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NASA NTRS1976

Interpretation key for SAR /L-band/ imagery of sea ice

An interpretation key, similar to those previously developed for use with aerial photography and other remotely sensed data, was developed for L-band (25 cm) radar imagery collected over the Arctic Ocean. Data from April, August, and October were considered. The procedure for developing a valid interpretation key for operation use involves substituting time for space. Open water situations (polynyas, leads, flaws), examples of unconsolidated ice (frazil, slush, brash), thin ice (nilas), and annual ice (first year, multi-year ice) situations are examined. It is suggested that the interpretation key will enhance the use of side looking airborne radar data in the qualitative photo interpretation mode.

Bryan, M. L.↗

NASA NTRS1981

Ground photography for improvved image interpretation training

Systematic sets of ground based color and color infrared photography were presented to students in remote sensing classes to enhance their ability to interpret satellite images. Features readily apparent on computer enhanced LANDSAT images were presented simultaneously with ground based photography in the format of slide triplicates. It was hypothesized that this instructional approach would improve the students' abilities to recognize, understand and interpret ground phenomena present on remotely sensed imagery. Tests conducted in undergraduate remote sensing classes substantially upheld this hypothesis. Student image interpretive abilities were tested before and after being exposed to the ground level photography. In general, image interpretive skills of the class improved by 25%. Also, the performance of students having different academic majors varied greatly for differing types of image subjects.

Lougeay, R.↗

NASA NTRS1987

A review of computer aided interpretation technology for the evaluation of radiographs of aluminum welds

Industrial radiography is a well established, reliable means of providing nondestructive structural integrity information. The majority of industrial radiographs are interpreted by trained human eyes using transmitted light and various visual aids. Hundreds of miles of radiographic information are evaluated, documented and archived annually. In many instances, there are serious considerations in terms of interpreter fatigue, subjectivity and limited archival space. Quite often it is difficult to quickly retrieve radiographic information for further analysis or investigation. Methods of improving the quality and efficiency of the radiographic process are being explored, developed and incorporated whenever feasible. High resolution cameras, digital image processing, and mass digital data storage offer interesting possibilities for improving the industrial radiographic process. A review is presented of computer aided radiographic interpretation technology in terms of how it could be used to enhance the radiographic interpretation process in evaluating radiographs of aluminum welds.

Lloyd, J. F., Sr.↗

NASA NTRS1989

Image understanding and interpretation in microwave diversity imaging

The authors investigate microwave imaging of metallic objects using a diversity method and interpret and predict the reconstructed image from an approach based on analysis of the scattering mechanism and a back-projection algorithm used in image retrieval. The connection between the various scattering mechanisms and the reconstructed images is discussed, what the images represent is interpreted, and a prediction is made as to what the image will look like over given spectral and angular windows. A brief description is given of the microwave diversity imaging system and the formulation of the microwave diversity imaging based on the physical optics approximation. The scattering mechanism of a complex shaped metallic object is then briefly reviewed and an alternate approach to interpreting the reconstructed image based on the understanding of the scattering mechanism and the reconstruction algorithm is given. Several numerical and experimental examples are included to support this interpretation approach.

Li, Hsueh-Jyh↗

NASA NTRS1994

Synoptic interpretation of measurements from HALOE

The degree to which the Southern Hemisphere polar vortex is isolated against horizontal (isentropic) mixing is investigated using data from the Halogen Occultation Experiment (HALOE), U.K. Meteorological Office (UKMO) potential vorticity (PV), and contour advection diagnostics. Measurements of methane and water vapor taken by HALOE during a disturbed period in the Southern Hemisphere springtime (21 September - 15 October 1992) are interpreted in light of the prevailing synoptic meteorology. Daily fields of winds and PV are shown to be essential in the interpretation of the data. A climatological high pressure region is responsible for a distorted vortex, and a substantial 'vortex stripping' event is present, associated with the early stages of vortex breakdown. This leads to significant temporal, zonal, and altitudinal variations in the distribution of tracers. The authors point out the difficulties this presents for the interpretation of solar occultation data, especially with regard to the use of zonal average time series. Longitude-height methane distributions from two days during the period are examined. Both days show substantial variations in abundance around a latitude circle. In particular, the authors investigate HALOE measurements at 77 deg S on 15 October 1992, which indicate an abundance of methane in the height region 600-2000 K (approximately 30-1 mb) that is more typical of midlatitude air. Similar distributions, observed in the 1991 HALOE data, have previously been interpreted as evidence for the penetration of midlatitude air into the vortex. Gradients of potential vorticity and contour advection diagnositcs are employed to examine whether the UKMO winds are consistent with this hypothesis in 1992. Although midlatitude air is able to penetrate poleward of the main jet core by advection processes alone, an essentially intact inner core of vortex air remains, which does not mix to any great extent with air from lower latitudes. The authors show that the high-latitude HALOE abundances that are typical of midlatitude air were observed in a region of extensive filamentation and mixing, rather than within the inner, more isolated, core.

