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1,855 results for “Framework”
Development and Application of a Decision-Making Aid Tool for Amputation Surgery in Diabetic Foot Patients Based on the Ottawa Decision Support Framework
ClinicalTrials.gov study NCT07252089. IPD Sharing: NO. Countries: 0. Publications: 0.
We Designed and Implemented the 'MASTER Framework' as a Theoretical Framework to Guide the Design and Delivery of PE and Sports.
ClinicalTrials.gov study NCT06496464. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Bio-HPP vs Cast Co-cr as Implant Framework
ClinicalTrials.gov study NCT05861388. IPD Sharing: NO. Countries: 0. Publications: 0.
GFRC Enhance Patient Satisfaction and Reduce Complications in Maxillary Implants With Pekkton Frameworks
ClinicalTrials.gov study NCT06581354. IPD Sharing: NO. Countries: 0. Publications: 0.
ONCOlogy-targeted NLP-powered Federated Hyper-archItecture and Data Sharing Framework for Health Data Reusability
ClinicalTrials.gov study NCT05060835. IPD Sharing: NO. Countries: 0. Publications: 0.
A Reproducibility-Based Computational Framework Identifies An Inducible, Enhanced Antiviral Dendritic Cell State In HIV-1 Elite Controllers
GEO Series GSE108445. Homo sapiens. 420 samples. Type: Expression profiling by high throughput sequencing.
A systems biology approach for defining the molecular framework of the hematopoietic stem cell niche
GEO Series GSE44181. Mus musculus. 51 samples. Type: Expression profiling by array.
Genome-Wide Direct Target Identification Reveals the transcriptional regulatory Framework of WOX11 in Root Development in Rice
GEO Series GSE84933. Oryza sativa Japonica Group. 4 samples. Type: Expression profiling by high throughput sequencing.
Empirical Evaluation of Diagnostic Algorithm Performance Using a Generic Framework
A variety of rule-based, model-based and datadriven techniques have been proposed for detection and isolation of faults in physical systems. However, there have been few efforts to comparatively analyze the performance of these approaches on the same system under identical conditions. One reason for this was the lack of a standard framework to perform this comparison. In this paper we introduce a framework, called DXF, that provides a common language to represent the system description, sensor data and the fault diagnosis results; a run-time architecture to execute the diagnosis algorithms under identical conditions and collect the diagnosis results; and an evaluation component that can compute performance metrics from the diagnosis results to compare the algorithms. We have used DXF to perform an empirical evaluation of 13 diagnostic algorithms on a hardware testbed (ADAPT) at NASA Ames Research Center and on a set of synthetic circuits typically used as benchmarks in the model-based diagnosis community. Based on these empirical data we analyze the performance of each algorithm and suggest directions for future development.
Fatigue Damage Prognosis in FRP Composites by Combining Multi-Scale Degradation Fault Modes in an Uncertainty Bayesian Framework
In this work, a framework for the estimation of the fatigue damage propagation in CFRP composites is proposed. Macro-scale phenomena such as stiffness and strength degradation are predicted by connecting micro-scale and macro-scale damage models in a Bayesian filtering framework that also allows incorporating uncertainties in the prediction. The approach is demonstrated on data collected from a run-to-failure tension-tension fatigue experiment measuring the evolution of fatigue damage in CRFP cross-ply laminates. Results are presented for the prediction of expected end of life for a given panel with the associated uncertainty estimates.
Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework
This paper presents an empirical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.*
Communication Optimizations for a Wireless Distributed Prognostic Framework
Distributed architecture for prognostics is an essential step in prognostic research in order to enable feasible real-time system health management. Communication overhead is an important design problem for such systems. In this paper we focus on communication issues faced in the distributed implementation of an important class of algorithms for prognostics – particle filters. In spite of being computation and memory intensive, particle filters lend well to distributed implementation except for one significant step – resampling. We propose new resampling scheme called parameterized resampling that attempts to reduce communication between collaborating nodes in a distributed wireless sensor network. Analysis and comparison with relevant resampling schemes are also presented. A battery health management system is used as a target application.
