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Bien, Z., M. J. Youn, M. J. Chung, J. H. Kim, B. C. Moon, and B. K. Kim. "A Fault Diagnostic Algorithm for Fault Tolerant Control Systems." IFAC Proceedings Volumes 19, no. 13 (November 1986): 119–22. http://dx.doi.org/10.1016/s1474-6670(17)59526-9.
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Park, Seong Ho. "Diagnostic Case-Control versus Diagnostic Cohort Studies for Clinical Validation of Artificial Intelligence Algorithm Performance." Radiology 290, no. 1 (January 2019): 272–73. http://dx.doi.org/10.1148/radiol.2018182294.
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Zheng, Chanjin, and Chun Wang. "Application of Binary Searching for Item Exposure Control in Cognitive Diagnostic Computerized Adaptive Testing." Applied Psychological Measurement 41, no. 7 (May 11, 2017): 561–76. http://dx.doi.org/10.1177/0146621617707509.
Повний текст джерелаАнотація:
Cognitive diagnosis has emerged as a new generation of testing theory for educational assessment after the item response theory (IRT). One distinct feature of cognitive diagnostic models (CDMs) is that they assume the latent trait to be discrete instead of continuous as in IRT. From this perspective, cognitive diagnosis bears a close resemblance to searching problems in computer science and, similarly, item selection problem in cognitive diagnostic computerized adaptive testing (CD-CAT) can be considered as a dynamic searching problem. Previously, item selection algorithms in CD-CAT were developed from information indices in information science and attempted to achieve a balance among several objectives by assigning different weights. As a result, they suffered from low efficiency from a tug-of-war competition among multiple goals in item selection and, at the same time, put an undue responsibility of assigning the weights for these goals by trial and error on users. Based on the searching problem perspective on CD-CAT, this article adapts the binary searching algorithm, one of the most well-known searching algorithms in searching problems, to item selection in CD-CAT. The two new methods, the stratified dynamic binary searching (SDBS) algorithm for fixed-length CD-CAT and the dynamic binary searching (DBS) algorithm for variable-length CD-CAT, can achieve multiple goals without any of the aforementioned issues. The simulation studies indicate their performances are comparable or superior to the previous methods.
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Fang, Yujie, Ting Kang, Yang Yang, Yonghong Zi, and Xiong Lu. "Dark-Lumen Magnetic Resonance Image Based on Artificial Intelligence Algorithm in Differential Diagnosis of Colon Cancer." Computational Intelligence and Neuroscience 2022 (March 27, 2022): 1–8. http://dx.doi.org/10.1155/2022/4217573.
Повний текст джерелаАнотація:
This research was aimed o investigate the application value and diagnostic effect of dark-lumen magnetic resonance imaging (dark-lumen MRI) based on artificial intelligence algorithm on colon cancer. A total of 98 patients with ulcerated colon cancer were selected as the study subjects. All patients underwent colonic endoscopy. The patients were divided into algorithm group (artificial intelligence algorithm processing image group) and control group (conventional method processing image group) according to different dark-lumen MRI processing methods. The detection efficiency of colon cancer was compared between the two groups. It showed that the diagnostic effect of dark-lumen MRI based on artificial intelligence algorithm was significant. The apparent diffusion coefficient (ADC) in the control group was 0.92 ± 0.14 mm2/s (minimum: 0.74, maximum: 1.30), ADC in the algorithm group was 1.55 ± 0.31 mm2/s (minimum: 1.22, maximum: 2.42). The ADC of patients in algorithm group was significantly higher than that of patients in control group, with statistical difference (t = 7.827, P < 0.001). The correct number of cases was 46 and the diagnostic error number was 3 in algorithm group, with accuracy of 93%. The correct number of cases was 41 and the diagnostic error number was 8 in control group, with accuracy of 83%. In comparison, the correct rate was 10% higher in algorithm group, indicating that the diagnostic effect was better in algorithm group. The mean value of invasion depth was 10.42 in the algorithm group and 5.27 in the control group, indicating that the algorithm group was more accurate in the judgment of invasion depth, had a good prospect of clinical application, and had guiding significance for the diagnosis of colon cancer.
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Sasayama, Takashi, Kazuhiro Tanaka, Masahiro Maeyama, Satoshi Nakamizo, Hirotomo Tanaka, Masamitsu Nishihara, Takanori Hirose, Yuichi Fujita та Eiji Kohmura. "PATH-44. MULTIPLE BIOMARKER ALGORITHM BASED ON CXCL13, IL-10, IL-2 RECEPTOR, AND β2-MICROGLOBULIN IN CEREBROSPINAL FLUID TO DIAGNOSE CENTRAL NERVOUS SYSTEM LYMPHOMA". Neuro-Oncology 21, Supplement_6 (листопад 2019): vi153. http://dx.doi.org/10.1093/neuonc/noz175.640.
Повний текст джерелаАнотація:
Abstract BACKGROUND Brain biopsy is the gold standard for the diagnosis of primary central nervous system lymphoma (PCNSL). However, the biopsy procedure has a risk of complications, such as hemorrhage and seizure. We have reported that cerebrospinal fluid (CSF) interleukin-10 (IL-10), soluble IL-2 receptor (sIL-2R), and β2-microglobulin (β2-MG) are the useful diagnostic biomarkers for PCNSL. Recently, the C-X-C motif chemokine ligand 13 (CXCL13) has been reported to be another useful biomarker for the PCNSL. The aim of this study was to validate the diagnostic performance of the CSF CXCL13 and diagnostic algorithm based on the combination of these biomarkers. METHODS We retrospectively examined the CSF CXCL13 concentration, as well as IL-10, sIL-2R, and β2-MG, in a case-control study (n=230). We used logistic regression analyses to create diagnostic algorithms based on these CSF biomarkers. To examine the utility of the multivariate diagnostic algorithm, we conducted the prospective study and evaluated the accuracy of this algorithm. RESULTS In case-control study, we have demonstrated that CSF CXCL13 levels were significantly higher in the patients with PCNSL (area under the curve (AUC)=0.981). A total of 84 patients were enrolled in the prospective study. The multivariate diagnostic algorithm using CSF levels of CXCL13, IL-10, sIL-2R, and β2-MG demonstrated excellent diagnostic performance: positive predictive value was 89% and negative predictive value was 100%. Four markers’ combination (CXCL13 + IL-10 + β2-MG + sIL-2R) had the highest AUC (AUC=0.998). The misdiagnosis cases of the algorithm were only 3 cases; CNS Sjögren’s syndrome, histiocytic sarcoma, and glioblastoma. In addition, CSF CXCL13 levels were prognostic biomarkers in PCNSL patients. CONCLUSIONS Our study suggests that the algorithm based on 4 CSF biomarkers had excellent diagnostic performance in CNS lymphoma. However, this algorithm should be further validated in prospective cohort studies with larger numbers of patients.
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Matveev, Stanislav A., Evgeny B. Korotkov, Yuri A. Zhukov, Nikita S. Slobodzian, Mikhail I. Nadezhin, Andrei V. Gorbunov, and Leonid T. Tanklevskiy. "Diagnostic and Monitoring System for Technical Condition of Electromechanical Section of Thermal Control Systems in Spacecraft." International Journal of Mathematical, Engineering and Management Sciences 5, no. 1 (November 1, 2019): 181–92. http://dx.doi.org/10.33889/ijmems.2020.5.1.015.
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Modern diagnostics methods ensuring the safety of production and operation, as well as the improvement of functional characteristics of electromechanical systems’ are discussed, method of diagnostics according to the spectrum and hodograph of the engine’s equivalent current is presented. Functional concept is presented for the system of control, diagnostic and monitoring of technical condition of thermal control systems’ electromechanical part in the spacecraft. The decision-making and forecasting algorithm for the operational resource is based on ground-based studies and diagnostic results. This approach to device diagnostics and monitoring is also used in other servo drives, mechatronic and robotic systems of space vehicles and other objects that are inaccessible and left unattended. Hardware-algorithmic implementation of the system is described, recommendations on the components base selection are given.
