Non-linear regression analysis and analysis of variance of periods defined by irregular observations
Nonlinear regression and variance analyses of periods defined by irregular observations
VARIANCE↗
SEARCH · Engineering Papers
Results for “regression”
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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Unlimited simultaneous discrimination intervals in regression Technical report no. 90
Unlimited simultaneous discrimination intervals in linear regressions
REGRESSION ANALYSIS↗
TEMS radar error model regression analysis results from the Saturn AS-201, AS-202, and SA-203 flight tests
Tracking system error model regression analysis results from Apollo-Saturn tests
REGRESSION ANALYSIS↗
Multiple Instance Regression with Structured Data
This slide presentation reviews the use of multiple instance regression with structured data from multiple and related data sets. It applies the concept to a practical problem, that of estimating crop yield using remote sensed country wide weekly observations.
multiple instance regression↗
Prediction of Lunar Reconnaissance Orbiter Reaction Wheel Assembly Angular Momentum Using Regression Analysis
This study determines the feasibility of creating a tool that can accurately predict Lunar Reconnaissance Orbiter (LRO) reaction wheel assembly (RWA) angular momentum, weeks or even months into the future. LRO is a three-axis stabilized spacecraft that was launched on June 18, 2009. While typically nadir-pointing, LRO conducts many types of slews to enable novel science collection. Momentum unloads have historically been performed approximately once every two weeks with the goal of maintaining system total angular momentum below 70 Nms; however flight experience shows the models developed before launch are overly conservative, with many momentum unloads being performed before system angular momentum surpasses 50 Nms. A more accurate model of RWA angular momentum growth would improve momentum unload scheduling and decrease the frequency of these unloads. Since some LRO instruments must be deactivated during momentum unloads and in the case of one instrument, decontaminated for 24 hours there after a decrease in the frequency of unloads increases science collection. This study develops a new model to predict LRO RWA angular momentum. Regression analysis of data from October 2014 to October 2015 was used to develop relationships between solar beta angle, slew specifications, and RWA angular momentum growth. The resulting model predicts RWA angular momentum using input solar beta angle and mission schedule data. This model was used to predict RWA angular momentum from October 2013 to October 2014. Predictions agree well with telemetry; of the 23 momentum unloads performed from October 2013 to October 2014, the mean and median magnitude of the RWA total angular momentum prediction error at the time of the momentum unloads were 3.7 and 2.7 Nms, respectively. The magnitude of the largest RWA total angular momentum prediction error was 10.6 Nms. Development of a tool that uses the models presented herein is currently underway.
LRO↗
Bayesian Model Selection for Reducing Bloat and Overfitting in Genetic Programming for Symbolic Regression
When performing symbolic regression using genetic programming, overfitting and bloat can negatively impact generalizability and interpretability of the resulting equations as well as increase computation times. A Bayesian fitness metric is introduced and its impact on bloat and overfitting during population evolution is studied and compared to common alternatives in the literature. The proposed approach was found to be more robust to noise and data sparsity in numerical experiments, guiding evolution to a level of complexity appropriate to the dataset. Further evolution of the population resulted not in overfitting or bloat, but rather in slight simplifications in model form. The ability to identify an equation of complexity appropriate to the scale of noise in the training data was also demonstrated. In general, the Bayesian model selection algorithm was shown to be an effective means of regularization which resulted in less bloat and overfitting when any amount of noise was present in the training data.
Uncertainty quantification↗
Simulation and Regression Modeling of Nasa'S X-59 Low-Boom Carpets Across America
NASA’s X-59 aircraft is predicted to produce a significantly quieter cruise sonic boom than traditional N-wave-producing aircraft. A propagation simulation study was undertaken to quantify loudness levels, exposure size, and variability of the X-59’s low-boom carpet using realistic atmospheric profiles across the contiguous United States of America (CONUS). Near-field pressure data of the X-59 in supersonic cruise from NASA’s fully unstructured Navier–Stokes three-dimensional (known as FUN3D) computational fluid dynamics code were propagated using NASA’s PCBoom code, which solves an enhanced Burgers equation along acoustic rays. Atmospheric profiles from the National Oceanic and Atmospheric Administration’s Climate Forecast System Version 2 database were used for propagation at 138 locations across the CONUS. Carpets at each location were generated for aircraft headings in the four cardinal directions. Over one million X-59 carpets were generated in total. The effects of the heading, season, geography, and climate zone on boom levels and exposure size are presented. Multiple linear regression models were developed to estimate carpet width and loudness metrics across the CONUS. These results inform regulators and mission planners on expected variations in boom levels and carpet extent from atmospheric variations. Understanding potential carpet variability is important when planning community noise surveys using the X-59.
