Search NASA⌕ Search

SEARCH · Search NASA

Results for “sampling methods”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Methodology for Extracting High-Molecular-Weight DNA from Field Collections of Macrofungi

Many macrofungi are impractical or impossible to culture. Consequently, DNA for long-read sequencing required for the assembly of high-quality genomes must be isolated from samples taken from the environment. Collection is often in remote locations, limiting the options for stabilising samples to methods that do not require refrigeration. Fungi contain species-specific arrays of metabolites that may complicate purification techniques and call for judgement to be made to apply appropriate modifications to the DNA extraction protocol in specific cases. The protocols and commentary we describe are informed by the preparation of DNA from a range of Australasian ectomycorrhizal and saprotrophic macrofungi. We collect samples into isopropanol at ambient temperature and employ a strategy of chromatin isolation followed by the sequential removal of unwanted molecular components to purify DNA.

Burgoyne, Leigh A↗
DOE OSTI2024

AGS-GNN: Attribute-guided Sampling for Graph Neural Networks

We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. (In homophilic graphs vertices of the same class are more likely to be connected, and vertices of different classes tend to be linked in heterophilic graphs.) While GNNs have been successfully applied to homophilic graphs, their application to heterophilic graphs remains challenging. The best-performing GNNs for heterophilic graphs do not fit the sampling paradigm, suffer high computational costs, and are not inductive. We employ samplers based on feature-similarity and feature-diversity to select subsets of neighbors for a node, and adaptively capture information from homophilic and heterophilic neighborhoods using dual channels. Currently, AGS-GNN is the only algorithm that we know of that explicitly controls homophily in the sampled subgraph through similar and diverse neighborhood samples. For diverse neighborhood sampling, we employ submodularity, which was not used in this context prior to our work. The sampling distribution is pre-computed and highly parallel, achieving the desired scalability. Using an extensive dataset consisting of 35 small (<=100K nodes) and large (>100K nodes) homophilic and heterophilic graphs, we demonstrate the superiority of AGS-GNN compare to the current approaches in the literature. AGS-GNN achieves comparable test accuracy to the best-performing heterophilic GNNs, even outperforming methods using the entire graph for node classification. AGS-GNN also converges faster compared to methods that sample neighborhoods randomly, and can be incorporated into existing GNN models that employ node or graph sampling.

artificial intelligence↗
NASA NTRS2013

Sample Handling in Extreme Environments

Harsh environments, such as that on Venus, preclude the use of existing equipment for functions that involve interaction with the environment. The operating limitations of current high temperature electronics are well below the actual temperature and pressure found on Venus (460 deg C and 92 atm), so proposed lander configurations typically include a pressure vessel where the science instruments are kept at Earth-like temperature and pressure (25 deg C and 1 atm). The purpose of this project was to develop and demonstrate a method for sample transfer from an external drill to internal science instruments for a lander on Venus. The initial concepts were string and pneumatically driven systems; and the latter system was selected for its ability to deliver samples at very high speed. The pneumatic system was conceived to be driven by the pressure difference between the Venusian atmosphere and the inside of the lander. The pneumatic transfer of a small capsule was demonstrated, and velocity data was collected from the lab experiment. The sample transfer system was modeled using CAD software and prototyped using 3D printing. General structural and thermal analyses were performed to approximate the proposed system's mass and effects on the temperature and pressure inside of the lander. Additionally, a sampler breadboard for use on Titan was tested and functionality problems were resolved.

sample handling↗
NASA NTRS2017

Method and Apparatus for Improving the Resolution of Digitally Sampled Analog Data

A system and method is described for converting an analog signal into a digital signal. The gain and offset of an ADC is dynamically adjusted so that the N-bits of input data are assigned to a narrower channel instead of the entire input range of the ADC. This provides greater resolution in the range of interest without generating longer digital data strings.

