Training of a crater detection algorithm for Mars crater imagery
This paper discusses the use of a crater detection algorithm for Mars crater imagery.
automatic feature identification↗
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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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Assessment of Envelope- and Machine Learning-Based Electrical Fault Type Detection Algorithms for Electrical Distribution Grids
This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Features and Characteristics of the New NASA MicroPuLse NETwork (MPLNET) Automatic Rain Detection Algorithm
The water cycle strongly influences life on Earth. In particular, the precipitation modifies the atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially low-intensity precipitation) at global scale, bedsides improving our understanding of the hydrological cycle, it is crucial to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios, i.e. to apply fast mitigation strategies. In this study we developed an algorithm to automatically detect precipitation from lidar measurements obtained by the National and Aeronautics Space Administration (NASA) Micropulse lidar network (MPLNET) permanent observational site in Goddard. The algorithm, once full operational, will deliver in Near Real Time (latency 1.5h) a new rain mask product that will be publicly available on MPLNET website as part of the new Version 3 Level 1.5 data. The methodology, based on an image processing technique, can detect only light precipitation events (defined by intensity and duration) as the morphological filters used through the detection process are applied on the lidar volume depolarization ratio range corrected composite images, i.e. heavy rain events are unusable as the lidar signal is completely extinguished after few meters in the precipitation or no signal detected because of the water accumulated on the receiver optics.
S. Lolli↗
DyG-DPCD: A Distributed Parallel Community Detection Algorithm for Large-Scale Dynamic Graphs
Dynamic (Temporal) graphs capture the valuable evolution of real-world systems, from the continuously evolving patterns of social interactions and genetic pathways to the dynamic fluctuations of economic forces. Detecting communities for such evolving networks poses unique challenges. Detecting and analyzing the evolution of communities within dynamic graphs unlocks valuable insights into the underlying structural and temporal patterns of real-world systems. However, the sheer volume of modern graph data and the inherent complexity of the temporal dimension pose significant challenges to scalable community detection algorithms. Addressing this gap, our work explores the limited landscape of scalable distributed-memory parallel methods specifically designed for dynamic network community detection. We propose a novel parallel algorithm, DyG-DPCD (Dynamic Graph Distributed Parallel Community Detection), to detect communities in dynamic networks using the Message Passing Interface (MPI) framework. We present a vertex-centric approach, allowing us to detect communities through local optimization. Furthermore, we enhance our baseline algorithm by incorporating three heuristics, which improve the algorithm’s performance significantly while maintaining the quality of the solutions. We demonstrate the efficiency of our algorithm by experimenting on several real-world large-scale networks with hundreds of millions of edges spanning diverse domains. Notably, DyG-DPCD achieves speedups between 25× and 30× for large networks that we experimented on using NERSC compute nodes. In conclusion, our algorithm outperforms the STINGER parallel re-agglomeration algorithm by 30×.
97 MATHEMATICS AND COMPUTING↗
Adaptation of the ISCCP cloud detection algorithm to combined AVHRR and SMMR arctic data
The International Satellite Cloud Climatology Project (ISCCP) cloud detection algorithm is applied to artic data, and modifications are suggested. Both Advanced Very High Resolution Radiometer (AVHRR) and Scanning Multichannel Microwave Radiometer (SMMR) data are examined. Synthetic AVHRR and SMMR data are also generated. Modifications suggested include the use of snow and ice data sets for the estimation of surface parameters, additional AVHRR channels, and surface class characteristic values when clear sky values cannot be obtained. Greatest improvement in computed cloud fraction is realized over snow and ice surfaces; over other surfaces all versions perform similarly. Since the use of SMMR for surface analysis increases the computational burden, its use may be justified only over snow and ice-covered regions.
Key, J.↗
Competitive evaluation of failure detection algorithms for strapdown redundant inertial instruments.
Seven algorithms for failure detection, isolation, and correction of strapdown inertial instruments in the dodecahedron configuration are competitively evaluated by means of a digital computer simulation that provides them with identical inputs. Their performance is compared in terms of orientation errors and computer burden. The analytical foundations of the algorithms are presented. The features that are found to contribute to superior performance are use of a definite logical structure, elimination of interaction between failures, different thresholds for first and second failures, use of the 'parity' test signals, and avoidance of iteration loops.