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Li, Yi-Guang. "Diagnostics of power setting sensor fault of gas turbine engines using genetic algorithm." Aeronautical Journal 121, no. 1242 (July 3, 2017): 1109–30. http://dx.doi.org/10.1017/aer.2017.49.
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ABSTRACTGas path diagnostics is one of the most effective condition monitoring techniques in supporting condition-based maintenance of gas turbines and improving availability and reducing maintenance costs of the engines. The techniques can be applied to the health monitoring of different gas path components and also gas path measurement sensors. One of the most important measurement sensors is that for the engine control, also called the power setting sensor, which is used by the engine control system to control the operation of gas turbine engines. In most of the published research so far, it is rarely mentioned that faults in such sensors have been tackled in either engine control or condition monitoring. The reality is that if such a sensor degrades and has a noticeable bias, it will result in a shift in engine operating condition and misleading diagnostic results.In this paper, the phenomenon of a power-setting sensor fault has been discussed and a gas path diagnostic method based on a Genetic Algorithm (GA) has been proposed for the detection of power-setting sensor fault with and without the existence of engine component degradation and other gas path sensor faults. The developed method has been applied to the diagnostic analysis of a model aero turbofan engine in several case studies. The results show that the GA-based diagnostic method is able to detect and quantify the power-setting sensor fault effectively with the existence of single engine component degradation and single gas path sensor fault. An exceptional situation is that the power-setting sensor fault may not be distinguished from a component fault if both faults have the same fault signature. In addition, the measurement noise has small impact on prediction accuracy. As the GA-based method is computationally slow, it is only recommended for off-line applications. The introduced GA-based diagnostic method is generic so it can be applied to different gas turbine engines.
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Zinko, Roman, Oleh Polishchuk, and Ewa Kuliś. "Vehicle diagnostic system of the car engine." MATEC Web of Conferences 351 (2021): 01014. http://dx.doi.org/10.1051/matecconf/202135101014.
Повний текст джерелаАнотація:
Support of the established technical characteristics of engines is reached by its regular service and diagnostics in particular - existence of the diagnostic programs built in the onboard computer of the car which carry out constant control of the main parameters of the engine.Based on the algorithm of vibroacoustic diagnostics, a system of vibration and vibroacoustic diagnostics of the internal combustion engine (ICE) is proposed, which can be integrated into the intelligent environment of self - diagnostics of the car (Check - Engine). The algorithm takes into account the normalized parameters and criteria for assessing the vibration state: the absolute values of displacements, velocities and accelerations and their changes.For the self-diagnostic system, it is important to develop a classifier of engine states also to determine and / or predict the failure of its parts or units. Engine vibrations can be attributed to the following types: imbalance of the 1st and 2nd order of the engine; vibration associated with combustion in the engine; auxiliary units.The analysis of a vibroacoustic signal at work of the serviceable and faulty engine is carried out, influence of various elements of a design, placement of the gauge in horizontal and vertical directions is shown on it.
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Jankowska, Kamila, and Mateusz Dybkowski. "A Current Sensor Fault Tolerant Control Strategy for PMSM Drive Systems Based on Cri Markers." Energies 14, no. 12 (June 10, 2021): 3443. http://dx.doi.org/10.3390/en14123443.
Повний текст джерелаАнотація:
The paper describes a vector-controlled fault tolerant control (FTC) structure for permanent magnet synchronous motor (PMSM) drives. As a control algorithm, the classical field oriented control was applied. For the proper operation of this drive, minimum two current sensors are necessary, however, in the FTC drives additional redundant transducers are applied. Each measuring sensor, including current sensors, are susceptible to damage and can lead to unstable operation of the drive. The presented control structure, with a diagnostic and compensation system, is robust to the typical current sensor faults—lack of signal, intermittent signal, variable gain, signal noise and signal saturation. The fault detection algorithm is based on the signal method. The fault diagnostic system is tested in two control algorithms—the scalar control and vector control ones—to demonstrate the transient of the faulted signals, detection signals and detection time. After current sensor fault appearance, its influence on the control structure, especially speed transient, is compensated using non-sensitive components. The analysis is presented for all the abovementioned faults for different speed conditions.
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Budanova, M. A., M. P. Chmelevsky, T. V. Treshkur, A. V. Aseev, and V. M. Tikhonenko. "Automatic detection of ventricular and supraventricular wide QRS arrhythmias using complex of morphological criteria and algorithms." Kardiologiia 59, no. 3S (April 13, 2019): 36–42. http://dx.doi.org/10.18087/cardio.2659.
Повний текст джерелаАнотація:
Aim. The aim of study is a detection of ventricular and supraventricular wide QRS arrhythmias using complex of morphological criteria and algorithms by method of automatic analysis. Materials and methods. For 100 patients (m/f – 61/39, Me (min; max) – 44.5 (10; 85) years) of researched group the analysis of 14306 single wide ectopic complexes (QRS 120–230 ms) has been done. Wide complexes include 11028 (77%) ventricular complexes and 3278 (23%) supraventricular complexes represented by 145 different forms of QRS. For verification of arrhythmias origin transesophageal ECG recording and endocardial electrophysiological study were done. The control group included 59 patients (m/f – 25/34, Me (min; max) – 49.5 (14,85) years) with 720 wide QRS, including 467 (65%) ventricular and 253 (35%) supraventricular complexes represented by 86 forms of QRS. The criteria Drew B.J., Scheinman M.M. (1995); Wellens H.J. (1978), RWPT II (Pava LF, 2010) and the algorithms of Brugada P. (1991); Bayesian (2000); Vereckei A. (2008) were used to evaluate sensitivity, specificity and diagnostic accuracy of wide QRS complexes recognition one by one and together, using the method of Wald sequential automatic analysis (KT Result3, CJSC INCART, Russia) and method of artificial neural networks. Results. The best results for the detection of ventricular arrhythmias algorithms were demonstrated by the Brugada P., Drew B.J., Scheinman M.M. algorithm (sensitivity 86.43%, specificity 66.73%, diagnostic accuracy 82.14% in the study group, sensitivity 81.80%, specificity 73.12%, diagnostic accuracy 78.75% in the control group), and the Bayesian algorithm (sensitivity 87.81%, specificity 73.62%, diagnostic accuracy 84.72% in the study group, sensitivity 83.30%, specificity 77.08%, diagnostic accuracy 81.11% in the control group). A complex analysis of the Wald method recognized ventricular arrhythmias in the research group with sensitivity 83.11%, specificity 83.65%, diagnostic accuracy 83.23% and in the control group with a sensitivity 83.51%, specificity of 84.58% and diagnostic accuracy 83.89%. Artificial neural networks recognized ventricular arrhythmias with sensitivity 91.43%, specificity 91.30% and diagnostic accuracy 91.39% in the control group and with sensitivity 97.06%, specificity 99.39% and diagnostic accuracy 97.6% in the research group. Conclusion. Automatic analysis allows obtaining simultaneously the results of each algorithms/criteria and in combination. It significantly reduces the doctor’s work in assessing of amplitude-time characteristics of the complexes. Using artificial neural networks increases the accuracy of of ventricular and supraventricular arrhythmias recognition.
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Xu, Jian, Fei Tian, Lei Wang, and Zhongchang Miao. "Binary Particle Swarm Optimization Intelligent Feature Optimization Algorithm-Based Magnetic Resonance Image in the Diagnosis of Adrenal Tumor." Contrast Media & Molecular Imaging 2022 (February 28, 2022): 1–8. http://dx.doi.org/10.1155/2022/5143757.
Повний текст джерелаАнотація:
This research was aimed to explore the application value of magnetic resonance imaging (MRI) based on binary particle swarm optimization algorithm (BPSO) in the diagnosis of adrenal tumors. 120 patients with adrenal tumors admitted to the hospital were selected and randomly divided into the control group (conventional MRI examination) and the observation group (MRI examination based on the BPSO intelligent feature optimization algorithm), with 60 cases in each group. The sensitivity, specificity, accuracy, and Kappa of the diagnostic methods were compared between the two groups. The results showed that the calculation rate of the BPSO algorithm was the best under the same processing effect ( P < 0.05). Optimization algorithm-based MRI is used in the diagnosis of adrenal tumors, and the results showed that the sensitivity, specificity, accuracy, and Kappa (83.33%, 79.17%, 81.67%, and 0.69) of the observation group were higher than those of the control group (50%, 75%, 58.33%, and 0.45). The similarity of tumor location results in the observation group (89.24%) was significantly higher than that in the control group (65.9%) ( P < 0.05). In conclusion, compared with SFFS and other algorithms, the BPSO algorithm has more advantages in calculation speed. MRI based on the BPSO intelligent feature optimization algorithm has a good diagnostic effect and higher accuracy in adrenal tumors, showing the good development prospects of computer intelligence technology in the field of medicine.
