Computer simulation is used to simulate stationary Gaussian stochastic processes that have selected autocorrelation functions. An analysis of the simulation results reveals a frequency distribution for overshoots with a functional dependence on the mean and variance of the process. Statistical estimation is then used to estimate the mean and variance of a process. It is shown that for an autocorrelation function, the mean and the variance for the number of overshoots, a frequency distribution for overshoots can be estimated.

Carter, M. C.↗
NASA NTRS1974

An empirical analysis of the distribution of the duration of overshoots in a stationary gaussian stochastic process

This analysis utilizes computer simulation and statistical estimation. Realizations of stationary gaussian stochastic processes with selected autocorrelation functions are computer simulated. Analysis of the simulated data revealed that the mean and the variance of a process were functionally dependent upon the autocorrelation parameter and crossing level. Using predicted values for the mean and standard deviation, by the method of moments, the distribution parameters was estimated. Thus, given the autocorrelation parameter, crossing level, mean, and standard deviation of a process, the probability of exceeding the crossing level for a particular length of time was calculated.

Parrish, R. S.↗
NASA NTRS1981

X-ray and H-alpha observations of a filament-disappearance flare - An empirical analysis of the magnetic field configuration

On August, 29, 1973, a flare event occurred that involved the disappearance of a filament near central meridian. The event, well-observed in X-rays on Skylab and in H-alpha, was a four-ribbon flare involving both new and old magnetic inversion lines which were roughly parallel. The H-alpha, X-ray, and magnetic field data are used to deduce the magnetic polarities of the H-alpha brightening at the footpoints of the brightest X-ray loops. It is suggested that the event involved a reconnection of magnetic field lines rather than a brightening in place of preexisting loops. The simultaneity of the H-alpha brightening onsets in the four ribbons and the apparent lack of an eruption of the filament are consistent with this interpretation.

Kahler, S. W.↗
NASA NTRS2001

Empirical Analysis of EEG and ERPs for Psychophysiological Adaptive Task Allocation

The present study was designed to test the efficacy of using Electroencephalogram (EEG) and Event-Related Potentials (ERPs) for making task allocation decisions. Thirty-six participants were randomly assigned to an experimental, yoked, or control group condition. Under the experimental condition, a tracking task was switched between task modes based upon the participant's EEG. The results showed that the use of adaptive aiding improved performance and lowered subjective workload under negative feedback as predicted. Additionally, participants in the adaptive group had significantly lower RMSE and NASA-TLX ratings than participants in either the yoked or control group conditions. Furthermore, the amplitudes of the N1 and P3 ERP components were significantly larger under the experimental group condition than under either the yoked or control group conditions. These results are discussed in terms of the implications for adaptive automation design.

Prinzel, Lawrence J., III↗
NASA NTRS2003

The Empirical Analysis of Impact of Alliances on Airline Operations

Airline alliances are dominating the current air transport industry with the largest carriers of the world belonging to one of the four alliance groupings - "Wings", Star Alliance, one world, SkyTeam - which represent 56% of world Revenue Passenger Kilometers. Although much research has been carried out to evaluate the impact of alliance membership on performance of airlines, it would be of interest to ascertain the degree of impact perceived by participating airlines in alliances. It is the purpose of this paper to gather the opinion of all the airlines, belonging to the four global alliance groupings on the impact alliances have had on their traffic and on their performance in general To achieve this, a comprehensive survey of the alliance management departments of airlines participating in the four global strategic alliances was carried out. With this framework the survey has examined which type of cooperation among carriers (FFP, Code Share, Strategic Alliance without antitrust immunity, Strategic Alliance with antitrust immunity) has produced the most positive impact on traffic and which type of route (short haul, long haul, hub-hub, hub-non hub, non hub-non hub) has been mostly affected. In addition, the respondent airlines quantified the effect alliances have had on specific areas of their operation, such as load factors, traffic, costs, revenue and fares. Their responses have been analysed under each global alliances grouping, under airline and under geographic region to establish which group, type of carrier and geographic region has benefited most. The results show that each of the four global alliances groupings has experienced different results according to the type of collaboration agreed amongst their member airlines.

Iatrou, Kostas↗