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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Air Classification of Forest Residue for Tissue and Ash Separation Efficiency

The goal of this Case Study was to evaluate the performance of air classification of logging residues toward meeting conversion CMAs for carbon and ash contents, as compared to the static status quo Base Case system in which the residues are first dried and then ground in a hammer mill with a 6 mm screen and fines less than 1.18 mm are removed. Also considered were moisture and ash impacts on throughput and Overall Operating Effectiveness (OOE), as well as delivered feedstock cost and minimum fuel selling price (MFSP). Laboratory data on the impacts of fan speed and moisture content on the separation efficiency of soil ash, needles and bark from white wood were received from FCIC Subtask 5.2: Preprocessing, High Temperature Conversion Preprocessing (Jordan Klinger and Tiasha Bhattacharjee, INL). Average throughput and energy consumption data were obtained from the Bioenergy Feedstock National User Facility (BFNUF) (Neal Yancey, INL) for the same air classifier. These data were utilized to develop the necessary response surface equations to perform throughput analysis using discrete event simulation. Feedstock-Conversion Interface Consortium. Because the Base Case status quo system utilizes drying prior to grinding, we modeled the Case Study with drying prior to air classification and subsequent grinding of the separated white wood to isolate the individual quality and cost impacts of air classification relative to the Base Case system.

CMA↗
DOE OSTI2024

Topological and Dynamical Representations for Radio Frequency Signal Classification

Radio Frequency (RF) signals are found throughout our world, carrying over-the-air information for both digital and analog uses with applications ranging from WiFi to the radio. One area of focus in RF signal analysis is determining the modulation schemes employed in these signals which is crucial in many RF signal processing domains from secure communication to spectrum monitoring. This work investigates the accuracy and noise robustness of novel Topological Data Analysis (TDA) and dynamic representation based approaches paired with a small convolution neural network for RF signal modulation classification with a comparison to state-of-the-art deep neural network approaches. We show that using TDA tools, like Vietoris-Rips and lower star filtrations, and the Takens' embedding in conjunction with a standard shallow neural network we can capture the intrinsic dynamical, geometric, and topological features of the underlying signal's manifold, offering informative representations of the RF signals. Our approach is effective in handling the modulation classification task and is notably noise robust, outperforming the commonly used deep neural network approaches in mode classification. Moreover, our fusion of dynamical and topological information is able to attain similar performance to deep neural network architectures with significantly smaller training datasets.

Myers, Audun D.↗
DOE OSTI2025

Power System Feature-Based Event Classification by Means of Multiple PMU Data

Abstract—Phasor Measurement Units (PMUs) provide time synchronized measurements across the power grid, enabling data driven event detection and classification for enhanced system monitoring and situational awareness. However, variations in event duration, spatial extent, and severity, along with coincident events, pose challenges for conventional classification models that require fixed-size inputs. This paper presents a feature-based framework that aggregates diverse attributes from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, and Multilayer Perceptron. A probabilistic post-processing scheme is further introduced to enable multi-label classification in the presence of overlapping events. Experiments using real-world PMU data demonstrate that the Random Forest model achieves 95% accuracy, while the proposed post-processing method yields an additional 3% improvement.

Nematirad, Reza↗
DOE OSTI2012

Hazard Classification of the Remote Handled Low-Level Waste Disposal Facility

The Battelle Energy Alliance (BEA) at the Idaho National Laboratory (INL) is constructing a new facility to replace remote-handled low-level radioactive waste disposal capability for INL and Naval Reactors Facility operations. Current disposal capability at the Radioactive Waste Management Complex (RWMC) will continue until the facility is full or closed for remediation (estimated at approximately fiscal year 2015). Development of a new onsite disposal facility is the highest ranked alternative and will provide RH-LLW disposal capability and will ensure continuity of operations that generate RH-LLW for the foreseeable future. As a part of establishing a safety basis for facility operations, the facility will be categorized according to DOE-STD-1027-92. This classification is important in determining the scope of analyses performed in the safety basis and will also dictate operational requirements of the completed facility. This paper discusses the issues affecting hazard classification in this nuclear facility and impacts of the final hazard categorization.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Towards catchment classification in data-scarce regions

Assessing spatial variation in hydrologic processes can help to inform freshwater management and advance ecological understanding, yet many areas lack sufficient flow records on which to base classifications. Seeking to address this challenge, we apply concepts developed in data-rich settings to public, global data in order to demonstrate a broadly replicable approach to characterizing hydrologic variation. The proposed approach groups the basins associated with reaches in a river network according to key environmental drivers of hydrologic conditions. This initial study examines Colorado (USA), where long-term streamflow records permit comparison to previously distinguished flow regime types, and the Republic of Ecuador, where data limitations preclude such analysis. The flow regime types assigned to gages in Colorado corresponded reasonably well to the classes distinguished from environmental features. The divisions in Ecuador reflected major known biophysical gradients while also providing a higher resolution supplement to an existing depiction of freshwater ecoregions. Although freshwater policy and management decisions occur amidst uncertainty and imperfect knowledge, this classification framework offers a rigorous and transferrable means to distinguish catchments in data-scarce regions. The maps and attributes of the resulting ecohydrologic classes offer a departure point for additional study and data collection programs such as the placement of stations in under-monitored classes, and the divisions may serve as a preliminary template with which to structure conservation efforts such as environmental flow assessments.