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Kleiman, L. A., and V. I. Freyman. "IMPROVING THE FUNCTIONING RELIABILITY OF THE INFORMATION MANAGEMENT SYSTEM ELEMENTS, USING BUILT-IN DIAGNOSTIC TOOLS." Radio Electronics, Computer Science, Control 1, no. 1 (March 30, 2021): 158–71. http://dx.doi.org/10.15588/1607-3274-2021-1-16.
Повний текст джерелаАнотація:
Context. In the modern world, information management systems have become widespread. This make it possible to automate the technological processes of enterprises of various sizes. Many information management systems include wireless and autonomous elements. Autonomy, in this case, means the ability of the system elements to function for a certain time without additional energy supply. In this regard, such a parameter of operational reliability as the battery life of a system element becomes one of the most important. One of the main tools for improving the reliability and fault tolerance of information management system elements – is the use of a modern diagnostic system.
Objective. The aim of the work is to develop a method for increasing the reliability of the functioning of autonomous elements of information management systems. It includes the creation of a model of an information management system and an algorithm for reasonable redistribution of diagnostic functions, as well as a software implementation of the developed algorithm, which confirms its higher reliability indicators in comparison with other algorithms.
Methods. The basic model was the Preparata-Metz-Chen model. On its basis, a new model of the system was built, including the structural and logical description of the elements and the determination of the way of their interaction. The elements were classified by the degree of criticality of the functions performed in the system. On the basis of the developed model and description of the elements, an algorithm was developed for the reasonable redistribution of the diagnostic load, which made it possible to reduce the average energy consumption of the elements and thereby improve the reliability indicators. A software implementation of the developed algorithm was created, which allows to numerically evaluate its advantages. The developed and existing algorithms were compared.
Results. A model of information management system has been developed. In such a system, it is proposed to use an integrated test diagnostics system. This diagnostic system implements algorithms for redistributing the diagnostic load. To determine the importance of the characteristics taken into account, a linear criterion was chosen, as the most studied and fastest in application. A software model, that implements the developed algorithm and makes it possible to compare it with existing algorithms, has been developed. A study of the software model with various parameters was carried out and, based on the results of the software simulation, conclusions were drawn about the possibilities of improving the algorithm and directions for further scientific research were formulated.
Conclusions. The usage of the developed algorithm makes it possible to increase such a characteristic of the reliability of the elements of the information and control system as the mean time of failure-free operation (mean time between failures) by increasing the operating time of autonomous elements without recharging. When carrying out software modeling of the developed and existing algorithms, the advantages of the first were confirmed, and theoretical possibilities for its improvement were formulated.
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Yesaulov, S., О. Babicheva, and M. Kovalik. "INCREASING THE EFFICIENCY OF THERMAL DIAGNOSTIC CONTROL OF ELECTRIC MOTORS." Municipal economy of cities 4, no. 157 (September 25, 2020): 163–71. http://dx.doi.org/10.33042/2522-1809-2020-4-157-163-171.
Повний текст джерелаАнотація:
The article considers the cause of electromechanical equipment heating (EMЕ) during its operation. It has been reflected the well-known malfunctions of electric motors, that lead to overheating and failure of their individual components. Based on the analysis of existing methods of thermal diagnostics, It has been considered the trends in the creation of devices for monitoring thermal processes and it has been noted the reasons that restrain the use of technical diagnostics tools for thermal faults during the operation of EME. The purpose of this work was to increase the efficiency of the formation of initial information messages. Using the experimental data in the implementation of the EME working cycles, value of the thermal process velocity accompanying the variable technological modes with the bipolar behavior of the output ordinate were determined. Compared to the heating temperature, the rate of value change with a more noticeable contrast reflected the thermal events in the EME, thus determining the priority of this parameter to increase the efficiency of the measuring device. It has been considered methods of forming an array of initial data using a remote transducer sensor to control the heating temperature of equipment with a modulator. It has been proposed algorithms for the electronic formation of an array of initial values and their sorting according to the “principle of flotation”, when a select variables, belonging to the heating processes or cooling of equipment, is provided. A way and an algorithm for determining the rate of temperature change based on current data using a D-shaper are considered. Experimental studies of the electronic components of the diagnostic device with the D-shaper of the initial data array elements confirmed their physical implementation possibility by hardware and software. The results of data arrays formation, taking into account digital sequences in int format with an error of ± 1 Hz, in contrast to the most controlled parameter – float with an error of ± 0.08 ° C, did not change the properties of information messages, but made it possible to reduce the requirements for a microcalculator or a computing device. The results, obtained using the proposed technical solution, confirmed the possibility of increasing the efficiency of thermal and diagnostic control, contributing to a more accurate identification of possible electric motor malfunctions in the EME. The work presents illustrations confirming the suitability of mathematical descriptions and algorithms for processing the initial data for their practical application in electronic measuring instruments for monitoring and diagnosing malfunctions based on thermal events. Keywords: artificial neural network, remote control, modeling, parameter converter, modulator, transport, traction motor, identification, programming.
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Shichev, P. S. "TO DEVELOPMENT OF A MEASURING SYSTEM AND METHODOLOGICAL SUPPORT FOR CONTROL OF TECHNICAL CONDITION OF CENTRIFUGAL PUMP UNITS." Kontrol'. Diagnostika, no. 282 (December 2021): 12–19. http://dx.doi.org/10.14489/td.2021.12.pp.012-019.
Повний текст джерелаАнотація:
The article discusses solutions to create an algorithm for specifying the value of diagnostic parameters and instrumentation of measuring systems for the method of diagnostics of electric driven cantilever centrifugal pumping units by analyzing the spectra of motor current and voltage. The simulation model of the unit was used to establish a quantitative relationship between the amplitudes of the frequency components of the current and voltage of the motor supply line and the dependence of the phase shift between the fundamental harmonics of the current and voltage signals when varying the pressure developed by the pump centrifugal pumping units. An algorithm has been formed that assumes two approaches to refining the amplitude of the informative frequency component of the current spectrum, taken depending on the set of measured parameters. Arrangements of measuring systems for diagnostics of the centrifugal pumping units are proposed both with minimal possibilities for assessing the state of nodes by the spectrum of the current of one of the phases of the power line of the electric motor, and with the extended functionality of the spectral analysis of signals of currents and voltages of three phases of the motor and the possibility of refining the diagnostic parameters in conditions of their distortion by the frequency components of the mains voltage.
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He, Xinrong, Juan Zhao, Yunpeng Xu, Huini Lei, Xianbin Zhang, and Ting Xiao. "Diagnostic Value of Coronary Computed Tomography Angiography Image under Automatic Segmentation Algorithm for Restenosis after Coronary Stenting." Contrast Media & Molecular Imaging 2022 (April 16, 2022): 1–10. http://dx.doi.org/10.1155/2022/7013703.
