Radar Jamming Effect Analysis Based on Bayesian Inference Network with Adaptive Clustering

2021 ◽  
pp. 1-1
Author(s):  
Zelong Wang ◽  
Tingpeng Li ◽  
Jiying Liu
Keyword(s):  
Bayesian Inference ◽  
Effect Analysis ◽  
Adaptive Clustering ◽  
Inference Network ◽  
Jamming Effect

Adaptive Damage Detection Using Tunable Piezoelectric Admittance Sensor and Intelligent Inference

2014 ◽  
Author(s):  
K. Zhou ◽  
Q. Shuai ◽  
J. Tang
Keyword(s):  
Bayesian Inference ◽  
Damage Detection ◽  
Structural Model ◽  
Experimental Studies ◽  
Frequency Measurement ◽  
Measurement Information ◽  
Accurate Identification ◽  
Measurement Capability ◽  
Uncertainty Measurement ◽  
Inference Network

The piezoelectric impedance/admittance-based damage detection has been recognized to be sensitive to small-sized damage due to its high frequency measurement capability. Recently, a new class of admittance-based damage detection schemes has been proposed, in which the piezoelectric transducer is integrated with a tunable inductive circuitry. The present research focuses on exploiting the tunable nature of the piezoelectric admittance sensor for the effective identification of damage. In particular, we incorporate the Bayesian inference network into the damage detection process which can intelligently guide the accurate identification of damage location and severity by taking full advantage of the baseline model and measurement as well as the online measurement. As the tunable sensor can provide greatly enriched measurement information, the Bayesian inference can adequately utilize such information and furthermore directly and continuously update the structural model until the model prediction matches with the measurement results. This new approach takes into account the model uncertainty, measurement error, and incompleteness of measurements. Extensive numerical analyses and experimental studies are carried out on a panel structure for methodology demonstration and validation.

scholarly journals Implementing Relevance Feedback in Ligand-Based Virtual Screening Using Bayesian Inference Network

2011 ◽  
Vol 16 (9) ◽  
pp. 1081-1088 ◽  
Author(s):  
Ammar Abdo ◽  
Naomie Salim ◽  
Ali Ahmed
Keyword(s):  
Bayesian Inference ◽  
Virtual Screening ◽  
Bayesian Network ◽  
Relevance Feedback ◽  
Data Sets ◽  
Reference Structure ◽  
Feedback Information ◽  
Screening Experiments ◽  
Inference Network ◽  
Report Data

Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.

scholarly journals Ruling In and Ruling Out COVID-19: Computing SARS-CoV-2 Infection Risk From Symptoms, Imaging and Test Data.

2020 ◽  
Author(s):  
Chistopher D'Ambrosia ◽  
Henrik Christensen ◽  
Eliah Aronoff-Spencer
Keyword(s):  
Bayesian Inference ◽  
Test Data ◽  
Medical Center ◽  
Metric Learning ◽  
Evaluation Process ◽  
Infection Risk ◽  
Molecular Diagnostic ◽  
Diagnostic Challenge ◽  
Diagnostic Uncertainty ◽  
Inference Network

Background: Assigning meaningful probabilities of SARS CoV2 infection risk presents a diagnostic challenge across the continuum of care. Methods: We integrated patient symptom and test data using machine learning and Bayesian inference to quantify individual patient risk of SARS CoV 2 infection. We trained models with 100,000 simulated patient profiles based on thirteen symptoms, estimated local prevalence, imaging, and molecular diagnostic performance from published reports. We tested these models with consecutive patients who presented with a COVID 19 compatible illness at the University of California San Diego Medical Center over 14 days starting in March 2020. Results: We included 55 consecutive patients with fever (78%) or cough (77%) presenting for ambulatory (n=11) or hospital care (n=44). 51% (n=28) were female, 49% were age <60. Common comorbidities included diabetes (22%), hypertension (27%), cancer (16%) and cardiovascular disease (13%). 69% of these (n=38) were RT-PCR confirmed positive for SARS CoV2 infection, 11 had repeated negative nucleic acid testing and an alternate diagnosis. Bayesian inference network, distance metric learning, and ensemble models discriminated between patients with SARS CoV2 infection and alternate diagnoses with sensitivities of 81.6 to 84.2%, specificities of 58.8 to 70.6%, and accuracies of 61.4 to 71.8%. After integrating imaging and laboratory test statistics with the predictions of the Bayesian inference network, changes in diagnostic uncertainty at each step in the simulated clinical evaluation process were highly sensitive to location, symptom, and diagnostic test choices. Conclusions: Decision support models that incorporate symptoms and available test results can help providers diagnose SARS CoV2 infection in real world settings.

scholarly journals Computing SARS-CoV-2 Infection Risk From Symptoms, Imaging, and Test Data: Diagnostic Model Development (Preprint)

2020 ◽  
Author(s):  
Christopher D'Ambrosia ◽  
Henrik Christensen ◽  
Eliah Aronoff-Spencer
Keyword(s):  
Machine Learning ◽  
Bayesian Inference ◽  
Test Data ◽  
Diagnostic Performance ◽  
Medical Center ◽  
Infection Risk ◽  
Molecular Diagnostic ◽  
Diagnostic Model ◽  
Diagnostic Challenge ◽  
Inference Network

BACKGROUND Assigning meaningful probabilities of SARS-CoV-2 infection risk presents a diagnostic challenge across the continuum of care. OBJECTIVE The aim of this study was to develop and clinically validate an adaptable, personalized diagnostic model to assist clinicians in ruling in and ruling out COVID-19 in potential patients. We compared the diagnostic performance of probabilistic, graphical, and machine learning models against a previously published benchmark model. METHODS We integrated patient symptoms and test data using machine learning and Bayesian inference to quantify individual patient risk of SARS-CoV-2 infection. We trained models with 100,000 simulated patient profiles based on 13 symptoms and estimated local prevalence, imaging, and molecular diagnostic performance from published reports. We tested these models with consecutive patients who presented with a COVID-19–compatible illness at the University of California San Diego Medical Center over the course of 14 days starting in March 2020. RESULTS We included 55 consecutive patients with fever (n=43, 78%) or cough (n=42, 77%) presenting for ambulatory (n=11, 20%) or hospital care (n=44, 80%). In total, 51% (n=28) were female and 49% (n=27) were aged &lt;60 years. Common comorbidities included diabetes (n=12, 22%), hypertension (n=15, 27%), cancer (n=9, 16%), and cardiovascular disease (n=7, 13%). Of these, 69% (n=38) were confirmed via reverse transcription-polymerase chain reaction (RT-PCR) to be positive for SARS-CoV-2 infection, and 20% (n=11) had repeated negative nucleic acid testing and an alternate diagnosis. Bayesian inference network, distance metric learning, and ensemble models discriminated between patients with SARS-CoV-2 infection and alternate diagnoses with sensitivities of 81.6%-84.2%, specificities of 58.8%-70.6%, and accuracies of 61.4%-71.8%. After integrating imaging and laboratory test statistics with the predictions of the Bayesian inference network, changes in diagnostic uncertainty at each step in the simulated clinical evaluation process were highly sensitive to location, symptom, and diagnostic test choices. CONCLUSIONS Decision support models that incorporate symptoms and available test results can help providers diagnose SARS-CoV-2 infection in real-world settings.

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