Wilcox, J. C.↗
Competitive evaluation of failure detection algorithms for strapdown redundant inertial instruments
Algorithms for failure detection, isolation, and correction of redundant inertial instruments in the strapdown dodecahedron configuration are competitively evaluated in a digital computer simulation that subjects them to identical environments. Their performance is compared in terms of orientation and inertial velocity errors and in terms of missed and false alarms. The algorithms appear in the simulation program in modular form, so that they may be readily extracted for use elsewhere. The simulation program and its inputs and outputs are described. The algorithms, along with an eight algorithm that was not simulated, also compared analytically to show the relationships among them.
Wilcox, J. C.↗
Electrical Fault and Power Quality Detection Algorithms and Customer-Owned DERs Monitoring with a Cyber Grid Guard System and DLT
In this study, the electrical fault and power quality detection algorithms and customer-owned DERs monitoring use cases were implemented, with a Cyber Grid Guard system and DLT. Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substations, and are associated with customer-owned distributed energy resources (DERs). Data from these IEDs, such as power meters and protection relays, must be kept confidential and of high integrity. Blockchain technology has the potential to increase microgrid resilience by enhancing data sharing security. The growing use of IEDs and customer-owned renewable energy sources (DERs) may make it necessary to connect Distributed Ledger Technology (DLT) with power system applications. We implemented the electrical faulted phase detection and power quality monitoring algorithms with a Cyber Grid Guard (CGG) system using DLT. In addition, the DERs (wind turbine farms) use case and protective relay cyber-event tests were assessed, by using the CGG system with DLT. In the experimental model, the testbed was created by using a real-time simulator and CGG system with power meters/ protective relays in the loop. The data collected from the CGG system and IEDs were compared with the same time stamp source. These results showed the successful assessment of protection, control and monitoring applications using a CGG system with DLT. In the future, power system applications for the ESGT with DERs and the CGG system will be based on executing smart contracts between electrical utilities and customer-owned DERs.
Piesciorovsky, Emilio↗
LU60645GT and MA132843GT Catalogues of Lunar and Martian Impact Craters Developed Using a Crater Shape-based Interpolation Crater Detection Algorithm for Topography Data
For Mars, 57,633 craters from the manually assembled catalogues and 72,668 additional craters identified using several crater detection algorithms (CDAs) have been merged into the MA130301GT catalogue. By contrast, for the Moon the most complete previous catalogue contains only 14,923 craters. Two recent missions provided higher-quality digital elevation maps (DEMs): SELENE (in 1/16° resolution) and Lunar Reconnaissance Orbiter (we used up to 1/512°). This was the main motivation for work on the new Crater Shape-based interpolation module, which improves previous CDA as follows: (1) it decreases the number of false-detections for the required number of true detections; (2) it improves detection capabilities for very small craters; and (3) it provides more accurate automated measurements of craters' properties. The results are: (1) LU60645GT, which is currently the most complete (up to ∼D>=8 km) catalogue of Lunar craters; and (2) MA132843GT catalogue of Martian craters complete up to ∼D>=2 km, which is the extension of the previous MA130301GT catalogue. As previously achieved for Mars, LU60645GT provides all properties that were provided by the previous Lunar catalogues, plus: (1) correlation between morphological descriptors from used catalogues; (2) correlation between manually assigned attributes and automated measurements; (3) average errors and their standard deviations for manually and automatically assigned attributes such as position coordinates, diameter, depth/diameter ratio, etc; and (4) a review of positional accuracy of used datasets. Additionally, surface dating could potentially be improved with the exhaustiveness of this new catalogue. The accompanying results are: (1) the possibility of comparing a large number of Lunar and Martian craters, of e.g. depth/diameter ratio and 2D profiles; (2) utilisation of a method for re-projection of datasets and catalogues, which is very useful for craters that are very close to poles; and (3) the extension of the previous framework for evaluation of CDAs with datasets and ground-truth catalogue for the Moon.
