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190 results for “knowledge base”
Effect of a Mobile-Based Hydration Tracking Program on Knowledge, Attitudes, and Behaviors in Older Adults
ClinicalTrials.gov study NCT07043933. IPD Sharing: YES. Countries: 0. Publications: 0.
Knowledge-Based System to Support Plug Load Management
Electrical plug loads comprise an increasingly larger share of building energy consumption as improvements have been made to Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems. It is anticipated that plug loads will account for a significant portion of the energy consumption of Sustainability Base, a recently constructed high-performance office building at NASA Ames Research Center. Consequently, monitoring plug loads will be critical to achieve energy efficient operations. In this paper we describe the development of a knowledge-based system to analyze data collected from a plug load management system that allows for metering and control of individual loads. Since Sustainability Base was not yet occupied at the time of this investigation, the study was conducted in another building on the Ames campus to prototype the system. The paper focuses on the knowledge engineering and verification of a modular software system that promotes efficient use of office building plug loads. The knowledge-based system generates summary usage reports and alerts building personnel of malfunctioning equipment and unexpected plug load consumption. The system is planned to be applied to Sustainability Base and is expected to identify malfunctioning loads and reduce building energy consumption.
Knowledge Management for Large Scale Condition Based Maintenance
This presentation will review the use of knowledge management in the development and support of Condition Based Maintenance (CBM) systems for complex systems with particular emphasis on the experience of the development of the Fault Model for large commercial aircraft. The presentation is divided into four sections: 1. Review of experience of building fault models and Central Maintenance Computer for large commercial aircraft. 2. Review of the key functions and usage scenarios for a typical CBM Knowledge Management System 3. Identification of criteria for evaluation of implementation alternatives The presentation will conclude with a short discussion of future directions for CBM Knowledge Management Systems. **Speaker: Tim Felke, Honeywell** Tim Felke joined Honeywell in 1984 as a control systems analyst and was the manager for their Systems Analysis and Engineering Sciences department for several years. He was a principle author of the proposal for the Central Maintenance Computer for the Boeing 777 and then was a leader in its development. Since then he has been an Engineering Fellow for the diagnostic and knowledge management functions of the Aircraft Diagnostic and Maintenance Systems group. In this work he has published several papers and is the principle inventor or significant contributor on nearly a dozen patents. He holds a BS in Electrical Engineering from Arizona State University.
A knowledge-based system approach for sensor fault modeling, detection and mitigation
Sensors are vital components for control and advanced health management techniques. However, sensors continue to be considered the weak link in many engineering applications since often they are less reli- able than the system they are observing. This is in part due to the sensors’ operating principles and their susceptibility to interference from the environment. Detecting and mitigating sensor failure modes are becoming increasingly important in more complex and safety-critical applications. This paper reports on different techniques for sensor fault detection, disambiguation, and mitigation. It presents an expert system that uses a combination of object-oriented modeling, rules, and semantic networks to deal with the most common sensor faults, such as bias, drift, scaling, and dropout, as well as system faults. The paper also describes a sensor correction module that is based on fault parameters extraction (for bias, drift, and scaling fault modes) as well as utilizing partial redundancy for dropout sensor fault modes). The knowledge-based system was derived from the results obtained in a previously deployed Neural Network (NN) application for fault detection and disambiguation. Results are illustrated on an electromechanical actuator application where the system faults are jam and spalling. In addition to the functions implemented in the previous work, system fault detection under sensor failure was also modeled. The paper includes a sensitivity analysis that compares the results previously obtained with the NN. It concludes with a discussion of similarities and differences between the two approaches and how the knowledge based system provides additional functionality compared to the NN implementation.
Prior Knowledge-based Approach for Associating Emerging Contaminants with Effects in Fish Exposed In Situ: A Case Study in the St. Croix River Basin, MN, WI, USA.
GEO Series GSE81263. Pimephales promelas. 40 samples. Type: Expression profiling by array.
Artifact for "Efficient Construction of Practical Python Call Graphs with Entity Knowledge Base"
<p>This is the artifact for the paper titled "Efficient Construction of Practical Python Call Graphs with Entity Knowledge Base"</p>
Dataset related to article "Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors"
<p>This record contains raw data related to article "Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors"</p><p><strong> Abstract</strong></p><p>Background: To analyze RapidPlan knowledge-based models for DVH estimation of organs at risk from breast cancer VMAT plans presenting arc sectors en-face to the breast with zero dose rate, feature imposed during the optimization phase (avoidance sectors AS).</p><p>Methods: CT datasets of twenty left breast patients in deep-inspiration breath-hold were selected. Two VMAT plans, PartArc and AvoidArc, were manually generated with double arcs from ~ 300 to ~ 160°, with the second having an AS en-face to the breast to avoid contralateral breast and lung direct irradiation. Two RapidPlan models were generated from the two plan sets. The two models were evaluated in a closed loop to assess the model performance on plans where the AS were selected or not in the optimization.</p><p>Results: The PartArc plans model estimated DVHs comparable with the original plans. The AvoidArc plans model estimated a DVH pattern with two steps for the contralateral structures when the plan does not contain the AS selected in the optimization phase. This feature produced mean doses of the contralateral breast, averaged over all patients, of 0.4 ± 0.1 Gy, 0.6 ± 0.2 Gy, and 1.1 ± 0.2 Gy for the AvoidArc plan, AvoidArc model estimation, RapidPlan generated plan, respectively. The same figures for the contralateral lung were 0.3 ± 0.1 Gy, 1.6 ± 0.6 Gy, and 1.2 ± 0.5 Gy. The reason was found in the possible incorrect information extracted from the model training plans due to the lack of knowledge about the AS. Conversely, in the case of plans with AS set in the optimization generated with the same AvoidArc model, the estimated and resulting DVHs were comparable. Whenever the AvoidArc model was used to generate DVH estimation for a plan with AS, while the optimization was made on the plan without the AS, the optimizer evidentiated the limitation of a minimum dose rate of 0.2 MU/°, resulting in an increased dose to the contralateral structures respect to the estimation.</p><p>Conclusions: The RapidPlan models for breast planning with VMAT can properly estimate organ at risk DVH. Attention has to be paid to the plan selection and usage for model training in the presence of avoidance sectors.</p><p> </p>
Interview Fragenkatalog: Unlocking AI-based Knowledge Management Potential for SMEs: Exploring Semantic Search Adoption
Open the record for dataset details and reuse information.
Knowledge of dental postgraduate students on evidence-based dentistry and research methodology. An international survey
<p>Dataset for the analyses of the paper</p>
Pediatric Cancer Knowledge Base
The PeCan platform presents curated pediatric cancer genomics data including variants, mutational signatures, and gene expression data in addition to histological slide images from ~9000 hematological, CNS, and non-CNS solid tumor patient samples. Data can be explored via a series of data facets containing both retrospective and prospective study cohorts from St. Jude Children's Research Hospital and other trusted institutions and research centers around the world.
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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.