X-59↗
Simulation and Regression Modeling of X-59 Low-Boom Carpets Across America
The NASA X-59 aircraft is predicted to produce a significantly quieter cruise sonic boom than traditional N-wave-producing aircraft. A propagation simulation study was undertaken to quantify loudness levels, exposure size, and variability of the X-59 low-boom carpet using realistic atmospheric profiles across the contiguous United States of America (CONUS). Near-field pressure data of the X-59 in supersonic cruise from NASA’s fully unstructured Navier–Stokes three-dimensional (known as FUN3D) computational fluid dynamics code were propagated using NASA’s PCBoom code, which solves an enhanced Burgers equation along acoustic rays. Atmospheric profiles from the National Oceanic and Atmospheric Administration’s Climate Forecast System Version 2 database were used for propagation at 138 locations across the CONUS. Carpets at each location were generated for aircraft headings in the four cardinal directions. Over one million X-59 carpets were generated in total. The effects of the heading, season, geography, and climate zone on boom levels and exposure size are presented. Multiple linear regression models were developed to estimate carpet width and loudness metrics across the CONUS. These results inform regulators and mission planners on expected variations in boom levels and carpet extent from atmospheric variations. Understanding potential carpet variability is important when planning community noise surveys using the X-59.
X-59↗
Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning
Nuclear reactors and related systems are becoming increasingly complex due to advancing technologies in next-generation power reactors. This increased complexity necessitates enhanced automation and data management capabilities. To successfully realize autonomous systems, methods must be developed to handle vast volumes of data and effectively distinguish anomalous data from noise and expected data. While impressive models utilizing digital twins and similar approaches are under development, here we propose a simplified model for analyzing fundamental methods and techniques. Initially, we created a general dataset by using initial data from PCTRAN in order to represent ideal steady-state conditions. We then inserted anomalies based on prevalent sensor anomaly types (e.g., point anomalies, linear drift, and downward deviations), along with unusual anomalies such as exponential drift and upward deviations. To detect anomalies, we developed a program that employs data partitioning and linear regression to preprocess and filter the anomalous data. A K-Means machine learning (ML) method was then applied to separate and count the data within the anomalous partition. The results from all datasets—apart from exponential growth—demonstrated positive outcomes, with each returning multiple instances of greaterthan-95% accuracy. We conducted further investigations using Idaho National Laboratory’s RAVEN software to perform a sensitivity analysis on the input variables (R 2 Tolerance, Slope Tolerance, and Window Size) and found that the output variables (Accuracy and Time) were most sensitive to the Window Size. Despite the promising results published, further development is required to effectively apply these methods to nuclear systems. Nevertheless, the strengths of this approach are evident and hold promise for future applications in the field.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Evaluating Environmental Remediation Performance at Radwaste Sites Using Multiple, Censored Regression Analysis - 19348
Located on the U.S. Department of Energy's Hanford Site in Richland,Washington, the 300 Area's remediation performance monitoring requires assessment of concentration trends and calculation of means and lower and upper confidence limits on the mean. The performance monitoring wells for uranium are located within 400 m of the Columbia River and groundwater elevations and uranium concentrations are influenced by groundwater-surface water interactions. A multiple regression approach was used to assess trends because multiple covariates can be used to account for the observed fluctuations in groundwater elevation and concentration. Incorporation of covariates accounting for these groundwater-surface water interactions (i.e., river stage) reduces the random error in the trend estimation and improves the estimates of remediation performance metrics (i.e., mean and confidence limits on the mean) over traditional, non-parametric methods. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Development of a transient signal validation technique via a modified kernel regression model - 365
The demand for robust and resilient performance has led to the use of online-monitoring and signal validation techniques to monitor the process parameters and equipment conditions using several empirical models. One of these models is kernel regression (KR). Although KR is being used and performed excellently when applied to steady state, it has limitation in time-varying data that has several repetition of the same data point of the signals, especially if those signals are used to infer the other signals. In addition, many situations are related to variation and fluctuation during normal states, such as transient - start-up and shutdown mode. However, accurate estimation of the process signal can lead to the proper understanding of the equipment behaviours as well as enhance the online monitoring applications. In the sight of foregoing, this paper proposed a modified KR model, especially for transient signal validation to resolve the setback of conventional KR by improving the measure of similarities in the conventional KR. The performance of the proposed model is verified using both experimental data and the plant simulation data, and its results are compared with that of conventional KR. The results show that the proposed model has a greater robustness in particular situations compared to conventional KR, and it can be used to improve process and equipment monitoring applications. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Refining T c Prediction in Hydrides via Symbolic‐Regression‐Enhanced Electron‐Localization‐Function‐Based Descriptors