Bithell, M.↗

NASA NTRS1991

On the Power of Abstract Interpretation

Increasingly sophisticated applications of static analysis place increased burden on the reliability of the analysis techniques. Often, the failure of the analysis technique to detect some information my mean that the time or space complexity of the generated code would be altered. Thus, it is important to precisely characterize the power of static analysis techniques. We follow the approach of Selur et. al. who studied the power of strictness analysis techniques. Their result can be summarized by saying 'strictness analysis is perfect up to variations in constants.' In other words, strictness analysis is as good as it could be, short of actually distinguishing between concrete values. We use this approach to characterize a broad class of analysis techniques based on abstract interpretation including, but not limited to, strictness analysis. For the first-order case, we consider abstract interpretations where the abstract domain for data values is totally ordered. This condition is satisfied by Mycroft's strictness analysis that of Sekar et. al. and Wadler's analysis of list-strictness. For such abstract interpretations, we show that the analysis is complete in the sense that, short of actually distinguishing between concrete values with the same abstraction, it gives the best possible information. We further generalize these results to typed lambda calculus with pairs and higher-order functions. Note that products and function spaces over totally ordered domains are not totally ordered. In fact, the notion of completeness used in the first-order case fails if product domains or function spaces are added. We formulate a weaker notion of completeness based on observability of values. Two values (including pairs and functions) are considered indistinguishable if their observable components are indistinguishable. We show that abstract interpretation of typed lambda calculus programs is complete up to this notion of indistinguishability. We use denotationally-oriented arguments instead of the detailed operational arguments used by Selur et. al.. Hence, our proofs are much simpler. They should be useful for further future improvements.

Reddy, Uday S.↗

NASA NTRS2004

Understanding AOP through the Study of Interpreters

I return to the question of what distinguishes AOP languages by considering how the interpreters of AOP languages differ from conventional interpreters. Key elements for static transformation are seen to be redefinition of the set and lookup operators in the interpretation of the language. This analysis also yields a definition of crosscutting in terms of interlacing of interpreter actions.

Filman, Robert E.↗

NASA NTRSDate not supplied

Interpretable Convolutional Learning Classifier System (C-LCS) for Higher Dimensional Datasets

The purpose of this paper is to devise an interpretable hybrid classification model for Convolutional Neural Networks (CNN) and a Learning Classifier System (LCS). The presented hybrid system integrates the fundamental attributes from both types of these classifiers. In the proposed hybrid model CNN works as an automatic feature extractor, and LCS works to provide interpretable rule-based classification results. Although LCS has limitations working on higher dimensional datasets, we resolve this limitation by using CNN as a feature extractor. The other concept of the non-interpretability of CNN is addressed by using the LCS rule. Furthermore, our experiment with higher dimensional datasets like CIFAR-10 and Fashion-MNIST shows that extended LCS provides comparable performance to the standard neural network model while also providing interpretable results. We named this extended LCS method Convolutional Learning Classifier Cystem (C-LCS).

Jelani Owens↗

DOE OSTI2025

AI for Interpreting Nuclear Power Plant Documents for Power Uprates

To reduce the cost and time needed for regulatory compliance, nuclear power plants (NPPs) can utilize artificial intelligence (AI) to assist in interpreting complex and voluminous documents that typically span thousands of pages. Usually, the process of interpreting a plant’s technical specifications (TSs) and associated documents is labor intensive. This study aims to understand what processes state-of-the-art large language models (LLMs) can automate and to identify the pitfalls associated with using LLMs to reduce human labor costs and time. This research uses a recent AI technology called retrieval augmented generation (RAG), which retrieves pages of information from TSs and associated documents to assist with NPP power uprates (cleared to produce more power). LLMs are integral to RAG because they create human-like responses based on the retrieved information, aiding in the interpretation and application processes. A baseline case demonstrates how LLMs can operate successfully for a power uprate application. Then five use cases show five types of potential failures: (1) RAG retrieving the incorrect information, (2) RAG misinterpreting the retrieved information, (3) RAG relying on knowledge not contained in the retrieved information, (4) RAG hallucinating, and (5) RAG refusing to answer. The results of the five use cases suggest that automating the human interpretation of TSs and associated documents with AI should be approached with caution. A subject-matter expert reviewed the AI outputs from the five use cases and concluded that an LLM can produce technical information that is needed to produce power uprate applications in certain instances.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2026