Liaghati, Amir L.↗
NASA NTRSDate not supplied

SURFACE EXOSURE TIMESCALES OF APOLLO CORE SAMPLE 73002 SPACE WEATHERED GRAINS

Introduction: Space weathering causes the surface regolith on airless bodies like the Moon to be morphologically, microstructurally, and chemically altered due to micrometeoroid bombardment and solar wind irradiation[1]. These processes produce a multitude of microstructural and chemical changes in individual soil grains that accumulate as grains are exposed on the surface over time. One microstructural feature produced by exposure to the solar wind is the development of ion-damaged rims on regolith grains via H+ and He+ ion irradiation. Also present are solar energetic particle (SEP) tracks, which are nanoscale lineations of ionization damage within grain interiors formed by high energy solar flare ions(primarily Fe group nuclei) which penetrate millimeters below the surface [2]. Recent work has confirmed that the thickness of solar wind-damaged amorphous rims on anorthite grains, nanocrystalline rims on olivine grains, and their respective SEP track densities are correlated with their surface exposure ages [3].Core sample 73002, recently released under the Apollo Next Generation Sample Analysis (ANGSA) Program, has provided an opportunity to study material collected from the light mantle formation during Apollo 17. The light mantle is thought to have been deposited via a landslide originating from the neighboring South Massif [4]. Spectral profiles and ferromagnetic resonance measurements of bulk soils sampled at cm-intervals from 73002 indicate the existence of mature regolith extending ~8 cm below the surface, suggesting the presence of an in-situ reworking zone in the core[5,6].Here we present the distribution of SEP track densities, solar wind damaged rim widths, and the corresponding surface exposure ages of grains residing in the proposed in-situ reworking zone of core 73002. Methods: Bulk samples of regolith from the first eight intervals and every following fourth interval down the core (dissection Pass 2) were delivered to Purdue University as <45 μm size fractions. The first eight0.5 cm intervals, representing the top 4cmof regolith, were individually dry sieved to a<20 μm size fraction, and grains were prepared by ultramicrotomy for analysis in the scanning transmission electron microscope (STEM). Bright field (BF) and dark field (DF) STEM images of solar flare tracks and solar wind damaged rims were acquired on a JEOL 2500SE TEM, equipped with a 60 mm2ultra-thin window silicon drift energy dispersive X-ray (EDX) spectrometer at NASA John-son Space Center. Grain compositions were determined by EDX compositional spectrum imaging. SEP track densities and amorphous rim thicknesses were measured on BF and DF images. Results: The fraction of grains with discernable space weathering features decreased with depth, with 100% in the first interval exhibiting space weathering features as opposed to ~60% in Interval 8.BF and DF STEM images show splash-melt and vapor deposited rims on outermost grain margins with embedded Fe-bearing nanoparticles ranging in size up to ~10 nm in diameter. Images also show solar-wind damaged rims below the vapor deposits, and SEP tracks present in grain interiors. The rim thicknesses and SEP track densities were determined using the methods of [3] for 54grains spanning the top 4 cm. ~80% of the grains analyzed were anorthite and ~20% were olivine. The track production rate from[3] was used to estimate the surface exposure times of the grains in this study. The majority of exposure times are in the 1 -5 MY range for all intervals. The lowest value was 3.8x 10¬5years in Interval 8and the highest value was1.1x 107years in Interval 6. Grains with the highest solar wind damage rim thicknesses and SEP track densities are found in Interval 6.The highest rim thickness and track density observed for anorthiteis126.5 nm and4.7 x 1011tracks/cm2, while for olivine they are 98.6 nm and 1.83 x 1011tracks/cm2, respectively. Discussion: Intervals from the top 4 cm of 73002 show similar SEP track density and solar wind damaged rim thickness distributions. Spectral and ferromagnetic resonance measurements of 73002 indicate an in-situ reworking zone up to~8 cm below the surface[5,6].The depth analyzed in this work is within the core’s uppermost in-situ reworking zone and may serve as an explanation to the homogenous distribution of exposure ages. The decreasing abundance of space weathered features with depth is consistent with regolith mixing models showing an exponential decay in regolith maturity with depth over time[7].A maximum surface exposure age determined via SEP track density is approximately 10million years. This timescale is consistent with the lower estimate of surface exposure ages of the light mantle determined in previous studies, which can range from 10s to over 100 Ma [4].It also falls within the core regolith reworking development timescale of ~17 Ma [5,6]. Additional analysis will be performed for a further seven intervals in 73002.

