scholarly journals Examination of informativeness of diagnoses expressed with multiple-valued logic

2018 ◽  
Vol 67 (2) ◽  
pp. 169-178
Author(s):  
Stanisław Duer ◽  
Dariusz Bernatowicz ◽  
Paweł Wrzesień ◽  
Radosław Duer
Keyword(s):  
Artificial Intelligence ◽  
Wind Turbine ◽  
Diagnostic Test ◽  
Diagnostic System ◽  
Diagnostic Information ◽  
Technical Diagnostics ◽  
State Table ◽  
Object State ◽  
Multiple Valued Logic ◽  
Turbine Equipment

This paper presents the essence of an examination of informativeness in the diagnostic information outputs expressed with multiple-valued logic. The diagnostic test required for the examination was completed on wind turbine equipment. The examination included a constant set of determined diagnostic output values. The DIAG 2 diagnostic system was used for the examination and the diagnostic test. DIAG 2 is a smart diagnostic system capable of any inference k of the set {k = 2, 3, 4}. The examination results were expressed in an Object State Table, separately for each k-valued logic of inference tested. Keywords: technical diagnostics, diagnostic inference, multiple-valued logic, artificial intelligence

scholarly journals Diagnosing of a complex technical object in four-valued logic

2018 ◽  
Vol 67 (1) ◽  
pp. 33-42
Author(s):  
Stanisław Duer ◽  
Dariusz Bernatowicz ◽  
Paweł Wrzesień ◽  
Radosław Duer
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic System ◽  
Technical Diagnostics ◽  
Technical Object ◽  
Multiple Valued Logic ◽  
Logic Testing ◽  
Signal Features ◽  
Turbine Equipment ◽  
Theoretical Foundations

This paper presents the essence of an investigation of a complex technical object with the use of four-valued logic. To this end, an intelligent diagnostic system (DIAG 2) is described. A special feature of this system was its capability of inferring k at {k = 4, 3, 2}, in which case the logic {k = 4} is applied. An important part of this work was to present the theoretical foundations describing the essence of inference in the four-valued logic contemplated. It was also pointed out that the basis for classification of states in the multiple-valued logic of the diagnostic system (DIAG 2) was the permissible interval of changes in the values of diagnostic signal features. Four-valued logic testing was applied to a system of wind turbine equipment. Keywords: technical diagnostics, diagnostic inference, multiple-valued logic, artificial intelligence

scholarly journals Knowledge base of wind farm devices in the computer expert system

2018 ◽  
Vol 67 (2) ◽  
pp. 179-190
Author(s):  
Radosław Duer ◽  
Paweł Wrzesień ◽  
Stanisław Duer ◽  
Dariusz Bernatowicz
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Knowledge Base ◽  
Wind Farm ◽  
Diagnostic Information ◽  
Diagnostic Model ◽  
Technical Diagnostics ◽  
Wind Power Plant ◽  
Power Plant Equipment ◽  
Plant Equipment

The article presents the problems of determining diagnostic information for the needs of testing the state of wind farm equipment. To this end, the essence of developing a functional and diagnostic model on the example of wind power plant equipment has been presented and described. Based on the developed model of the examined object, diagnostic information was determined in the form of a set of basic elements and a set of diagnostic signals, which are developed by the designated j-elements in the i-functional units of the object. The article presents a description of the process of building a knowledge base for an expert system. Keywords: technical diagnostics, diagnostic reasoning, multivalent logic, artificial intelligence

scholarly journals Knowledge base for wind farm devices in the computer expert system

2020 ◽  
Vol 68 (4) ◽  
pp. 107-118
Author(s):  
Radosław Duer ◽  
Stanisław Duer ◽  
Lech Drawski
Keyword(s):  
Artificial Intelligence ◽  
Renewable Energy ◽  
Expert System ◽  
Knowledge Base ◽  
Wind Farm ◽  
Expert Knowledge ◽  
Diagnostic Information ◽  
Diagnostic Model ◽  
Technical Diagnostics ◽  
Model Based

