scholarly journals Automated Diagnostic Model Based on Heart Tissue Isoline Map Analysis

2017 ◽  
Author(s):  
Olga Senyukova ◽  
Danuta Brotikovskaya ◽  
Svetlana Gorokhova ◽  
Ekaterina Tebenkova
Keyword(s):  
Heart Tissue ◽  
Diagnostic Model ◽  
Model Based ◽  
Map Analysis

Automated Diagnostic Model Based on Isoline Map Analysis of Myocardial Tissue Structure

2019 ◽  
pp. 221-238
Author(s):  
Olga V. Senyukova ◽  
Danuta S. Brotikovskaya ◽  
Svetlana G. Gorokhova ◽  
Ekaterina S. Tebenkova
Keyword(s):  
Tissue Structure ◽  
Diagnostic Model ◽  
Myocardial Tissue ◽  
Model Based ◽  
Map Analysis

CONSTRUCTION OF THE DIAGNOSTIC MODEL BASED ON COMBINING SPECTRAL CHARACTERISTICS OF NONLINEAR DYNAMIC OBJECTS

2020 ◽  
Vol 3 (1) ◽  
pp. 431-442
Author(s):  
Hanna Fedorova ◽  
Oleksandr Fomin ◽  
Oleksandr Ruban ◽  
Vitaliy Pavlenko
Keyword(s):  
Nonlinear Dynamic ◽  
Spectral Characteristics ◽  
Diagnostic Model ◽  
Model Based ◽  
Dynamic Objects

scholarly journals A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer Models

2008 ◽  
Vol 18 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Antoni Ligęza ◽  
Jan Kościelny
Keyword(s):  
Fault Diagnosis ◽  
Simultaneous Occurrence ◽  
Diagnostic Model ◽  
Rule Based ◽  
New Approach ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Model Based ◽  
Multiple Fault Diagnosis ◽  
System Variables

A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer ModelsThe diagnosis of multiple faults is significantly more difficult than singular fault diagnosis. However, in realistic industrial systems the possibility of simultaneous occurrence of multiple faults must be taken into account. This paper investigates some of the limitations of the diagnostic model based on the simple binary diagnostic matrix in the case of multiple faults. Several possible interpretations of the diagnostic matrix with rule-based systems are provided and analyzed. A proposal of an extension of the basic, single-level model based on diagnostic matrices to a two-level one, founded on causal analysis and incorporating an OR and an AND matrix is put forward. An approach to the diagnosis of multiple faults based on inconsistency analysis is outlined, and a refinement procedure using a qualitative model of dependencies among system variables is sketched out.

scholarly journals Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1350 ◽  
Author(s):  
Chen ◽  
Wu ◽  
Wu ◽  
Xiong ◽  
Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Early Warning ◽  
Flight Control ◽  
Data Driven ◽  
Diagnostic Model ◽  
Flight Control System ◽  
Model Based ◽  
On Line

The unmanned aerial vehicle (UAV), which is a typical multi-sensor closed-loop flight control system, has the properties of multivariable, time-varying, strong coupling, and nonlinearity. Therefore, it is very difficult to obtain an accurate mathematical diagnostic model based on the traditional model-based method; this paper proposes a UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning. In order to realize the rapid on-line fault detection of the rotor UAV flight system and solve the problems of over-fitting, limited generalization, and long training time in the traditional shallow neural network for sensor fault diagnosis, a comprehensive fault diagnosis method based on deep belief network (DBN) is proposed. Using the DBN to replace the shallow neural network, a large amount of off-line historical sample data obtained from the rotor UAV are trained to obtain the optimal DBN network parameters and complete the on-line intelligent diagnosis to achieve the goal of early warning as possible as quickly. In the end, the two common faults of the UAV sensor, namely the stuck fault and the constant deviation fault, are simulated and compared with the back propagation (BP) neural network model represented by the shallow neural network to verify the effectiveness of the proposed method in the paper.

scholarly journals Immune profiling of plasma-derived extracellular vesicles identifies Parkinson disease

2020 ◽  
Vol 7 (6) ◽  
pp. e866 ◽  
Author(s):  
Elena Vacchi ◽  
Jacopo Burrello ◽  
Dario Di Silvestre ◽  
Alessio Burrello ◽  
Sara Bolis ◽  
...  
Keyword(s):  
Parkinson Disease ◽  
Diagnostic Performance ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Diagnostic Model ◽  
Surface Markers ◽  
Model Based ◽  
Immune Profiling

ObjectiveTo develop a diagnostic model based on plasma-derived extracellular vesicle (EV) subpopulations in Parkinson disease (PD) and atypical parkinsonism (AP), we applied an innovative flow cytometric multiplex bead-based platform.MethodsPlasma-derived EVs were isolated from PD, matched healthy controls, multiple system atrophy (MSA), and AP with tauopathies (AP-Tau). The expression levels of 37 EV surface markers were measured by flow cytometry and correlated with clinical scales. A diagnostic model based on EV surface markers expression was built via supervised machine learning algorithms and validated in an external cohort.ResultsDistinctive pools of EV surface markers related to inflammatory and immune cells stratified patients according to the clinical diagnosis. PD and MSA displayed a greater pool of overexpressed immune markers, suggesting a different immune dysregulation in PD and MSA vs AP-Tau. The receiver operating characteristic curve analysis of a compound EV marker showed optimal diagnostic performance for PD (area under the curve [AUC] 0.908; sensitivity 96.3%, specificity 78.9%) and MSA (AUC 0.974; sensitivity 100%, specificity 94.7%) and good accuracy for AP-Tau (AUC 0.718; sensitivity 77.8%, specificity 89.5%). A diagnostic model based on EV marker expression correctly classified 88.9% of patients with reliable diagnostic performance after internal and external validations.ConclusionsImmune profiling of plasmatic EVs represents a crucial step toward the identification of biomarkers of disease for PD and AP.

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