Повний текст джерелаАнотація:
The diagnostic efficacy of coronary computed tomography angiography (CTA) images of coronary arteries in restenosis after coronary stenting based on the combination of the convolutional neural network (CNN) algorithm and the automatic segmentation algorithm for region growth of vascular similarity features was explored to provide a more effective diagnostic method for patients. 130 patients with coronary artery disease were randomly selected as the research objects, and they were averagely classified into the control group (conventional coronary CTA image diagnosis) and the observation group (coronary CTA image diagnosis based on an improved automatic segmentation algorithm). Based on the diagnostic criteria of coronary angiography (CAG), the efficacy of two kinds of coronary CTA images on the postoperative subsequent visit of coronary heart disease (CHD) stenting was evaluated. The results showed that the accuracy of the CNN algorithm was 87.89%, and the average voxel error of the improved algorithm was signally lower than that of the traditional algorithm (1.8921 HU/voxel vs. 7.10091 HU/voxel) ( p < 0.05). The average score of the coronary CTA image in the observation group was higher than that in the control group (2.89 ± 0.11 points vs. 2.01 ± 0.73 points) ( p < 0.05). The diagnostic sensitivity (91.43%), specificity (86.76%), positive predictive value (88.89%), negative predictive value (89.66%), and accuracy (89.23%) of the observation group were higher than those of the control group ( p < 0.05). In conclusion, the region growth algorithm under the CNN algorithm and vascular similarity features had an accurate segmentation effect, which was helpful for the diagnosis of CTA image in restenosis after coronary stenting.
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M., Venkateswaran, Govindaraju C., and Santhosh T.K. "Integrated predictive control and fault diagnosis algorithm for single inductor-based DC-DC converters for photovoltaic systems." Circuit World 47, no. 1 (June 5, 2020): 105–16. http://dx.doi.org/10.1108/cw-11-2019-0166.
Повний текст джерелаАнотація:
Purpose Power converters are an integral part of the energy conversion process in solar photovoltaic (PV) systems which is used to match the solar PV generation with the load requirements. The increased penetration of renewable invokes intermittency in the generated power affecting the reliability and continuous energy supply of such converters. DC-DC converters deployed in solar PV systems impose stringent restrictions on supplied power, continuous operation and fault prediction scenarios by continuously observing state variables to ensure continuous operation of the converter. Design/methodology/approach A converter deployed for a mission-critical application has to ensure continuous regulated output for which the converter has to ensure fault-free operation. The fault diagnostic algorithm relies on the measurement of a state variable to assess the type of fault. In the same line, a predictive controller depends on the measurement of a state variable to predict the control variable of a converter system to regulate the converter output around a fixed or a variable reference. Consequently, both the fault diagnosis and the predictive control algorithms depend on the measurement of a state variable. Once measured, the available data can be used for both algorithms interchangeably. Findings The objective of this work is to integrate the fault diagnostic and the predictive control algorithms while sharing the measurement requirements of both these control algorithms. The integrated algorithms thus proposed could be applied to any converter with a single inductor in its energy buffer stage. Originality/value laboratory prototype is created to verify the feasibility of the integrated predictive control and fault diagnosis algorithm. As the proposed method combine the fault detection algorithm along with predictive control, a load step variation and manual fault creation methods are used to verify the feasibility of the converter as with the simulation analysis. The value for the capacitors and inductors were chosen based on the charge-second and volt-second balance equations obtained from the steady-state analysis of boost converter.
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Pajor, Mirosław, and Jacek Zapłata. "Intelligent Machine Tool – A Thermal Diagnostic System for a CNC Pretensioned Ball Screw." Solid State Phenomena 220-221 (January 2015): 491–96. http://dx.doi.org/10.4028/www.scientific.net/ssp.220-221.491.
Повний текст джерелаАнотація:
The paper presents a compensation system of thermal deformation for conventional feed axes applied in CNC machine tools allowing for an effective reduction in the impact of heat generated during its operation on the positioning accuracy of the axis. The result has been achieved by equipping feed screws with thermistor temperature sensors. Wiring sensors was led out through an axial bore in the screw and through a rotating electrical connector to an acquisition device coupled with the control system of the CNC machine. An algorithm based on neural networks was implemented in the machine control system, which allows for the online calculation and compensation of heat deformation of feed screws. The algorithm takes into account a variation of thermal deformation values as a function of the table position and the current distribution of the temperature field of the screw and machine. The paper presents a user-friendly method for implementing algorithms containing neural networks in the machine control system. The proposed compensation method has been verified by measuring the linear accuracy of the feed axis positioning. The obtained results confirm the effectiveness of the proposed method in reducing the impact of thermal deformation errors on the positioning accuracy of the axis in CNC machine tools.
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Han, Xiuping, Juan Tan, and Yumei He. "Deep Learning Algorithm-Based MRI Image in the Diagnosis of Diabetic Macular Edema." Contrast Media & Molecular Imaging 2022 (March 4, 2022): 1–9. http://dx.doi.org/10.1155/2022/1035619.
Повний текст джерелаАнотація:
This study investigates the value of magnetic resonance imaging (MRI) based on a deep learning algorithm in the diagnosis of diabetic macular edema (DME) patients. A total of 96 patients with DME were randomly divided into the experimental group (N = 48) and the control group (N = 48). A deep learning 3D convolutional neural network (3D-CNN) algorithm for MRI images of patients with DME was designed. The application value of this algorithm was comprehensively evaluated by MRI image segmentation Dice value, sensitivity, specificity, and other indicators and diagnostic accuracy. The results showed that the quality of MRI images processed by the 3D-CNN algorithm based on deep learning was significantly improved, and the Dice value, sensitivity, and specificity index data were significantly better than those of the traditional CNN algorithm ( P < 0.05 ). In addition, the diagnostic accuracy of MRI images processed by this algorithm was 93.78 ± 5.32%, which was significantly better than the diagnostic accuracy of 64.25 ± 10.24% of traditional MRI images in the control group ( P < 0.05 ). In summary, the 3D-CNN algorithm based on deep learning can significantly improve the accuracy and sensitivity of MRI image recognition and segmentation in patients with DME, can significantly improve the diagnostic accuracy of MRI in patients with DME, and has a good clinical application value.
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Molins, Claudia R., Mark J. Delorey, Christopher Sexton, and Martin E. Schriefer. "Lyme Borreliosis Serology: Performance of Several Commonly Used Laboratory Diagnostic Tests and a Large Resource Panel of Well-Characterized Patient Samples." Journal of Clinical Microbiology 54, no. 11 (August 24, 2016): 2726–34. http://dx.doi.org/10.1128/jcm.00874-16.
Повний текст джерелаАнотація:
The current recommendation for the laboratory confirmation of Lyme disease is serology-based diagnostics. Specifically, a standardized two-tiered testing (STTT) algorithm is applied that utilizes a first-tier immunofluorescence assay or enzyme immunoassay (EIA) that, if the result is positive or equivocal, is followed by second-tier immunoblotting. Despite the standardization and performance achievements, STTT is considered technically complex and subjective, as well as insensitive for early acute infection. These issues have prompted development of novel algorithms and testing platforms. In this study, we evaluated the performance of several commonly used assays for STTT. Several modified two-tiered testing (MTTT) algorithms, including a 2-EIA algorithm and modified criteria for second-tier IgG immunoblots, were also evaluated. All tests were performed on sera from a recently available, well-defined archive of positive- and negative-control patients. Our study demonstrates differences in the results between individual first- and second-tier tests, although the overall agreement of the different STTT algorithms used was strong. In addition, the MTTT algorithm utilizing 2-EIAs was found to be equivalent to all STTT algorithms tested, with agreement ranging from 94 to 97%. The 2-EIA MTTT algorithm slightly enhanced sensitivity in early disease compared to the STTT algorithms evaluated. Furthermore, these data add to the mounting evidence that a 2-EIA-based MTTT algorithm, where immunoblotting is replaced by the C6 EIA, performs as well or better than STTT.
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Vyplaven, V., A. Kolomeets, and A. Popkov. "Processing of strain gauge control signals by the Kalman filter." Journal of Physics: Conference Series 2131, no. 3 (December 1, 2021): 032015. http://dx.doi.org/10.1088/1742-6596/2131/3/032015.
Повний текст джерелаАнотація:
Abstract One of the methods for detecting defects in the rolling surface of the wheels of freight cars is to measure the deformations of the rail under the passing train. The method is based on the analysis of a strain gauge signal. The main task of the strain gauge signal analysis is the selection of informative components and the removal (filtering) of interference. The paper presents methods of filtering diagnostic signals of strain gauge control and the selection of informative components. The useful signal component can be used to measure the mass of cars, to determine the dynamic load on the rails and to detect defects in the rolling surface of the wheels. The method of adaptive Kalman filtering and linear convolution are proposed as signal processing tools. Based on these algorithms, a software module based on the.NET Framework has been developed using the C# programming language. The algorithms were tested on the signals received when the train was moving along the active section of the track, with a strain gauge control system located on it. The computational complexity and speed of the algorithms are assessed, and the possibility of their further application in the autonomous mode of the system is investigated. The results show that the use of the Kalman filtering algorithm provides a significant performance advantage over the linear convolution algorithm.