13 HYDRO ENERGY↗
DOE OSTI2019

Gastrointestinal stromal tumor risk classification: spectral CT quantitative parameters

Purpose: To examine the value of spectral CT quantitative parameters in gastrointestinal stromal tumor (GIST) risk classification. Materials and methods: This retrospective study was approved by the institutional review board. The requirement for informed consent was signed. The authors evaluated 86 patients (30 high risk, 22 medium risk, 28 low risk, and 6 very low risk; mean age: 59 years [range 19–83 years]) with pathologically confirmed GIST who underwent plain and triple-phase contrast-enhanced CT with spectral CT imaging mode from March 2015 through September 2017, with manual follow-up. Quantitative parameters including the CT value of 70 keV monochromatic images, the slope of spectral curves, and the normalized iodine concentration (NIC) and water (iodine) concentrations were measured and calculated, and conducted a power analysis of the above data. Results: (1) The CT values at 70 keV of the high-risk group were higher than the intermediate and low groups in each of the enhanced phases (P ≤ 0.001), no significant differences in the intermediate-risk and low-risk groups were noted (P = 0.874, 0.871, 0.831, respectively). (2) The slope of the spectral curve of the high-risk group was higher than those of the intermediate and low groups in each of the enhanced phases (P ≤ 0.001), and there were no significant differences between the intermediate- and low-risk groups (P = 0.069, 0.466, 0.840, respectively). (3) The NIC of the high-risk group significantly differed from the lower risk groups (P ≤ 0.001). There was also no significant difference observed between the intermediate- and low-risk groups (P = 0.671, 0.457, 0.833, respectively). (4) The power analysis results show that only the low-risk group with delay period is 0.530, the rest groups are all greater than 0.999. Conclusion: Dual-energy spectral CT with quantitative analysis may help to increase the accuracy in differentiating the pathological risk classification of GIST between high risk and non-high risk, preoperatively. There were limitations for distinguishing the intermediate- and low-risk groups.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem

Some research topics belonging to classification problems in the nuclear industry, such as fault diagnosis and accident identification, can be solved by feature extraction and subsequent application of statistical machine learning classifiers. Recently, deep neural network-based methods with automatic feature extraction and high accuracy have gained wide attention. They usually require large-scale training data, however, plant fault or accident data are scarce or difficult to obtain. Here this paper proposes a convolutional network (CNN)-based transfer learning method to solve this problem. The network's shallow layer is derived from a pre-trained CNN based on the ImageNet database to automatically extract features, and the deep layer is customized to match the classification problem. Data in non-image formats are converted to image formats and subsequently used to train the network. Case studies of rotating machines fault diagnosis show that the proposed method requires only limited training data to achieve high accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2012

Prognostic Value of Subclassification Using MRI in the T4 Classification Nasopharyngeal Carcinoma Intensity-Modulated Radiotherapy Treatment

Purpose: To subclassify patients with the T4 classification nasopharyngeal carcinoma (NPC), according to the seventh edition of the American Joint Committee on Cancer staging system, using magnetic resonance imaging (MRI), and to evaluate the prognostic value of subclassification after intensity-modulated radiotherapy (IMRT). Methods and Materials: A total of 140 patients who underwent MRI and were subsequently histologically diagnosed with nondisseminated classification T4 NPC received IMRT as their primary treatment and were included in this retrospective study. T4 patients were subclassified into two grades: T4a was defined as a primary nasopharyngeal tumor with involvement of the masticator space only; and T4b was defined as involvement of the intracranial region, cranial nerves, and/or orbit. Results: The 5-year overall survival (OS) rate and distant metastasis-free survival (DMFS) rate for T4a patients (82.5% and 87.0%, respectively), were significantly higher than for T4b patients (62.6% and 66.8%; p = 0.033 and p = 0.036, respectively). The T4a/b subclassification was an independent prognostic factor for OS (hazard ratio = 2.331, p = 0.032) and DMFS (hazard ratio = 2.602, p = 0.034), and had no significant effect on local relapse-free survival. Conclusions: Subclassification of T4 patients, as T4a or T4b, using MRI according to the site of invasion, has prognostic value for the outcomes of IMRT treatment in NPC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Classification System for Identifying Women at Risk for Altered Partial Breast Irradiation Recommendations After Breast Magnetic Resonance Imaging