J A McFadden↗
NASA NTRS2019

Numerical Investigation and Optimization of a Flushwall Injector for Scramjet Applications at Hypervelocity Flow Conditions

An investigation utilizing Reynolds-averaged simulations (RAS) was performed in order to demonstrate the use of design and analysis of computer experiments (DACE) methods in Sandia’s DAKOTA software package for surrogate modeling and optimization. These methods were applied to a flow- path fueled with an interdigitated flushwall injector suitable for scramjet applications at hyper- velocity conditions and ascending along a constant dynamic pressure flight trajectory. The flight Mach number, duct height, spanwise width, and injection angle were the design variables selected to maximize two objective functions: the thrust potential and combustion efficiency. Because the RAS of this case are computationally expensive, surrogate models are used for optimization. To build a surrogate model a RAS database is created. The sequence of the design variables comprising the database were generated using a Latin hypercube sampling (LHS) method. A methodology was also developed to automatically build geometries and generate structured grids for each design point. The ensuing RAS analysis generated the simulation database from which the two objective functions were computed using a one-dimensionalization (1D) of the three-dimensional simulation data. The data were fitted using four surrogate models: an artificial neural network (ANN), a cubic polynomial, a quadratic polynomial, and a Kriging model. Variance-based decomposition showed that both objective functions were primarily driven by changes in the duct height. Multiobjective design optimization was performed for all four surrogate models via a genetic algorithm method. Optimal solutions were obtained at the upper and lower bounds of the flight Mach number range. The Kriging model predicted an optimal solution set that exhibited high values for both objective functions. Additionally, three challenge points were selected to assess the designs on the Pareto fronts. Further sampling among the designs of the Pareto fronts may be required to lower the surrogate model errors and perform more accurate surrogate-model-based optimization.

Shenoy, Rajiv R.↗
NASA NTRS1976

The usefulness of Skylab/EREP S-190 and S-192 imagery in multistage forest surveys

The author has identified the following significant results. The RMSE of point location achieved with the annotation system on S190A imagery was 100 m and 90 m in the x and y direction, respectively. Potential gains in sampling precision attributable to space derived imagery ranged from 4.9 to 43.3 percent depending on the image type, interpretation method, time of year, and sampling method applied. Seasonal variation was significant. S190A products obtained in September yielded higher gains than those obtained in June. Using 100 primary sample units as a base under simple random sampling, the revenue made available for incorporating space acquired data into the sample design to estimate timber volume was as high as $39,400.00.

Langley, P. G.↗
DOE OSTI2024

S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models

While projection-based reduced order models can reduce the dimension of full order solutions, the resulting reduced models may still contain terms that scale with the full order dimension. Hyper-reduction techniques are sampling-based methods that further reduce this computational complexity by approximating such terms with a much smaller dimension. The goal of this work is to introduce the points selection algorithm developed by Shin and Xiu as a hyper-reduction method. The selection algorithm was originally proposed as a stochastic collocation method for uncertainty quantification. Since the algorithm aims at maximizing a quantity $\mathcal{S}$ that measures both the column orthogonality and the determinant, we refer to the algorithm as S-OPT. Numerical examples are provided to demonstrate the performance of S-OPT and to compare its performance with a gappy proper orthogonal decomposition (POD) algorithm. Here, we found that using the S-OPT algorithm is shown to predict the full order solutions with higher accuracy than gappy POD especially when the number of sampling points is small, although we note that S-OPT shows slow asymptotic convergence with respect to the number of samples for some applications, e.g., Lagrangian hydrodynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A novel framework for increasing research transparency: Exploring the connection between diversity and innovation

A split sample/dual method research protocol is demonstrated to increase transparency while reducing the probability of false discovery. We apply the protocol to examine whether diversity in ownership teams increases or decreases the likelihood of a firm reporting a novel innovation using data from the 2018 United States Census Bureau’s Annual Business Survey. Transparency is increased in three ways: 1) all specification testing and identifying potentially productive models is done in an exploratory subsample that 2) preserves the validity of hypothesis test statistics fromde novoestimation in the holdout confirmatory sample with 3) all findings publicly documented in an earlier registered report and in this journal publication. Bayesian estimation procedures that leverage information from the exploratory stage included in the confirmatory stage estimation replace traditional frequentist null hypothesis significance testing. In addition to increasing statistical power by using information from the full sample, Bayesian methods directly estimate a probability distribution for the magnitude of an effect, allowing much richer inference. Estimated magnitudes of diversity along academic discipline, race, ethnicity, and foreign-born status dimensions are positively associated with innovation. A maximally diverse ownership team on these dimensions would be roughly six times more likely to report new-to-market innovation than a homophilic team.