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Tomczyk, Marcin, Ryszard Mielnik, Anna Plichta, Iwona Gołdasz, and Maciej Sułowicz. "Application of Genetic Algorithm for Inter-Turn Short Circuit Detection in Stator Winding of Induction Motor." Energies 14, no. 24 (December 17, 2021): 8523. http://dx.doi.org/10.3390/en14248523.
Повний текст джерелаАнотація:
This paper presents a new method of inter-turn short-circuit detection in cage induction motors. The method is based on experimental data recorded during load changes. Measured signals were analyzed using a genetic algorithm. This algorithm was next used in the diagnostics procedure. The correctness of fault detection was verified during experimental tests for various configurations of inter-turn short-circuits. The tests were run for several relevant diagnostic signals that contain symptoms of faults in an examined cage induction motor. The proposed algorithm of inter-turn short-circuit detection for various levels of winding damage and for various loads of the examined motor allows one to state the usefulness of this diagnostic method in normal industry conditions of motor exploitation.
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Lee, Soo Young, Bayu Adhi Tama, Seok Jun Moon, and Seungchul Lee. "Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map." Applied Sciences 9, no. 24 (December 12, 2019): 5449. http://dx.doi.org/10.3390/app9245449.
Повний текст джерелаАнотація:
Steel defect diagnostics is considerably important for a steel-manufacturing industry as it is strongly related to the product quality and production efficiency. Product quality control suffers from a real-time diagnostic capability since it is less-automatic and is not reliable in detecting steel surface defects. In this study, we propose a relatively new approach for diagnosing steel defects using a deep structured neural network, e.g., convolutional neural network (CNN) with class activation maps. Rather than using a simple deep learning algorithm for the classification task, we extend the CNN diagnostic model for being used to analyze the localized defect regions within the images to support a real-time visual decision-making process. Based on the experimental results, the proposed approach achieves a near-perfect detection performance at 99.44% and 0.99 concerning the accuracy and F-1 score metric, respectively. The results are better than other shallow machine learning algorithms, i.e., support vector machine and logistic regression under the same validation technique.
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Chen, Guangxi, Liang Zeng, Liu Yang, Yixian Yu, Panli Sun, and Tao Yao. "Improved Quantitative Susceptibility Mapping under Laplace Algorithm in Diagnosis of Parkinson’s Disease." Scientific Programming 2021 (November 12, 2021): 1–9. http://dx.doi.org/10.1155/2021/8210526.
Повний текст джерелаАнотація:
To explore the application value of quantitative susceptibility mapping (QSM) based on Laplace algorithm in the diagnosis of Parkinson’s disease, 48 Parkinson’s disease patients admitted to our hospital were included as the research objects. They were randomly divided into control group (24 cases) and experimental group (24 cases). All patients underwent quantitative magnetic susceptibility imaging scan. In the experimental group, the improved Laplace algorithm was used for QSM diagnosis, while in the control group, conventional QSM diagnosis was used. Through calculations of precision, recall, dice similarity coefficient, intersection-over-union (IoU), and area under the curve (AUC), the quality improvement effect of the improved Laplace algorithm for QSM image was assessed. Then, the diagnostic accuracy of the algorithm was verified by comparing with the results of QSM image diagnosis in Parkinson’s patients without algorithm processing. The results showed that compared with the traditional Laplace algorithm, the improved Laplace algorithm can considerably reduce the image noise level ( P < 0.05 ). The dice, IoU, precision, and recall rate of image quality evaluation indicator were considerably improved ( P < 0.05 ), and the AUC reached 0.896. There were no significant differences in fraction anisotropy (FA) and mean diffusivity (MD) between the two groups ( P > 0.05 ) and no significant differences in magnetic susceptibility of brain nuclei between the two groups ( P > 0.05 ). However, they all showed high magnetic susceptibility in the substantia nigra region of the brain. Compared with the control group, the diagnostic accuracy of the experimental group was 97.5 ± 1.23%, which was considerably higher than that of the control group (86.5 ± 3.56%) ( P < 0.05 ). In short, the image quality of QSM based on Laplace improved algorithm was greatly improved, and the diagnostic accuracy of PD was also greatly improved, which was worthy of promotion in the field of clinical QSM imaging diagnosis of PD.
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Pawlak, Mariusz. "Active Fault Tolerant Control System for the Measurement Circuit in a Drum Boiler Feed-Water Control System." Measurement and Control 51, no. 1-2 (March 2018): 4–15. http://dx.doi.org/10.1177/0020294018754714.
Повний текст джерелаАнотація:
This paper presents a water-level control system in a drum boiler. The system was equipped with a fault tolerant control–type diagnostic system. The paper presents the results of tests conducted on the fault tolerant control system implemented in the water-level control system in a boiler drum. The diagnostics of the measurement circuits was carried out online. To that end, the appropriate partial models were developed and tested. This allowed for the application of analytical redundancy for the measurement circuits. The paper also identifies the influence of diagnostics and fault tolerance on the values of reliability indices and operating safety of a power unit. Fault tolerant control systems increase the safety of a power unit operation, and the studies described in the paper directly contribute to them. These kinds of systems have not been used so far in power unit automation. Site tests confirmed the validity of the acquired concept for the diagnostic system. Fault tolerant control systems have not been commonly applied in power engineering yet. Studies of the water-level control system in a steam drum using the fault tolerant control system for the measurement circuits as presented in the paper are original ideas, providing a new solution. All control systems made for the study fulfil their role in a satisfactory way, which results in a minor deviation in the water-level adjustment in the boiler drum. The tests confirmed the efficiency of the fault detection algorithm. The created models of the water level and flows proved to be successful. Under a no-fault condition of the facility, there were no errors in the diagnoses and the values of all residua were below the detection thresholds. This was achieved despite a high value of measurement noises. The residua helped detect minor faults.
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Mironov, S. V., and D. V. Speranskii. "Genetic algorithms to reduce diagnostic information." Automation and Remote Control 69, no. 7 (July 2008): 1231–40. http://dx.doi.org/10.1134/s000511790807014x.
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Zhdanov, K. V., S. M. Zakharenko, Aleksandr N. Kovalenko, A. V. Semenov, S. S. Kozlov, and A. Ya Fisun. "Zika fever: clinical, epidemiological, laboratory, and diagnostic aspects." Clinical Medicine (Russian Journal) 95, no. 2 (April 21, 2017): 112–17. http://dx.doi.org/10.18821/0023-2149-2017-95-2-112-117.
Повний текст джерелаАнотація:
The authors overview data on the prevalence of Zika fever with reference to biological properties of the causative agent, epidemiological process, pathogenesis, and clinical symptoms of the disease. Special attention is given to the identification of the virus in pregnant women, microcephaly in the babies born by Zika-infected women, algorithm of laboratory diagnostics, and measures needed to prevent and control mosquitoes that spread viruses.
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Dhote, Nitin K., and Jagdish B. Helonde. "Fuzzy Algorithm for Power Transformer Diagnostics." Advances in Fuzzy Systems 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/421621.
Повний текст джерелаАнотація:
Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results fall outside conventional methods codes or when more than one fault exist in the transformer. To overcome these limitations, the fuzzy inference system (FIS) is proposed. Two hundred different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.
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Зимовець, Вікторія Ігорівна, Олександр Сергійович Приходченко та Микита Ігорович Мироненко. "ІНФОРМАЦІЙНО-ЕКСТРЕМАЛЬНИЙ КЛАСТЕР-АНАЛІЗ ВХІДНИХ ДАНИХ ПРИ ФУНКЦІОНАЛЬНОМУ ДІАГНОСТУВАННІ". RADIOELECTRONIC AND COMPUTER SYSTEMS, № 4 (25 грудня 2019): 105–15. http://dx.doi.org/10.32620/reks.2019.4.12.