Hydrogen‐based materials are able to possess extremely high superconducting critical temperatures, T c s , due to hydrogen's low atomic mass and strong electron–phonon interaction. Recently, a descriptor based on the Electron Localization Function (ELF) has enabled the rapid estimation of the T c of hydrogen‐containing compounds from electronic networking properties, but its applicability has been limited by the small size and homogeneity of the training dataset used. Herein, the model is re‐examined, compiling a publicly available combined dataset of 244 binary and ternary hydride superconductors. The analysis shows that though ELF‐based networking remains a valuable descriptor, its predictive power declines with increasing compositional complexity. However, by introducing the molecularity index, defined as the highest value of the ELF at which two hydrogen atoms connect, and applying symbolic regression, the accuracy of the predictions can be substantially enhanced. These results establish a more robust framework for assessing superconductivity in hydride materials, facilitating accelerated screening of novel candidates through integration with crystal structure prediction methods or high‐throughput searches.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Bayesian Design of Experiments for Logistic Regression to Evaluate Multiple Forensic Algorithms
When evaluating the performance of several forensic classification algorithms, it is desirable to construct a design that considers a variety of performance levels for each of the algorithms. We describe a strategy to use Bayesian design of experiments with multiple prior estimates to capture anticipated performance. Our goal is to characterize results from the different classification algorithms as a function of multiple explanatory variables and use this to choose a design about which units to test. Bayesian design of experiments has been successful for generalized linear models, including logistic regression models. Here, we develop methodology for the case where there are several potentially nonoverlapping priors for anticipated performance under consideration. The weighted priors method performs well for a broad range of true underlying model parameter choices and is more robust when compared to other candidate design choices. Additionally, we show how this can be applied in the multivariate input case and provide some useful summary measures. The shared information plot is used to evaluate design point allocation, and the D-value difference plot allows for the comparison of design performance across multiple potential parameter values in higher dimensions. Here, we illustrate the methods with several examples.
97 MATHEMATICS AND COMPUTING↗
Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials
Herein, this article presents generalized semiparametric regression models for conditional cumulative incidence functions with competing risks data when covariates are missing by sampling design or happenstance. A doubly robust augmented inverse probability weighted (AIPW) complete-case approach to estimation and inference is investigated. This approach modifies IPW complete-case estimating equations by exploiting the key features in the relationship between the missing covariates and the phase-one data to improve efficiency. An iterative numerical procedure is derived to solve the nonlinear estimating equations. The asymptotic properties of the proposed estimators are established. A simulation study examining the finite-sample performances of the proposed estimators shows that the AIPW estimators are more efficient than the IPW estimators. The developed method is applied to the RV144 HIV-1 vaccine efficacy trial to investigate vaccine-induced IgG binding antibodies to HIV-1 as correlates of acquisition of HIV-1 infection while taking account of whether the HIV-1 sequences are near or far from the HIV-1 sequences represented in the vaccine construct.
97 MATHEMATICS AND COMPUTING↗
Predicting fatigue from heart rate signatures using functional logistic regression
Physical fatigue can have adverse effects on humans in extreme environments. Therefore, being able to predict fatigue using easy to measure metrics such as heart rate (HR) signatures has potential to have an impact in real-life scenarios. We apply a functional logistic regression model that uses HR signatures to predict physical fatigue, where physical fatigue is defined in a data-driven manner. Data were collected using commercially available wearable devices on 47 participants hiking the 20.7-mile Grand Canyon rim-to-rim trail in a single day. Fitted model provides good predictions and interpretable parameters for real-life application.