Science & Technology - Other Topics↗
NASA NTRS1996

Ignition of Cellulosic Paper at Low Radiant Fluxes

The ignition of cellulosic paper by low level thermal radiation is investigated. Past work on radiative ignition of paper is briefly reviewed. No experimental study has been reported for radiative ignition of paper at irradiances below 10 Watts/sq.cm. An experimental study of radiative ignition of paper at these low irradiances is reported. Experimental parameters investigated and discussed include radiant power levels incident on the sample, the method of applying the radiation (focussed vs. diffuse Gaussian source), the presence and relative position of a separate pilot ignition source, and the effects of natural convection (buoyancy) on the ignition process in a normal gravity environment. It is observed that the incident radiative flux (in W/sq.cm) has the greatest influence on ignition time. For a given flux level, a focussed Gaussian source is found to be advantageous to a more diffuse, lower amplitude, thermal source. The precise positioning of a pilot igniter relative to gravity and to the fuel sample affects the ignition process, but the precise effects are not fully understood. Ignition was more readily achieved and sustained with a horizontal fuel sample, indicating the buoyancy plays a role in the ignition process of cellulosic paper. Smoldering combustion of doped paper samples was briefly investigated, and results are discussed.

White, K. Alan↗
DOE OSTI2025

Accelerating Hamiltonian Monte Carlo for Bayesian inference in neural networks and neural operators

Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s parameter space and the non-convexity of their posterior distributions. Therefore, various approximation techniques, such as variational inference (VI) or stochastic gradient MCMC, are often employed to infer the posterior distribution of the network parameters. Such approximations introduce inaccuracies in the inferred distributions, resulting in unreliable uncertainty estimates. In this work, we propose a hybrid approach that combines inexpensive VI and accurate HMC methods to efficiently and accurately quantify uncertainties in neural networks and neural operators. The proposed approach leverages an initial VI training on the full network. We examine the influence of individual parameters on the prediction uncertainty, which shows that a large proportion of the parameters do not contribute substantially to uncertainty in the network predictions. This information is then used to significantly reduce the dimension of the parameter space, and HMC is performed only for the subset of network parameters that strongly influence prediction uncertainties. This yields a framework for accelerating the full batch HMC for posterior inference in neural networks. We demonstrate the efficiency and accuracy of the proposed framework on deep neural networks and operator networks, showing that inference can be performed for large networks with tens to hundreds of thousands of parameters. Finally, we show that this method can effectively learn surrogates for complex physical systems by modeling the operator that maps from upstream conditions to wall-pressure data on a cone in hypersonic flow.

Bayesian inference↗
NASA NTRS2019

FIGS: spectral fitting constraints on the star formation history of massive galaxies since the cosmic noon

We constrain the stellar population properties of a sample of 52 massive galaxies – with stellar mass log (M(s)/M(⊙)) ≳ 10.5 – over the redshift range 0.5 < z < 2 by use of observer-frame optical and near-infrared slitless spectra from Hubble Space Telescope’s ACS and WFC3 grisms. The deep exposures (∼100 ks) allow us to target individual spectra of massive galaxies to F160W = 22.5 AB. Our spectral fitting approach uses a set of six base models adapted to the redshift and spectral resolution of each observation, and fits the weights of the base models, including potential dust attenuation, via a Markov Chain Monte Carlo method. Our sample comprises a mixed distribution of quiescent (19) and star-forming galaxies (33). We quantify the width of the age distribution (Δt) that is found to dominate the variance of the retrieved parameters according to principal component analysis. The population parameters follow the expected trend towards older ages with increasing mass, and Δt appears to weakly anticorrelate with stellar mass, suggesting a more efficient star formation at the massive end. As expected, the redshift dependence of the relative stellar age (measured in units of the age of the Universe at the source) in the quiescent sample rejects the hypothesis of a single burst (aka monolithic collapse). Radial colour gradients within each galaxy are also explored, finding a wider scatter in the star-forming subsample, but no conclusive trend with respect to the population parameters.