Повний текст джерелаАнотація:
The study aims to increase the functional efficiency of machine learning of the functional diagnosis system of a multi-rope shaft hoist through cluster analysis of diagnostic features. To achieve the goal, it was necessary to solve the following tasks: formalize the formulation of the task of information synthesis, capable of learning a functional diagnosis system, which operates in the cluster-analysis mode of diagnostic signs; to propose a categorical model and, on its basis, to develop an algorithm for information-extreme cluster analysis of diagnostic signs in the process of information-extreme machine learning of a functional diagnostic system; carry out fuzzification of input fuzzy data by optimizing the geometric parameters of hyperspherical containers of recognition classes that characterize the possible technical conditions of the diagnostic object; to develop an algorithm and implement it on the example of information synthesis of the functional diagnostics system of a multi-rope mine hoisting machine. The object of the study is the processes of information synthesis of a functional diagnostic system capable of learning, integrated into the automated control system of a multi-rope mine hoisting machine. The subject of the study is categorical models, an information-extremal machine learning algorithm of a functional diagnostic system that operates in the cluster analysis model of diagnostic signs and constructs decision rules. The research methods are based on the ideas and methods of information-extreme intellectual data analysis technology, a theoretical-informational approach to assessing the functional effectiveness of machine learning and on the geometric approach of pattern recognition theory. As a result, the following results were obtained: a categorical model was proposed, and on its basis, an algorithm for information-extremal machine learning of the functional diagnostics system for a multi-rope mine hoist was developed and implemented, which allows you to automatically generate an input classified fuzzy training matrix, which significantly reduces time and material costs when creating incoming mathematical description. The obtained result was achieved by cluster analysis of structured vectors of diagnostic signs obtained from archival data for three recognition classes using the k-means procedure. As a criterion for optimizing machine learning parameters, we considered a modified Kullback measure in the form of a functional on the exact characteristics of diagnostic solutions and distance criteria for the proximity of recognition classes. Based on the optimal geometric parameters of the containers of recognition classes obtained during machine learning, decisive rules were constructed that allowed us to classify the vectors of diagnostic features of recognition classes with a rather high total probability of making the correct diagnostic decisions. Conclusions. The scientific novelty of the results obtained consists in the development of a new method for the information synthesis of the functional diagnostics system of a multi-rope mine hoisting machine, which operates in the cluster analysis model, which made it possible to automatically form an input classified fuzzy training matrix with its subsequent dephasification in the process of information-extreme machine learning system.
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Totikov, Z. V., V. Z. Totikov, O. V. Remizov, Sh Sh Gadaev, E. A. Magоmadov, T. V. Ardasenov, U. U. Taramov, and M.-S. A.-S. Abdurzakov. "OPTIMAL DIAGNOSTIC ALGORITHM FOR COLORECTAL CANCER COMPLICATED BY ACUTE BOWEL OBSTRUCTION." Koloproktologia 19, no. 3 (August 25, 2020): 72–79. http://dx.doi.org/10.33878/2073-7556-2020-19-3-72-79.
Повний текст джерелаАнотація:
AIM: to work out a diagnostic program that allows to individualize approach in preoperative care and surgery for patients with colorectal cancer complicated by acute obstruction.PATIENTS AND METHODS: the study included 442 patients with colorectal cancer complicated by acute obstruction, at whom were used special diagnostic methods for the purpose of precise diagnosis.RESULTS: the most informative and safe methods for the diagnosis of tumor obstruction were abdominal X-ray, CT scan, colonoscopy (CS), barium enema (BE) and abdominal ultrasound. These methods allow to prove bowel obstruction and its stage, to detect the tumor site, depth of invasion, distant metastases, to control the efficacy of decompression and to make prognosis of the outcome in 87.2-96.4% patients.CONCLUSION: the information obtained allows to plan individually the use of conservative, endoscopic and surgical methods of treatment for patients with colorectal cancer complicated by acute bowel obstruction.
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Pishchukhin, Aleksandr, and Gulnara Akhmedyanova. "Multidimensional analysis of monitoring and diagnostic information on the technological process." MATEC Web of Conferences 224 (2018): 01022. http://dx.doi.org/10.1051/matecconf/201822401022.
Повний текст джерелаАнотація:
The study divides the analysis of monitoring and diagnostic information, leaving for monitoring the parameters of the stationary process, and for diagnostics various transient regimes in the technological process. This, in turn, divides the algorithms of information processing into control of the output from the given boundaries for stationary parameters and the classification and prediction for dynamically changing parameters in a multidimensional space. In view of the large number of monitoring and diagnostic information, as well as due to different algorithms for processing it in an appropriate information system, it is necessary to apply multi-dimensional analysis methods. As diagnostic influences, various jumplike changes of a “natural character” are used, and the state of the equipment allows judging the apparatus of the influence functions. The abrupt changes in the technological process are reflected in the change in its parameters. The reaction to them is weakened as they are removed in accordance with the influence functions. The values of the parameters at the moment of the reaction define a point in the multidimensional parameter space and allow one to relate the state to one or another standard, and to relate the corresponding management algorithm to the standard. The experimental model includes five links simulating the operations of the technological process, a pulsed signal source simulating a step change and five links of propagation delay simulating the duration of operations. The results confirmed theoretical conclusions about the influence functions.
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Kavanaugh, Michael J., Steven E. Azzam, and David M. Rockabrand. "Malaria Rapid Diagnostic Tests: Literary Review and Recommendation for a Quality Assurance, Quality Control Algorithm." Diagnostics 11, no. 5 (April 25, 2021): 768. http://dx.doi.org/10.3390/diagnostics11050768.
Повний текст джерелаАнотація:
Malaria rapid diagnostic tests (RDTs) have had an enormous global impact which contributed to the World Health Organization paradigm shift from empiric treatment to obtaining a parasitological diagnosis prior to treatment. Microscopy, the classic standard, requires significant expertise, equipment, electricity, and reagents. Alternatively, RDT’s lower complexity allows utilization in austere environments while achieving similar sensitivities and specificities. Worldwide, there are over 200 different RDT brands that utilize three antigens: Plasmodium histidine-rich protein 2 (PfHRP-2), Plasmodium lactate dehydrogenase (pLDH), and Plasmodium aldolase (pALDO). pfHRP-2 is produced exclusively by Plasmodium falciparum and is very Pf sensitive, but an alternative antigen or antigen combination is required for regions like Asia with significant Plasmodium vivax prevalence. RDT sensitivity also decreases with low parasitemia (<100 parasites/uL), genetic variability, and prozone effect. Thus, proper RDT selection and understanding of test limitations are essential. The Center for Disease Control recommends confirming RDT results by microscopy, but this is challenging, due to the utilization of clinical laboratory standards, like the College of American Pathologists (CAP) and the Clinical Lab Improvement Act (CLIA), and limited recourses. Our focus is to provide quality assurance and quality control strategies for resource-constrained environments and provide education on RDT limitations.
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Krstevski, Kiril, Ivancho Naletoski, Dine Mitrov, Slavcho Mrenoshki, Iskra Cvetkovikj, Aleksandar Janevski, Aleksandar Dodovski, and Igor Djadjovski. "Application of Fluorescence Based Molecular Assays for Improved Detection and Typing of Brucella Strains in Clinical Samples." Macedonian Veterinary Review 38, no. 2 (October 1, 2015): 223–32. http://dx.doi.org/10.14432/j.macvetrev.2015.09.055.