60 APPLIED LIFE SCIENCES↗
Prediction of Subsidence of Buildings as a Result of Earthquakes by Gaussian Process Regression
Subsidence of a foundation caused by liquefaction of sandy soil is a critical step in the creation of a building. Seven measured indicators of soil liquefaction in the course of an earthquake were selected as key impacting indicators that predict subsidence of a building. These indicators are the earthquake intensity EI, length–height ratio L/H, average contact pressure p, width-depth ratio, relative density D{sub R}, thickness of the non-liquefied layer, and the depth of the gravel layer. Furthermore, as a probabilistic machine learning kernel and powerful tool for solving highly nonlinear problems, Gaussian process regression (GPR model) can meet execution time requirements and assure a high level of precision of subsidence prediction due to the intrinsic defects of theoretical analysis and numerical calculation. A total of 41 groups of typical cases were first selected as a training sample and 20 groups of typical cases were defined as a test sample, where these cases possess input test value and were obtained at the yield of the prediction accuracy from the GPR model. Next, the PLS method, MLR method and LSSVM method were selected to verify the validity and reliability of the GPR model. Finally, the simulation result shows that the GPR model can improve the prediction precision for the case of the above problems and is of great value for practical applications.
58 GEOSCIENCES↗
Ensemble-Based Support Vector Regression with Gravitational Search Algorithm Optimization for Estimating Magnetic Relative Cooling Power of Manganite Refrigerant in Magnetic Refrigeration Application
Magnetic refrigeration technology (MRT) is considered an energy-efficient and environmental-friendly system of refrigeration that has a considerable potential of replacing the classical gas-compression expansion method of refrigeration. Inclusion of manganite-based material (MBM) in MRT as a magnetic refrigerant has attracted significant attention recently due to cost effectiveness of the refrigerant as well as better resistance to oxidation and corrosion as compared to the commonly used metal gadolinium refrigerant. Relative cooling power (RCP) is one of the most important parameters to be considered while assessing the usefulness of MBM. Its value can be altered through doping with external materials and accurate estimation of the dopant influence is required to achieve the right amount of RCP. This present research work proposes support vector regression (SVR) ensemble models with gravitational search algorithm (GSA) hyper-parameters optimization, for estimating RCP of MBM and to determine the influence of dopants on RCP using ionic radii and dopant concentrations as descriptors. GSA-SVR ensemble model (GSE) is developed by employing the outputs of five different SVR models as descriptors while GSA-SVR ensemble model with averaging (GSEA) uses the average of the five different SVR models as its descriptor. The novel ensemble models outperform other SVR models, specifically; GSE performs better than GSA-SVR model and the conventional SVR model with performance improvement of 269.14% and 283.61%, respectively on the basis of root mean square error (RMSE). Furthermore, GSEA outperforms GSE, GSA-SVR model and conventional SVR with performance improvement of 27.51%, 370.70%, and 389.14%, respectively on the basis of RMSE. The developed GSE and GSEA also perform better than the existing RCP model in the literature with performance improvement of 11.53% and 42.21%, respectively. The results of this research work will not only serve to circumvent the experimental challenges of RCP measurement without loss of experimental precision but also further promotes environment-friendly system of refrigeration.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Combination therapy of tumor-targeting Salmonella typhimurium A1-R and oral recombinant methioninase regresses a BRAF-V600E-negative melanoma
Melanoma is a recalcitrant cancer. To improve and individualize treatment for this disease, we previously developed a patient-derived orthotopic xenograft (PDOX) model for melanoma. We previously reported the individual efficacy of tumor-targeting Salmonella typhimurium A1-R (S. typhimurium A1-R) and recombinant methioninase (rMETase) for melanoma in the PDOX models of this disease. In the present study, we evaluated the efficacy of the combination of S. typhimurium A1-R with orally-administered rMETase (o-rMETase) for BRAF-V600E-negative melanoma in a PDOX model. Three weeks after implantation, 60 PDOX mouse models were randomized into six groups of 10 mice each: untreated control, temozolomide (TEM); o-rMETase; S. typhimurium A1-R; TEM + rMETase, S. typhimurium A1-R + rMETase. All treatments inhibited tumor growth compared to untreated control (TEM: p S. typhimurium A1-R: p S. typhimurium A1-R + rMETase: p S. typhimurium A1-R + o-rMETase which regressed this melanoma PDOX, thereby indicating a new paradigm for treatment of metastatic melanoma.
60 APPLIED LIFE SCIENCES↗