The article presents the issue of determining diagnostic information for the needs of testing the condition of wind farm equipment. To this end, the essence of the structure of an intelligent expert system was presented and described. The structure of the tested object is shown in the form of a functional and diagnostic model. Based on the developed model of the examined object, diagnostic information was determined in the form of a set of basic elements and a set of diagnostic signals, which are later used in the construction of an expert knowledge base. The expert knowledge base is determined by sets of facts and rules applied. An important part of this article is description of the structure of the expert system and the expert knowledge base used in it. Keywords: wind farm, renewable energy, technical diagnostics, diagnostic inference, artificial intelligence

scholarly journals Information with diagnostic technical object used to create experts’ knowledge base

2017 ◽  
Vol 66 (2) ◽  
pp. 91-106
Author(s):  
Radosław Duer ◽  
Stanisław Duer
Keyword(s):  
Diagnostic System ◽  
Knowledge Bases ◽  
Diagnostic Information ◽  
Diagnostic Model ◽  
Technical Diagnostics ◽  
Wind Power Plant ◽  
Technical Object ◽  
Pro Bono ◽  
Definition Of ◽  
Functional Diagnostic

The article presents the problem of determining diagnostic information for the purpose of testing a complex technical object. To this end, the essence of the development of the functional-diagnostic model was presented and described on the example of the wind power plant. Based on the developed model of the object, diagnostic information has been determined, which consists of two components: a set of basic elements and a set of diagnostic signals, which are worked out by the designated elements in the functional groups of the object. An important aspect in this paper is the shift in the pro bono definition of a set of diagnostic signals. Knowledge of the set of diagnostic signals and their nominal (master) signals is the basis for determining the range of changes in diagnostic signals that are the basis for the diagnosis of an object using the intelligent diagnostic system (DIAG 2) or using knowledge bases for expert systems. Keywords: technical diagnostics, diagnostic reasoning, multivalent logic, artificial intelligence

scholarly journals Diagnostics of low-capacity solar power station equipment with 2- and 3-valued logic

2018 ◽  
Vol 67 (3) ◽  
pp. 185-195
Author(s):  
Stanisław Duer ◽  
Paweł Wrzesień ◽  
Radosław Duer ◽  
Dariusz Bernatowicz
Keyword(s):  
Low Power ◽  
Solar Power ◽  
Diagnostic System ◽  
Diagnostic Model ◽  
Technical Diagnostics ◽  
Power Station ◽  
Research Issues ◽  
Multiple Valued Logic ◽  
Differential Distance ◽  
Solar Power Station

The paper outlines research issues relating to 2- and 3-valued logic diagnoses developed with the diagnostic system (DIA G 2) for the equipment installed at a low-capacity solar power station. The presentation is facilitated with an overview and technical description of the functional and diagnostic model of the low-power solar power station. A model of the low-power solar power station (the tested facility, a.k.a. the test object) was developed, from which a set of basic elements and a set of diagnostic outputs were determined and developed by the number of functional elements j of j. The work also provides a short description of the smart diagnostic system (DIA G 2) used for the tests shown herein. (DIA G 2) is a proprietary work. The diagnostic program of (DIA G 2) operates by comparing a set of actual diagnostic output vectors to their master vectors. The output of the comparison are elementary divergence metrics of the diagnostic output vectors determined by a neural network. The elementary divergence metrics include differential distance metrics which serve as the inputs for (DIA G 2) to deduct the state (condition) of the basic elements of the tested facility. Keywords: technical diagnostics, diagnostic inference, multiple-valued logic, artificial intelligence.

ID: 3526637 ARTIFICIAL INTELLIGENCE-ASSISTED DIAGNOSTIC SYSTEM FOR NARROW-BAND IMAGING FOR COLORECTAL LESIONS.