Purpose: To study the utility of preoperative breast MRI for partial breast irradiation (PBI) patient selection, using multivariable analysis of significant risk factors to create a classification rule. Methods and Materials: Between 2002 and 2009, 712 women with newly diagnosed breast cancer underwent preoperative bilateral breast MRI at Mayo Clinic Florida. Of this cohort, 566 were retrospectively deemed eligible for PBI according to the National Surgical Adjuvant Breast and Bowel Project Protocol B-39 inclusion criteria using physical examination, mammogram, and/or ultrasound. Magnetic resonance images were then reviewed to determine their impact on patient eligibility. The patient and tumor characteristics were evaluated to determine risk factors for altered PBI eligibility after MRI and to create a classification rule. Results: Of the 566 patients initially eligible for PBI, 141 (25%) were found ineligible because of pathologically proven MRI findings. Magnetic resonance imaging detected additional ipsilateral breast cancer in 118 (21%). Of these, 62 (11%) had more extensive disease than originally noted before MRI, and 64 (11%) had multicentric disease. Contralateral breast cancer was detected in 28 (5%). Four characteristics were found to be significantly associated with PBI ineligibility after MRI on multivariable analysis: premenopausal status (P=.021), detection by palpation (P<.001), first-degree relative with a history of breast cancer (P=.033), and lobular histology (P=.002). Risk factors were assigned a score of 0-2. The risk of altered PBI eligibility from MRI based on number of risk factors was 0:18%; 1:22%; 2:42%; 3:65%. Conclusions: Preoperative bilateral breast MRI altered the PBI recommendations for 25% of women. Women who may undergo PBI should be considered for breast MRI, especially those with lobular histology or with 2 or more of the following risk factors: premenopausal, detection by palpation, and first-degree relative with a history of breast cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types

In the last decade, the widespread adoption of electronic health record documentation has created huge opportunities for information mining. Natural language processing (NLP) techniques using machine and deep learning are becoming increasingly widespread for information extraction tasks from unstructured clinical notes. Disparities in performance when deploying machine learning models in the real world have recently received considerable attention. In the clinical NLP domain, the robustness of convolutional neural networks (CNNs) for classifying cancer pathology reports under natural distribution shifts remains understudied. In this research, we aim to quantify and improve the performance of the CNN for text classification on out-of-distribution (OOD) datasets resulting from the natural evolution of clinical text in pathology reports. We identified class imbalance due to different prevalence of cancer types as one of the sources of performance drop and analyzed the impact of previous methods for addressing class imbalance when deploying models in real-world domains. Our results show that our novel class-specialized ensemble technique outperforms other methods for the classification of rare cancer types in terms of macro F1 scores. We also found that traditional ensemble methods perform better in top classes, leading to higher micro F1 scores. Based on our findings, we formulate a series of recommendations for other ML practitioners on how to build robust models with extremely imbalanced datasets in biomedical NLP applications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

SympGNNs: Symplectic Graph Neural Networks for identifying high-dimensional Hamiltonian systems and node classification

Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many-body systems. Herein, we introduce Symplectic Graph Neural Networks (SympGNNs) that can effectively handle system identification in high-dimensional Hamiltonian systems, as well as node classification. SympGNNs combine symplectic maps with permutation equivariance, a property of graph neural networks. Specifically, we propose two variants of SympGNNs: (i) G-SympGNN and (ii) LA-SympGNN, arising from different parameterizations of the kinetic and potential energy. We demonstrate the capabilities of SympGNN on two physical examples: a 40-particle coupled Harmonic oscillator, and a 2000-particle molecular dynamics simulation in a two-dimensional Lennard-Jones potential. Furthermore, we demonstrate the performance of SympGNN in the node classification task, achieving accuracy comparable to the state-of-the-art. Finally, we also empirically show that SympGNN can overcome the oversmoothing and heterophily problems, two key challenges in the field of graph neural networks.