Повний текст джерелаАнотація:
AbstractBacteria from the genus Brucella are causative agents of brucellosis - a zoonotic disease which affects many wild and domestic animal species and humans. Taking into account the significant socio-economic and public health impact of brucellosis, its control is of great importance for endemic areas. The chosen control strategy could be successful only if adapted to the current epidemiological situation. This implies that a choice of appropriate diagnostic procedures for detection and typing of Brucella spp. strains are of essential importance. Significant advancement of molecular techniques and their advantages compared to classical methods, give strong arguments in promotion of these techniques as a powerful tool for comprehensive diagnostics of brucellosis. Considering this, the major tasks of the study were to select and implement molecular tests for detection and genotyping Brucella spp. and evaluate their performances using DNA from cultivated brucellae (islolates) and limited number of tissue samples from seropositive animals. The obtained results confirmed that implemented real time PCR for Brucella spp. detection, as well as MLVA-16 used for genotyping, have excellent analytical sensitivity (4.2 fg of Brucella DNA were successfully detected and genotyped). Furthermore, compared to bacteriological cultivation of Brucella spp., real time PCR and MLVA-16 protocols showed superior diagnostic sensitivity and detected Brucella DNA in tissues from which Brucella could not be cultivated. Based on the summarized study results, we propose a diagnostic algorithm for detection and genotyping of Brucella spp. bacteria. Routine use of proposed diagnostic algorithm will improve the effectiveness of infection confirmation and help for accurate evaluation of epidemiological situation.
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Han, Kezhen, Changzhi Chen, Mengdi Chen, and Zipeng Wang. "Constrained Active Fault Tolerant Control Based on Active Fault Diagnosis and Interpolation Optimization." Entropy 23, no. 8 (July 21, 2021): 924. http://dx.doi.org/10.3390/e23080924.
Повний текст джерелаАнотація:
A new active fault tolerant control scheme based on active fault diagnosis is proposed to address the component/actuator faults for systems with state and input constraints. Firstly, the active fault diagnosis is composed of diagnostic observers, constant auxiliary signals, and separation hyperplanes, all of which are designed offline. In online applications, only a single diagnostic observer is activated to achieve fault detection and isolation. Compared with the traditional multi-observer parallel diagnosis methods, such a design is beneficial to improve the diagnostic efficiency. Secondly, the active fault tolerant control is composed of outer fault tolerant control, inner fault tolerant control and a linear-programming-based interpolation control algorithm. The inner fault tolerant control is determined offline and satisfies the prescribed optimal control performance requirement. The outer fault tolerant control is used to enlarge the feasible region, and it needs to be determined online together with the interpolation optimization. In online applications, the updated state estimates trigger the adjustment of the interpolation algorithm, which in turn enables control reconfiguration by implicitly optimizing the dynamic convex combination of outer fault tolerant control and inner fault tolerant control. This control scheme contributes to further reducing the computational effort of traditional constrained predictive fault tolerant control methods. In addition, each pair of inner fault tolerant control and diagnostic observer is designed integratedly to suppress the robust interaction influences between estimation error and control error. The soft constraint method is further integrated to handle some cases that lead to constraint violations. The effectiveness of these designs is finally validated by a case study of a wastewater treatment plant model.
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Dovbysh, Anatoliy S., Anna A. Stadnyk, and Stanislav A. Holubnychyi. "Hierarchical Information-Extreme Learning Algorithm for a Diagnostic System with Redundant Training Matrix." Journal of Automation and Information Sciences 47, no. 4 (2015): 54–63. http://dx.doi.org/10.1615/jautomatinfscien.v47.i4.60.
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Sigua, B. V., V. P. Zemlyanoy, A. S. Sokolova, D. F. Cherepanov, S. A. Vinichuk, A. V. Nikiforenko, D. S. Sakhno, and E. A. Zakharov. "Modern technology in the treatment of patients with postoperative ventral hernias." HERALD of North-Western State Medical University named after I.I. Mechnikov 10, no. 1 (March 15, 2018): 37–42. http://dx.doi.org/10.17816/mechnikov201810137-42.
Повний текст джерелаАнотація:
The analysis of results of treatment of 113 patients with postoperative ventral hernias was carried out patients divided into two groups. 74 patients have received treatment (II (control) group) from 2013 to 2014 year. Patients of the control group were examined according to the accepted diagnostic algorithms, in addition to physical examination algorithm include chest and abdominal x-ray examination, ultrasound diagnosis of abdominal organs and fibro-esophagogastroduodenoscopy. Plastic by reticular endoprostheses was carried out in various methods. Methods have been chosen directly by surgeon during the operation. The draining of the postoperative wound was mandatory. Main (I) group included 39 patients who received treatment from 2015 to 2016. The diagnostic algorithm was supplemented with fibrocolonoscopy and spiral computed tomography, and the plastic method was predominantly sub-lay. Hypoderma treatment by high-temperature oscillating plasma flow energy according to the original technique for the prevention of lymphorrhea was provided to all patients. Maims were mostly sutured tightly. This approach allowed to reliably reduce frequency complications from 13.4% (14) to 1.8% (2) and, as a consequence, the length of stay of the patient in the hospital.
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Ahn, Hyojung, Dawoon Jung, and Han-Lim Choi. "Deep Generative Models-Based Anomaly Detection for Spacecraft Control Systems." Sensors 20, no. 7 (April 2, 2020): 1991. http://dx.doi.org/10.3390/s20071991.
Повний текст джерелаАнотація:
A spacecraft attitude control system provides mechanical and electrical control to achieve the required functions under various mission scenarios. Although generally designed to be highly reliable, mission failure can occur if anomalies occur and the attitude control system fails to properly orient and stabilize the spacecraft. Because accessing spacecraft to directly repair such problems is usually infeasible, developing a continuous condition monitoring model is necessary to detect anomalies and respond accordingly. In this study, a method for detecting anomalies and characterizing failures for spacecraft attitude control systems is proposed. Herein, features are extracted from multidimensional time-series data of a simulation of the attitude control system. Then, the artificial neural network learning algorithms based on two types of generation models are applied. A Bayesian optimization algorithm with a Gaussian process is used to optimize the hyperparameters for the neural network to improve the performance. The performance is evaluated based on the reconstruction error through the algorithm using the newly generated data not used for learning as input data. Results show that the detection performance depends on the operating characteristics of each submode in the operation scenarios and type of generation model. The diagnostic results are monitored to detect anomalies in operation modes and scenarios.
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Zhu, Qinyue, Zhaoyang Li, Xitang Tan, Dabo Xie, and Wei Dai. "Sensors Fault Diagnosis and Active Fault-Tolerant Control for PMSM Drive Systems Based on a Composite Sliding Mode Observer." Energies 12, no. 9 (May 5, 2019): 1695. http://dx.doi.org/10.3390/en12091695.
Повний текст джерелаАнотація:
Due to the use of multiple observers and controllers in multi-sensor fault-tolerant control of PMSM drive systems, the algorithm is complex and the system control performance is affected. In view of this, the paper studies multi-sensor fault diagnosis and active fault-tolerant control strategies based on a composite sliding mode observer. With the mathematical model of PMSM built, a design method of the composite sliding mode observer is proposed. A single observer is used to observe and estimate various state variables in the system in real time, which simplifies the implementation of observer-related algorithms. In order to improve the diagnostic accuracy of different types of sensors under different faults, a method for determining fault thresholds is proposed through global search for the maximum residual value. Based on this, a fault diagnosis and active fault-tolerant control strategy is proposed to realize fast switching and reconstruction of feedback signals of closed-loop control systems under different faults of multiple sensors, thus restoring the system performance. Finally, the effectiveness of the proposed algorithm and control strategy is verified by simulation experiments
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GROSU-BULARDA, Andreea, Andra-Luana LAZARESCU, Mihaela-Cristina ANDREI, Dan Aurel IONESCU, Adrian FRUNZA, Sabina GRAMA, Alexandru STOIAN, et al. "Infectious Complications in Severely Burned Adult Patients-Diagnostic and Therapeutic Algorithm." Medicina Moderna - Modern Medicine 28, no. 1 (March 29, 2021): 25–40. http://dx.doi.org/10.31689/rmm.2021.28.1.25.