Ignacio Ferreras↗
NASA NTRS2012

Achieving Accuracy Requirements for Forest Biomass Mapping: A Spaceborne Data Fusion Method for Estimating Forest Biomass and Lidar Sampling Error

The synergistic use of active and passive remote sensing (i.e., data fusion) demonstrates the ability of spaceborne light detection and ranging (LiDAR), synthetic aperture radar (SAR) and multispectral imagery for achieving the accuracy requirements of a global forest biomass mapping mission (+/-20 Mg/ha or 20%, the greater of the two, for at least 80% of grid cells). A data fusion approach also provides a means to extend 3D information from discrete spaceborne LiDAR measurements of forest structure across scales much larger than that of the LiDAR footprint. For estimating biomass, these measurements mix a number of errors including those associated with LiDAR footprint sampling over regional-global extents. A general framework for mapping above ground live forest biomass density (AGB) with a data fusion approach is presented and verified using data from NASA field campaigns near Howland, ME, USA, to assess AGB and LiDAR sampling errors across a regionally representative landscape. We combined SAR and Landsat-derived optical (passive optical) image data to identify contiguous areas (>0.5 ha) that are relatively homogenous in remote sensing metrics (forest patches). We used this image-derived data with simulated spaceborne LiDAR derived from orbit and cloud cover simulations and airborne data from NASA's Laser Vegetation Imaging Sensor (LVIS) to compute AGB and estimate LiDAR sampling error for forest patches and 100 m, 250 m, 500 m, and 1 km grid cells. At both the patch and grid scales, we evaluated differences in AGB estimation and sampling error from the combined use of LiDAR with both SAR and passive optical and with either SAR or passive optical alone. First, this data fusion approach demonstrates that incorporating forest patches into the AGB mapping framework can provide sub-grid forest information for coarser grid-level AGB reporting. Second, a data fusion approach for estimating AGB using simulated spaceborne LiDAR with SAR and passive optical image combinations reduced forest AGB sampling errors 12%-38% from those where LiDAR is used with SAR or passive optical alone. In absolute terms, sampling errors were reduced from 14-40 Mg/ha to 11-28 Mg/ha across all grid scales and prediction methods, where minimum sampling errors were 11, 15, 18, and 22 Mg/ha for 1 km, 500 m, 250 m, and 100 m grid scales, respectively. Third, spaceborne global scale accuracy requirements were achieved whereby at least 80% of the grid cells at 100 m, 250 m, 500 m, and 1 km grid levels met AGB accuracy requirements using a combination of passive optical and SAR along with machine learning methods to predict vegetation structure metrics for forested areas without LiDAR samples. Finally, using either passive optical or SAR, accuracy requirements were met at the 500 m and 250 m grid level, respectively..

LiDAR↗
NASA NTRS1980

Minority carrier diffusion lengths and absorption coefficients in silicon sheet material

Most of the methods which have been developed for the measurement of the minority carrier diffusion length of silicon wafers require that the material have either a Schottky or an ohmic contact. The surface photovoltage (SPV) technique is an exception. The SPV technique could, therefore, become a valuable diagnostic tool in connection with current efforts to develop low-cost processes for the production of solar cells. The technique depends on a knowledge of the optical absorption coefficient. The considered investigation is concerned with a reevaluation of the absorption coefficient as a function of silicon processing. A comparison of absorption coefficient values showed these values to be relatively consistent from sample to sample, and independent of the sample growth method.