Interpretable Machine Learning for Characterizing Electric Vehicle Charging Behavior: Insights from Real-World Data

As electric vehicle (EV) adoption rises globally, concerns about the impact on aging electrical grids grow, particularly regarding the charging behavior of EV drivers. This study analyzes real-world driving and charging data from Ford battery electric vehicles (BEVs) collected between 2018 and 2019 to develop interpretable models that characterize charging behavior and quantify influencing factors. Prior research has relied on assumptions regarding driver behavior, often overlooking actual charging patterns. By employing generalized linear mixed models (GLMMs), this work offers insights into how various elements, such as next trip distance and state of charge (SOC), influence charging decisions. The dataset comprises over three million park-trip pairs from 1,997 vehicles, revealing that features related to driving behavior significantly dictate charging behavior, while infrastructure and regional factors have lesser impacts. The findings suggest that existing simulation models may oversimplify EV charging behavior assumptions. This work utilizes real-world EV driving and charging data to train interpretable models that describe charging behavior and quantify the factors most associated with how drivers use charging infrastructure. This research underscores the need for interpretable, data-driven methodologies to inform future EV infrastructure planning and grid management.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2024

Teaching Freight Mode Choice Models New Tricks Using Interpretable Machine Learning Methods

Understanding and forecasting the intricate freight mode choice behavior under various industry, policy, and technology contexts is essential in freight planning and policymaking. Numerous models have been developed in prior studies to provide insights into freight mode selection, the majority of which use discrete choice models such as multinomial logit (MNL) models. However, logit models often rely on linear specifications of independent variables, despite potential nonlinear relationships in the data. Moreover, there often lacks a heuristic and efficient approach to identify such complex relationships to define the logit model specifications. To fill this gap, we developed an MNL model for freight mode choice using the insights from state-of-the- art machine learning (ML) models. ML models can capture the nonlinear nature of the complex decision-making process, and recent advances in 'explainable AI' have greatly improved their interpretability. The interpretable ML methods help enhance the performance of MNL models and advance knowledge of freight mode choice. Specifically, the influential factors and their relationship with individual modes are identified using SHapley Additive exPlanations (SHAP) to improve the MNL's performance. The workflow is demonstrated in a case study of Austin, Texas, and the SHAP results reveal multiple nonlinear relationships predicted by ML models. Incorporating those relationships into MNL model specifications improves the interpretability and accuracy of the MNL model compared to a conventional MNL model. Findings from this study can be used to guide freight planning and inform policymakers and practitioners on how key factors affect freight decision-making.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

NASA NTRS2017

Interpretable Categorization of Heterogeneous Time Series Data

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

Drones↗

NASA NTRSDate not supplied

On the New Optical Constants Database (OCdb) and its Importance for the Interpretation of Observational Data

The Optical Constants database(ocdb.smce.nasa.gov) came online in February 2023 and provides complex refractive indices of laboratory-generated organic refractory materials and ices relevant to (exo) planetary and astrophysical environments.The goal of the OCdb is to centralize published optical constants data to facilitate both their access by the scientific community and their use to analyze observational data returned by space missions and ground-based observatories. Computational tools are also under development to facilitate scientific use of the available OCdb optical constants data sets. Investigators generating laboratory optical constants are therefore encouraged to contribute their data to OCdb in order to increase the availability of their data and to enhance the scientific effectiveness of the database. Optical constants are critical input parameters in models (e.g.,radiative transfer, atmospheric, and reflectance spectral models)that are used to simulate the absorption, reflection, and scattering of light due to solid materials present in planetary and astrophysical environments (planets, their satellites, exoplanets, asteroids, comets, protoplanetary disks, etc.), and are key to the compositional interpretation of observations. We will first present the infrastructure of the OCdb and show how to use and contribute to it. We will introduce the two large NASA projects, namely, the Laboratory Astrophysics Directed Work Package and the NASA Center for Optical Constants, that have been instrumental in (i) developing the OCdb, (ii) generating planetary-and astrophysics-relevant ices and organic refractory materials from gas and ice irradiation in the laboratory, and (iii) determining their optical constants for inclusion in OCdb. We will also present two studies that are making use of these optical constants to interpret observations of Titan’s atmosphere and Pluto’s surface. These studies show the importance of measuring optical constants of laboratory-generated materials, and their impact on the models used to analyze and interpret astronomical observations. These studies also demonstrate the essential importance of such a database and the need for optical constants of a broad range of materials and wavelengths to enable the scientific community and to maximize the scientific return from space missions (e.g., Cassini, New Horizons, SOFIA, JWST).