Deep learning↗
DOE OSTI2013

The evaluation of an analytical protocol for the determination of substances in waste for hazard classification

Highlights: • Knowledge of wastes in substances will be necessary to assess HP1–HP15 hazard properties. • A new analytical protocol is proposed for this and tested by two service laboratories on 32 samples. • Sixty-three percentage of the samples have a satisfactory analytical balance between 90% and 110%. • Eighty-four percentage of the samples were classified identically (Seveso Directive) for their hazardousness by the two laboratories. • The method, in progress, is being normalized in France and is be proposed to CEN. - Abstract: The classification of waste as hazardous could soon be assessed in Europe using largely the hazard properties of its constituents, according to the the Classification, Labelling and Packaging (CLP) regulation. Comprehensive knowledge of the component constituents of a given waste will therefore be necessary. An analytical protocol for determining waste composition is proposed, which includes using inductively coupled plasma (ICP) screening methods to identify major elements and gas chromatography/mass spectrometry (GC–MS) screening techniques to measure organic compounds. The method includes a gross or indicator measure of ‘pools’ of higher molecular weight organic substances that are taken to be less bioactive and less hazardous, and of unresolved ‘mass’ during the chromatography of volatile and semi-volatile compounds. The concentration of some elements and specific compounds that are linked to specific hazard properties and are subject to specific regulation (examples include: heavy metals, chromium(VI), cyanides, organo-halogens, and PCBs) are determined by classical quantitative analysis. To check the consistency of the analysis, the sum of the concentrations (including unresolved ‘pools’) should give a mass balance between 90% and 110%. Thirty-two laboratory samples comprising different industrial wastes (liquids and solids) were tested by two routine service laboratories, to give circa 7000 parameter results. Despite discrepancies in some parameters, a satisfactory sum of estimated or measured concentrations (analytical balance) of 90% was reached for 20 samples (63% of the overall total) during this first test exercise, with identified reasons for most of the unsatisfactory results. Regular use of this protocol (which is now included in the French legislation) has enabled service laboratories to reach a 90% mass balance for nearly all the solid samples tested, and most of liquid samples (difficulties were caused in some samples from polymers in solution and vegetable oil). The protocol is submitted to French and European normalization bodies (AFNOR and CEN) and further improvements are awaited.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier

Highlights: • Solid waste bin level detection using Dynamic Time Warping (DTW). • Gabor wavelet filter is used to extract the solid waste image features. • Multi-Layer Perceptron classifier network is used for bin image classification. • The classification performance evaluated by ROC curve analysis. - Abstract: The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. Moreover, a range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. In this paper, we illustrate how the method can be used to: (1) distinguish between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Ensemble voting-based fault classification and location identification for a distribution system with microgrids using smart meter measurements

This study presents an ensemble learning approach for fault classification and location identification in a smart distribution network containing photovoltaics (PV)-based microgrid. Lack of available data points and the unbalanced nature of the distribution system make fault handling a challenging task for utilities. The proposed method uses event-driven voltage data from smart meters to classify and locate faults. The ensemble voting classifier is composed of three base learners; random forest, k-nearest neighbours, and artificial neural network. The fault location (FL) task has been formulated as a classification problem where the fault type is classified in the first step and based on the fault type, the faulty bus is identified. The method is tested on IEEE-123 bus system modified with added PV-based microgrid along with dynamic loading conditions and varying fault resistances from 0 to 20 Ω for both unbalanced and balanced fault types. A further sensitivity analysis has been done to test the robustness of the proposed method under various noise levels and data loss errors in the smart meter measurements. The ensemble method shows improved performance and robustness compared to some previously proposed FL methods. Finally, the proposed method has been experimentally validated on a real-time simulation-based testbed using a state-of-the-art digital real-time simulator, industry standard DNP3 communication protocol and a cpu-based control centre running the FL algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Classification of constraints and degrees of freedom for quadratic discrete actions

We provide a comprehensive classification of constraints and degrees of freedom for variational discrete systems governed by quadratic actions. This classification is based on the different types of null vectors of the Lagrangian two-form and employs the canonical formalism developed in Dittrich and Höhn [“Constraint analysis for variational discrete systems,” J. Math. Phys. 54, 093505 (2013); e-print http://arxiv.org/abs/arXiv:1303.4294 [math-ph]] and Höhn [“Quantization of systems with temporally varying discretization I: Evolving Hilbert spaces,” J. Math. Phys. 55, 083508 (2014); e-print http://arxiv.org/abs/arXiv:1401.6062 [gr-qc]]. The analysis is carried out in both the classical and quantum theory and applies to systems with both temporally varying or constant discretization. In particular, it is shown explicitly how changes in the discretization, e.g., resulting from canonical coarse graining or refining operations or an evolving background geometry, change the dynamical content of the system. It is demonstrated how, on a temporally varying discretization, constraints, Dirac observables, symmetries, reduced phase spaces, and physical Hilbert spaces become spacetime region dependent. These results are relevant for free field theory on an evolving lattice and linearized discrete gravity models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Classification of Malaysia aromatic rice using multivariate statistical analysis

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Threshold selection for classification of MR brain images by clustering method

Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