Craters↗
Data for training and testing radiation detection algorithms in an urban environment
The detection, identification, and localization of illicit nuclear materials in urban environments is of utmost importance for national security. Most often, the process of performing these operations consists of a team of trained individuals equipped with radiation detection devices that have built-in algorithms to alert the user to the presence nuclear material and, if possible, to identify the type of nuclear material present. To encourage the development of new detection, radioisotope identification, and source localization algorithms, a dataset consisting of realistic Monte Carlo-simulated radiation detection data from a 2 in. × 4 in. × 16 in. NaI(Tl) scintillation detector moving through a simulated urban environment based on Knoxville, Tennessee, was developed and made public in the form of a Topcoder competition. The methodology used to create this dataset has been verified using experimental data collected at the Fort Indiantown Gap National Guard facility. Realistic signals from special nuclear material and industrial and medical sources are included in the data for developing and testing algorithms in a dynamic real-world background.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms
Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration.
Cavur, Mahmut↗
Comparative Analysis of Daytime Fire Detection Algorithms, Using AVHRR Data for the 1995 Fire Season in Canda: Perspective for MODIS
Two fixed-threshold Canada Centre for Remote Sensing and European Space Agency (CCRS and ESA) and three contextual GIGLIO, International Geosphere and Biosphere Project, and Moderate Resolution Imaging Spectroradiometer (GIGLIO, IGBP, and MODIS) algorithms were used for fire detection with Advanced Very High Resolution Radiometer (AVHRR) data acquired over Canada during the 1995 fire season. The CCRS algorithm was developed for the boreal ecosystem, while the other four are for global application. The MODIS algorithm, although developed specifically for use with the MODIS sensor data, was applied to AVHRR in this study for comparative purposes. Fire detection accuracy assessment for the algorithms was based on comparisons with available 1995 burned area ground survey maps covering five Canadian provinces. Overall accuracy estimations in terms of omission (CCRS=46%, ESA=81%, GIGLIO=75%, IGBP=51%, MODIS=81%) and commission (CCRS=0.35%, ESA=0.08%, GIGLIO=0.56%, IGBP=0.75%, MODIS=0.08%) errors over forested areas revealed large differences in performance between the algorithms, with no relevance to type (fixed-threshold or contextual). CCRS performed best in detecting real forest fires, with the least omission error, while ESA and MODIS produced the highest omission error, probably because of their relatively high threshold values designed for global application. The commission error values appear small because the area of pixels falsely identified by each algorithm was expressed as a ratio of the vast unburned forest area. More detailed study shows that most commission errors in all the algorithms were incurred in nonforest agricultural areas, especially on days with very high surface temperatures. The advantage of the high thresholds in ESA and MODIS was that they incurred the least commission errors.
Ichoku, Charles↗
Wire Detection Algorithms for Navigation
In this research we addressed the problem of obstacle detection for low altitude rotorcraft flight. In particular, the problem of detecting thin wires in the presence of image clutter and noise was studied. Wires present a serious hazard to rotorcrafts. Since they are very thin, their detection early enough so that the pilot has enough time to take evasive action is difficult, as their images can be less than one or two pixels wide. Two approaches were explored for this purpose. The first approach involved a technique for sub-pixel edge detection and subsequent post processing, in order to reduce the false alarms. After reviewing the line detection literature, an algorithm for sub-pixel edge detection proposed by Steger was identified as having good potential to solve the considered task. The algorithm was tested using a set of images synthetically generated by combining real outdoor images with computer generated wire images. The performance of the algorithm was evaluated both, at the pixel and the wire levels. It was observed that the algorithm performs well, provided that the wires are not too thin (or distant) and that some post processing is performed to remove false alarms due to clutter. The second approach involved the use of an example-based learning scheme namely, Support Vector Machines. The purpose of this approach was to explore the feasibility of an example-based learning based approach for the task of detecting wires from their images. Support Vector Machines (SVMs) have emerged as a promising pattern classification tool and have been used in various applications. It was found that this approach is not suitable for very thin wires and of course, not suitable at all for sub-pixel thick wires. High dimensionality of the data as such does not present a major problem for SVMs. However it is desirable to have a large number of training examples especially for high dimensional data. The main difficulty in using SVMs (or any other example-based learning method) is the need for a very good set of positive and negative examples since the performance depends on the quality of the training set.