The Optical Constants database↗

DOE OSTI2026

L-VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts

Symptom modelling in head and neck cancer is challenged by the complexity of heterogeneous patient data, leading to an interest in deep learning approaches. Although Long Short-Term Memory Networks (LSTMs) have shown great results in patient risk prediction, their low interpretability requires data modellers to collaborate with clinical experts to validate the results. We present L-VISP, a human–machine solution that uses visual analytics for LSTM modelling in clinical research. L-VISP uses custom visual encodings to make multiple LSTM variants interpretable, supporting a full range of analysis, from understanding model operations and evaluating performance to interpreting results in a clinical context. We evaluate L-VISP with data modellers and a clinical oncologist and present the takeaways from this multidisciplinary collaboration.

LSTM modeling↗

NASA NTRS1971

Architectural design of an Algol interpreter

The design of a syntax-directed interpreter for a subset of Algol is described. It is a conceptual design with sufficient details and completeness but as much independence of implementation as possible. The design includes a detailed description of a scanner, an analyzer described in the Floyd-Evans productions, a hash-coded symbol table, and an executor. Interpretation of sample programs is also provided to show how the interpreter functions.

Jackson, C. K.↗

NASA NTRS1973

A technique for interpretation of multispectral remote sensor data

The author has identified the following significant results. The U.S. Army Engineer Waterways Experiment Station is engaged in a study to detect from ERTS-1 satellite data alterations to the absorption and scattering properties caused by movement of suspended particles and solutes in selected areas of the Chesapeake Bay and to correlate the data to determine the feasibility of delineating flow patterns, flushing action of the estuary, and sediment and pollutant dispersion. As a part of this study, ADP techniques have been developed that permit automatic interpretation of data from any multispectral remote sensor with computer systems which have limited memory capacity and computing speed. The multispectral remote sensor is considered as a reflectance spectrophotometer. The data which define the spectral reflectance characteristics of a scene are scanned pixel by pixel. Each pixel whose spectral reflectance matches a reference spectrum is identified, and the results are shown in a map that identifies the locations where spectrum matches were detected and spectrum that was matched. The interpretation technique is described and an example of interpreted data from ERTS-1 is presented.

Williamson, A. N.↗

NASA NTRS1973

Ground Operations Aerospace Language (GOAL). Volume 4: Interpretive code translator

This specification identifies and describes the principal functions and elements of the Interpretive Code Translator which has been developed for use with the GOAL Compiler. This translator enables the user to convert a compliled GOAL program to a highly general binary format which is designed to enable interpretive execution. The translator program provides user controls which are designed to enable the selection of various output types and formats. These controls provide a means for accommodating many of the implementation options which are discussed in the Interpretive Code Guideline document. The technical design approach is given. The relationship between the translator and the GOAL compiler is explained and the principal functions performed by the Translator are described. Specific constraints regarding the use of the Translator are discussed. The control options are described. These options enable the user to select outputs to be generated by the translator and to control vrious aspects of the translation processing.

Source record↗

NASA NTRS1974

Location of geologic structures from interpretation of ERTS-1 imagery, Carbon County, Wyoming

The author has identified the following significant results. Possible geologic structures in the basin sediments of Carbon County and vicinity were located by interpretation of ERTS-1 imagery. These same structures are not evident on existing conventional geologic maps of the area. Subsequent field checks confirmed much of the geologic interpretation, but revealed that two apparent closed structures identified on the ERTS-1 imagery were actually topographic pseudostructures in flat or homoclinal sediments. Stereoscopic coverage (where available) allows the interpreter to avoid such misinterpretations.

Marrs, R. W.↗