Towards a Framework for Evaluating and Comparing Diagnosis Algorithms
Diagnostic inference involves the detection of anomalous system behavior and the identification of its cause, possibly down to a failed unit or to a parameter of a failed unit. Traditional approaches to solving this problem include expert/rule-based, model-based, and data-driven methods. Each approach (and various techniques within each approach) use different representations of the knowledge required to perform the diagnosis. The sensor data is expected to be combined with these internal representations to produce the diagnosis result. In spite of the availability of various diagnosis technologies, there have been only minimal efforts to develop a standardized software framework to run, evaluate, and compare different diagnosis technologies on the same system. This paper presents a framework that defines a standardized representation of the system knowledge, the sensor data, and the form of the diagnosis results – and provides a run-time architecture that can execute diagnosis algorithms, send sensor data to the algorithms at appropriate time steps from a variety of sources (including the actual physical system), and collect resulting diagnoses. We also define a set of metrics that can be used to evaluate and compare the performance of the algorithms, and provide software to calculate the metrics.
RSPF-based Prognosis Framework for Estimation of Remaining Useful Life in Energy Storage Devices
This paper presents a case study where a RSPF-based prognosis framework is applied to estimate the remaining useful life of an energy storage device (Li-Ion battery). A comparison based on prognosis performance metrics indicates that the RSPF-based prognostic approach is more suitable than classic PF methods to represent rare events such as capacity regeneration phenomena between charging periods, in terms of accuracy of the state estimate and steadiness of the RUL estimate. We surmise that the existence of particles in the tails of the state pdf allow the RSPF-based prognostic algorithm to generally provide a more conservative estimate of the RUL of the faulty piece of equipment. We surmise that it also helps to incorporate the probability of rare and costly events in the evolution of the fault condition in time.
DXC'11 Framework and Oracle
The DXC Framework is a collection of programs and APIs for running and evaluating diagnostic algorithms (DAs) under identical experimental conditions. It is complementary to system XML catalogs and data sets. The Oracle for DXC'11 is the same as DXC'10 and provides a querying capability to the DAs to determine the correct actions for given faults. Because the Oracle uses proprietary NASA software, it is provided only in binary form. Thus the framework is available as a windows binary installer, linux source files + binary oracle, or windows source files + binary oracle. Please see the included READMEs and example DAs for more information.
Model Adaptation for Prognostics in a Particle Filtering Framework
One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well- designed prognostic model that can take advantage of the model adaptation properties of a particle filter.*
An Integrated Model-Based Diagnostic and Prognostic Framework
Systems health monitoring is essential in guar- anteeing the safe, efficient, and correct opera- tion of complex engineered systems. Diagnosis, which consists of detection, isolation and identi- fication of faults; and prognosis, which consists of prediction of the remaining useful life of com- ponents, subsystems, or systems; constitute sys- tems health monitoring. This paper presents an integrated model-based diagnostic and prognos- tic framework, where we make use of a com- mon modeling paradigm to model both the nom- inal and faulty behavior in all aspects of systems health monitoring. We illustrate our approach us- ing a simulated propellant loading system that in- cludes tanks, valves, and pumps.
Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework
This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and esti- mation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochem- ical processes in the form of equivalent electric circuit parame- ters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal frame- work. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.1 Index Terms—Battery health, Bayesian learning, particle filter, prognostics, relevance vector machine, remaining useful life.