Kasturi, Rangachar↗
Obstacle Detection Algorithms for Rotorcraft Navigation
In this research we addressed the problem of obstacle detection for low altitude rotorcraft flight. In particular, the problem of detecting thin wires in the presence of image clutter and noise was studied. Wires present a serious hazard to rotorcrafts. Since they are very thin, their detection early enough so that the pilot has enough time to take evasive action is difficult, as their images can be less than one or two pixels wide. After reviewing the line detection literature, an algorithm for sub-pixel edge detection proposed by Steger was identified as having good potential to solve the considered task. The algorithm was tested using a set of images synthetically generated by combining real outdoor images with computer generated wire images. The performance of the algorithm was evaluated both, at the pixel and the wire levels. It was observed that the algorithm performs well, provided that the wires are not too thin (or distant) and that some post processing is performed to remove false alarms due to clutter.
Kasturi, Rangachar↗
An Improved Far-Field Small Unmanned Aerial System Optical Detection Algorithm
Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation for autonomous small Unmanned Aerial System (sUAS) operations. This work presents an aircraft detect and track pipeline that fuses image differencing and morphological filtering detections for inputs to a Kalman-based object tracking pipeline. The pipeline is evaluated using two types of flight encounters: 1) motorcopter sUAS vs. fixed-wing sUAS 2) motorcopter sUAS vs general aviation plane.
Chester Dolph↗
Algorithmic Detection of Elemental Biosignatures
Machine learning models that classify a sample as indicative or non-indicative of life could play an important role in life-detection missions. Their predictions result from agnostic algorithms and thereby add redundancy to judgements resulting from human expertise. Additionally, their important features can reveal the most informative measurements within the operational constraints of a life-detection mission. The Ladder of Life Detection (Neveu 2018) identifies the need for an understanding of how combinations of multiple biosignatures affect overall confidence. The present work provides a starting point to answer this need, and future work will expand the data types to obtain even more predictive combinations of features. Elemental abundance was chosen as a starting set of features due to its availability in diverse sample types, which are needed to train a generalizable model. A standardized dataset was collected, including 35 non-indicative, e.g., lunar rock, basalt; 19 indicative mixed, e.g., seawater, agricultural soil; 46 indicative non-alive, e.g., coal, chalk; and 10 indicative alive, e.g., biofilm, bacteria. This dataset could be valuable for complementary biosignature research. The samples were standardized to the same limit of detection of a simulated mission scenario. Four classification models were used: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), and Gaussian naïve Bayes (GNB). To obtain feature importances, KNN was run on three principal components of the training data and LR and SVM were run with L1 and L2 regularization. The performances and feature importances of the six model variants on 40:60 train to validation ratios were assessed with Monte Carlo simulations. ROC AUC and mean accuracy scores ranged between 82% - 94%, with sensitivity greater than specificity. For indicative of life predictors, all models had C and Ca as strong and Cl as medium; a majority of models had N, K, and P as medium. For non-indicative of life predictors, all models had Si as strong, and a majority of models had Mg, Al, and Ti as medium. Varied elements were Fe (slightly non-indicative), H (slightly indicative), O (widely varied), Na, Mn, and S. These results serve as a proof of concept and suggest important elemental signals beyond merely the CHNOPS of Earth-based life.
Algorithmic↗
Computer simulation and evaluation of edge detection algorithms and their application to automatic path selection
The construction of an autonomous roving vehicle requires the development of complex data-acquisition and processing systems, which determine the path along which the vehicle travels. Thus, a vehicle must possess algorithms which can (1) reliably detect obstacles by processing sensor data, (2) maintain a constantly updated model of its surroundings, and (3) direct its immediate actions to further a long range plan. The first function consisted of obstacle recognition. Obstacles may be identified by the use of edge detection techniques. Therefore, the Kalman Filter was implemented as part of a large scale computer simulation of the Mars Rover. The second function consisted of modeling the environment. The obstacle must be reconstructed from its edges, and the vast amount of data must be organized in a readily retrievable form. Therefore, a Terrain Modeller was developed which assembled and maintained a rectangular grid map of the planet. The third function consisted of directing the vehicle's actions.
Longendorfer, B. A.↗
Level 1 Cloud Detection Algorithm Theoretical Basis
This Algorithm Basis (ATB) document describes the algorithms used to retrieve the Radiometic Camera-by-camera Cloud Mask (RCCM) within the MISR level 1B2 Geo-rectified Radiance Product.
algorithm theoretical cloud detection↗