Dumas, K. A.↗
DOE OSTI2026

Structural and Mechanical Analysis of Individual Mineralized Collagen Fibrils Using In Situ Transmission Electron Microscopy

Bone serves as an example of nature’s architectured material with its characteristic blend of strength and toughness, all at a lightweight design. Given the hierarchical nature of these materials, it is essential to understand the governing mechanisms and organization of their constituents across length scales for bioinspired structural design. Despite recent advances in transmission electron microscopy (TEM) that have allowed us to witness the hierarchical arrangement of bone at micro-down to the nanoscale, we are still missing the details about the structural organization and mechanical properties of the main building blocks of bone─mineralized collagen fibrils (MCFs). Here, we present a method to extract individual MCFs from nature’s model material, mineralized turkey leg tendon, using a dropcasting procedure. By isolating the MCFs onto TEM supporting grids, we visualized the arrangement of organic and mineral phases within individual MCFs at the nanoscale. Using a four-dimensional scanning transmission electron microscopy (4D-STEM) approach, the orientation of individual mineral crystals within the MCFs was examined. Furthermore, we conducted in situ tensile experiments, revealing exceptional tensile strains of at least 8%, demonstrating the intricate relationship between structural organization and the mechanical behavior of MCFs. These insights into the ultrastructure of mineralized tissue building blocks, as well as the proposed sample-extraction method compatible with in situ mechanical testing, provide a strong basis for research into nature-inspired material design.

4D-STEM↗
NASA NTRS2014

Diversity in C-Xanes Spectra Obtained from Carbonaceous Solid Inclusions from Monahans Halite

Monahans meteorite (H5) contains fluid inclusion- bearing halite (NaCl) crystals [1]. Microthermometry and Raman spectroscopy showed that the fluid in the inclusions is an aqueous brine and they were trapped near 25degC [1]. Their continued presence in the halite grains requires that their incorporation into the H chondrite asteroid was post metamorphism [2]. Abundant solid inclusions are also present in the halites. The solid inclusions include abundant and widely variable organics [2]. Analyses by Raman microprobe, SEM/EDX, synchrotron X-ray diffraction and TEM reveal that these grains include macromolecular carbon similar in structure to CV3 chondrite matrix carbon, aliphatic carbon compounds, olivine (Fo99−59), high- and low-Ca pyroxene, feldspars, magnetite, sulfides, lepidocrocite, carbonates, diamond, apatite and possibly the zeolite phillipsite [3]. Here we report organic analyses of these carbonaceous residues in Monahans halite using C-, N-, and O- X-ray absorption near edge structure (XANES). Samples and Methods: Approximately 100 nm-thick sections were extracted with a focused ion beam (FIB) at JSC from solid inclusions from Monahans halite. The sections were analyzed using the scanning transmission X-ray microscope (STXM) on beamline 5.3.2.2 at the Advanced Light Source, Lawrence Berkeley National Laboratory for XANES spectroscopy. Results and Discussion: C-XANES spectra of the solid inclusions show micrometer-scale heterogeneity, indicating that the macromolecular carbon in the inclusions have complex chemical variations. C-XANES features include 284.7 eV assigned to aromatic C=C, 288.4-288.8 eV assigned to carboxyl, and 290.6 eV assigned to carbonate. The carbonyl features obtained by CXANES might have been caused by the FIB used in sample preparation. No specific N-XANES features are observed. The CXANES spectra obtained from several areas in the FIB sections include type 1&2 chondritic IOM like, type 3 chondritic IOM like, and none of the above. The natures of the macromolecular carbon in the solid inclusions observed by C-XANES are consistent with the previous studies showing that the carbonaceous solid inclusions have not originated from Monahans parent body [1-3], and have various origins, including various chondritic meteorite parent bodies as well as other unknown source(s).

Kebukawa, Y.↗
NASA NTRS2018

A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification

A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high dimensional input space of the training samples into a low dimensional space with the topology relations preserved. This makes SOMs supportive of organizing and visualizing complex data sets and have been pervasively used among numerous disciplines with different applications. Notwithstanding its wide applications, the self-organizing map is perplexed by its inherent randomness, which produces dissimilar SOM patterns even when being trained on identical training samples with the same parameters every time, and thus causes usability concerns for other domain practitioners and precludes more potential users from exploring SOM based applications in a broader spectrum. Motivated by this practical concern, we propose a deterministic approach as a supplement to the standard self-organizing map. In accordance with the theoretical design, the experimental results with satellite cloud data demonstrate the effective and efficient organization as well as simplification capabilities of the proposed approach.

Initialization method↗