A Modeling Framework for Prognostic Decision Making and its Application to UAV Mission Planning
The goal of prognostic decision making (PDM) is to utilize information on anticipated system health changes in selecting future actions. One of the key challenges in PDM is finding a sufficiently expressive yet compact mathematical representation of the system for use with decision optimization algorithms. In this paper we describe a general modeling approach for a class of PDM problems with non-linear system degradation processes and uncertainties in state estimation, action effects, and future operating conditions. The approach is based on continuous Partially Observable Markov Decision Processes (POMDPs) used in conjunction with 'black box' system simulations. The proposed modeling framework can be cast into simpler representations, depending on which sources of uncertainty are being included. The approach is illustrated with a mission planning case study for an unmanned aerial vehicle (UAV). In the case study a PDM system is tasked with optimizing the vehicle route after an in-flight component fault is detected. A stochastic algorithm (based on particle filtering) is used for decision optimization, with a second, deterministic algorithm providing a performance evaluation baseline. Both algorithms utilize a UAV physics simulator for generating predictions of future vehicle states. Performance benchmarking is done on a set of mission scenarios of increasing complexity.
DXC'09 Framework
The DXC Framework is a collection of programs and APIs for running and evaluating diagnostic algorithms (DAs). It is complementary to system XML catalogs and datasets. A DXC Framework installer for Windows may also be downloaded. Competitors must submit DAs that can communicate with and be executed by the DXC Framework. It was designed primarily for the Diagnostic Competition and future competitions, however, it is lightweight and reusable in any context where data from one or more sources needs to be communicated to, processed by, and diagnosed by reasoners. It is distributed under the LGPL. Contents: APIs- The Java and C++ DXC APIs provide the classes and functionality necessary for data exchange. Scenario Execution Software - A scenario is an interval of time in which system data is sent to a diagnostic algorithm. StandaloneSDS is used to execute one scenario on its own and send the results to a file. ScenarioLoader is for executing a large collection of scenarios with one or more DAs in an automated fashion (as is necessary to perform benchmarking of algorithms). Example Diagnostic Algorithms - Two minimal example DAs are provided (along with source code) to demonstrate proper use of the APIs--one in C++, the other in Java. #Updates to the DXC Framework: This DxcFramework update (DxcFramework-1.1.zip) provides several important bugfixes and new features (see below). Your questions, feedback and suggestions have been very helpful in improving the software--please continue sending them. A DXC Framework installer update for Windows (DxcFramework-1.1.msi) may also be downloaded. DxcFramework 1.1 Changelog: * Added Evaluator. The Evaluator takes the output generated by scenario execution (stored in ./Scenarios/Results/) and calculates DA evaluation metrics. See README for more information. Cpu time and peak memory usage are currently not reported in Linux, this will change. * Added SystemCatalog class for basic XML catalog parsing and validation. * Included system catalogs and example scenarios for one system from each track. * New StandaloneSDS options: -t: specify wait time (in seconds) to keep listening for diagnoses after scenario end. -v: validate scenarios against corresponding system catalog (Not available in JavaSSDS). Catalogs must be placed in $DXC_HOME/SystemCatalogs, unless a different directory is specified in Dxc.cfg SYSTEMCAT_DIR. * Added JavaStandaloneSDS execution script, now just run "JavaStandaloneSDS" * Scenario timing changes. DA startup timeout now 20 seconds. Added 20 second post-scenario wait period to allow DAs to send final diagnosis and exit. * Scenario timing values can now be edited in Dxc.cfg (values are in milliseconds). Current values reflect our planned competition scenario conditions. Competitors will be notified of any changes to these values. #Evaluator Update Please follow these instructions to update the Evaluator in the Framework (see Evaluator.zip below): 1. Make sure your version of DXC Framework is the most recent (1.1). Source release users, ensure that Java code is built. 2. Move the copy of Evaluator.jar contained in $DXC_HOME/Bin to Evaluator.jar.backup. 3. Save new Evaluator.jar to $DXC_HOME/Bin. 4. Choose desired config file and save to $DXC_HOME (top-level directory), renaming to evalCfg.xml. Use evalCfg.xml.industrial for industrial track metrics/scoring, evalCfg.xml.synthetic for synthetic. 5. Run "Evaluator > results.txt" (or similar) at the command line. Evaluator will calculate metrics and scores for results files it finds in $DXC_HOME/Scenarios/Results.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.