2021 ◽  
Vol 93 (6) ◽  
pp. AB198-AB199
Author(s):  
Masashi Misawa ◽  
Shinei Kudo ◽  
Yuichi Mori ◽  
Misaki Ishiyama ◽  
Yosuke Minegishi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Diagnostic System

scholarly journals Automated Detection of Laryngeal Carcinoma in Laryngoscopic Images from a Multicenter Database using a Convolutional Neural Network

2021 ◽  
Author(s):  
Peikai Yan ◽  
Shaohua Li ◽  
Zhou Zhou ◽  
Qian Liu ◽  
Jiahui Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Laryngeal Carcinoma ◽  
Detection System ◽  
Tertiary Care ◽  
Diagnostic System ◽  
Intelligence System ◽  
Testing Dataset ◽  
Malignant Group ◽  
Automatic Detection System ◽  
Tertiary Care Centers

OBJECTIVE Little is known about the efficacy of using artificial intelligence to identify laryngeal carcinoma from images of vocal lesions taken in different hospitals with multiple laryngoscope systems. This multicenter study was aimed to establish an artificial intelligence system and provide a reliable auxiliary tool to screen for laryngeal carcinoma. Study Design: Multicentre case-control study Setting: Six tertiary care centers Participants: The laryngoscopy images were collected from 2179 patients with vocal lesions. Outcome Measures: An automatic detection system of laryngeal carcinoma was established based on Faster R-CNN, which was used to distinguish vocal malignant and benign lesions in 2179 laryngoscopy images acquired from 6 hospitals with 5 types of laryngoscopy systems. Pathology was the gold standard to identify malignant and benign vocal lesions. Results: Among 89 cases of the malignant group, the classifier was able to evaluate the laryngeal carcinoma in 66 patients (74.16%, sensitivity), while the classifier was able to assess the benign laryngeal lesion in 503 cases among 640 cases of the benign group (78.59%, specificity). Furthermore, the CNN-based classifier achieved an overall accuracy of 78.05% with a 95.63% negative prediction for the testing dataset. Conclusion: This automatic diagnostic system has the potential to assist clinical laryngeal carcinoma diagnosis, which may improve and standardize the diagnostic capacity of endoscopists using different laryngoscopes.

scholarly journals Artificial intelligence assisted electrocardiography for early diagnosis of thyrotoxic periodic paralysis

2021 ◽  
Author(s):  
Chin Lin ◽  
Chin-Sheng Lin ◽  
Ding-Jie Lee ◽  
Chia-Cheng Lee ◽  
Sy-Jou Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Early Diagnosis ◽  
Validation Cohort ◽  
Laboratory Data ◽  
Diagnostic System ◽  
Periodic Paralysis ◽  
Medical Emergency ◽  
Diagnostic Process ◽  
Routine Laboratory ◽  
Thyrotoxic Periodic Paralysis

Abstract CONTEXT Thyrotoxic periodic paralysis (TPP) characterized by acute weakness, hypokalemia and hyperthyroidism is a medical emergency with a great challenge in early diagnosis since most TPP patients do not have overt symptoms. OBJECTIVE To assess artificial intelligence (AI)-assisted electrocardiography (ECG) combined with routine laboratory data in the early diagnosis of TPP. METHODS A deep learning model (DLM) based on ECG12Net, an 82-layer convolutional neural network, was constructed to detect hypokalemia and hyperthyroidism. The development cohort consisted of 39 ECGs from patients with TPP and 502 ECGs of hypokalemic control; the validation cohort consisted of 11 ECGs of TPP and 36 ECGs of non-TPP with weakness. The AI-ECG based TPP diagnostic process was then consecutively evaluated in 22 male patients with TTP-like features. RESULTS In the validation cohort, the DLM-based ECG system detected all cases of hypokalemia in TPP patients with a mean absolute error of 0.26 mEq/L and diagnosed TPP with an area under curve (AUC) of ~80%, surpassing the best standard ECG parameter (AUC=0.7285 for the QR interval). Combining the AI predictions with the estimated glomerular filtration rate (eGFR) and serum chloride (Cl -) boosted the diagnostic accuracy of the algorithm to AUC 0.986. In the prospective study, the integrated AI and routine laboratory diagnostic system had a PPV of 100% and F-measure 87.5%. CONCLUSIONS An AI-ECG system reliably identifies hypokalemia in patients with paralysis and integration with routine blood chemistries provides valuable decision support for the early diagnosis of TPP.

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