Повний текст джерелаАнотація:
Severe burn injuries represent a major challenge to the entire healthcare system in developing countries and even for states with a high standard of care. A clear understanding of the physiopathology of burn injuries is essential for providing an adequate prompt treatment to ensure an optimal patient outcome. Early recognition and treatment of burns complications, especially severe infections represent an important prevention strategy, improving survival after these severe injuries. Specific treatment must be conducted according to the characteristics of the patients in order to reduce morbidity and mortality and avoid the development of antimicrobial resistance. A diagnostic and therapeutic algorithm is presented, centered on infectious source prevention and control with early surgical excision and skin grafting together with culture-guided antimicrobial therapy. It is a known fact that, indifferent of the involved germ, the best intervention for both prophylaxis and treatment of infections in the burn patient is the early excision of the devitalized tissue and subsequent closure of burn wounds with skin grafts, measures that diminish local and systemic mediator releasing effects in burnt tissue, attenuating the progressive inflammatory
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Quattrocchi, Gaetano, Alessandro Iacono, Pier C. Berri, Matteo D. L. Dalla Vedova, and Paolo Maggiore. "A New Method for Friction Estimation in EMA Transmissions." Actuators 10, no. 8 (August 11, 2021): 194. http://dx.doi.org/10.3390/act10080194.
Повний текст джерелаАнотація:
The increasing interest for adopting electromechanical actuators (EMAs) on aircraft demands improved diagnostic and prognostic methodologies to be applied to such systems in order to guarantee acceptable levels of reliability and safety. While diagnostics methods and techniques can help prevent fault propagation and performance degradation, prognostic methods can be applied in tandem to reduce maintenance costs and increase overall safety by enabling predictive and condition-based maintenance schedules. In this work, a predictive approach for EMAs friction torque estimation is proposed. The algorithm is based on the reconstruction of the residual torque in mechanical transmissions. The quantity is then sampled and an artificial neural network (ANN) is used to obtain an estimation of the current health status of the transmission. Early results demonstrate that such an approach can predict the transmission health status with good accuracy.
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You, Seung-Han, Jin-Oh Hahn, Young Man Cho, Soojoon Kang, and Kyo Lee. "A Fault Diagnostics Algorithm for Differential Brake Control System." IFAC Proceedings Volumes 36, no. 14 (August 2003): 275–80. http://dx.doi.org/10.1016/s1474-6670(17)32432-1.
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Wang, X., and A. Subic. "Psychoacoustic modelling of vehicle side mirror power-fold actuator noise characteristics." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, no. 6 (April 13, 2011): 1419–29. http://dx.doi.org/10.1177/0954406211399514.
Повний текст джерелаАнотація:
A study of vehicle side mirror power-fold actuator noise characteristic was undertaken, which seeks to correlate subjective evaluation with objective measurements as a basis for development of a product sound quality control algorithm. Psychoacoustic metrics were extracted from the product sound measurements and analysed for manufacturing quality assessment. Two approaches were explored in this study; one is a multiple metric sequential pass-through gate approach and the other is the least square fit regression approach, where measured relevant psychoacoustic metrics are modelled against subjective rating data provided by product sound quality evaluation experts. The ‘gate’ approach using sound pressure level, roughness, and tonality was successfully implemented to segregate bad power-fold actuators from good ones in terms of sound quality. A non-linear, two metric regression algorithm assessing what is a ‘good’ or a ‘bad’ actuator, was then developed and validated through comparison with a linear eight metric regression algorithm. Based on correlation of objective measurement and subjective evaluation results for given product samples, the diagnostic methodology developed in this research is applicable to other products for noise diagnostics and quality control.
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Jiang, Fenfen, Haokai Xu, Xiaowen Shi, Bingjiang Han, Zhenliang Chu, Bin Xu, and Xiaorong Liu. "Dynamic Electrocardiogram under P Wave Detection Algorithm Combined with Low-Dose Betaloc in Diagnosis and Treatment of Patients with Arrhythmia after Hepatocarcinoma Resection." Journal of Healthcare Engineering 2021 (October 16, 2021): 1–13. http://dx.doi.org/10.1155/2021/6034180.
Повний текст джерелаАнотація:
This work aimed to study the diagnostic value of dynamic electrocardiogram (ECG) based on P wave detection algorithm for arrhythmia after hepatectomy in patients with primary liver cancer, and to compare the therapeutic effect of different doses of Betaloc. P wave detection algorithm was introduced for ECG automatic detection and analysis, which can be used for early diagnosis of arrhythmia. Sixty patients with arrhythmia after hepatectomy for primary liver cancer were selected as the research objects. They were randomly divided into control group, SD group, MD group, and HD group, with 15 cases in each group. No Betaloc, low-dose (≤47.5 mg), medium-dose (47.5–95 mg), and high-dose (142.5–190 mg) Betaloc were used for treatment. As a result, P wave detection algorithms can mark P waves that may be submerged in strong interference. P waves from arrhythmia database were used to verify the performance of the proposed algorithm. The prediction precision (Pp) of ventricular arrhythmia and atrial arrhythmia was 98.53% and 98.76%, respectively. Systolic blood pressure (117.35 ± 7.33, 126.44 ± 9.38, and 116.02 ± 8.2) mmHg in SD group, MD group, and HD group was significantly lower than that in control group (140.3 ± 7.21) mmHg after two weeks of treatment. Moreover, those of SD group and HD group were significantly lower than MD group ( P < 0.05 ). The effective rate of cardiac function improvement in SD group (72.35 ± 1.21%) was significantly higher than that in control group, MD group, and HD group (38.2 ± 0.98%, 65.12 ± 1.33%, and 60.43 ± 1.25%; P < 0.05 ). In short, dynamic ECG based on P wave detection algorithm had high diagnostic value for arrhythmia after hepatectomy in patients with primary liver cancer. It was safe and effective for patients to choose small dose of Betaloc.
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Bazhenov, YU V., and V. P. Kalenov. "Diagnosis of electronic engine control systems for LADA automobile." Izvestiya MGTU MAMI 12, no. 4 (December 15, 2018): 2–9. http://dx.doi.org/10.17816/2074-0530-66808.
Повний текст джерелаАнотація:
The article is devoted to the development of a system for ensuring the efficiency of ECM through the introduction of diagnostics of its technical condition into the technological processes of servicing and repairing vehicles. In order to create such a system, we performed studies on the operational reliability of the structural elements of the ECU, the effect of their failures on the engine operating parameters, and the substantiation of diagnostic parameters evaluating the technical condition of the system. The object of the research is the electronic control system M86, installed on domestic LADA VESTA automobiles (VAZ-21179 engine). According to the results of the research, the average operating time of the structural elements of the ECM to failure and the reasons for their occurrence were established. Authors imitated the failure of each ECM elements by turning it off and using the appropriate equipment to measure selected parameters of engine operation. The most significant influence on the operation of the engine is caused by failure of fuel pump, ECU and crankshaft position sensor. The failure of this elements causes the complete loss of engine performance as fuel supply to the cylinders stops. In case of failure of other elements of ECM the engine performance is maintained, but its operating capabilities significantly decrease. To ensure the required level of reliability of the ECM, it is necessary at regular intervals to maintain the vehicles to diagnose the technical condition of its elements at automobile service enterprises. In this paper, it is recommended to split the ECM into three subsystems, each of which is evaluated by one complex diagnostic parameter reflecting the technical condition of the structural elements included there. The deviation of the parameters beyond the tolerance limits caused by the occurrence of faults serves as the basis for deciding whether to conduct the necessary technical actions. Diagnostics of the ECM subsystems is carried out in accordance with the developed algorithms that establish a rational sequence of control, adjustment and other operations to identify and eliminate damages arising in the process of operation. As an example, the article presents an algorithm for diagnosing the fuel supply subsystem and reducing the toxicity of exhaust gases. The developments proposed in the article were tested and introduced into the technological processes of maintenance and automobile repairs of the LADA dealership center in the city of Vladimir.
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Liu, Yang, Yan Ma, Yongsheng Yang, and Tingting Zheng. "Counterexample Generation for Probabilistic Model Checking Micro-Scale Cyber-Physical Systems." Micromachines 12, no. 9 (August 31, 2021): 1059. http://dx.doi.org/10.3390/mi12091059.
Повний текст джерелаАнотація:
Micro-scale Cyber-Physical Systems (MCPSs) can be automatically and formally estimated by probabilistic model checking, on the level of system model MDPs (Markov Decision Processes) against desired requirements in PCTL (Probabilistic Computation Tree Logic). The counterexamples in probabilistic model checking are witnesses of requirements violation, which can provide the meaningful information for debugging, control, and synthesis of MCPSs. Solving the smallest counterexample for probabilistic model checking MDP has been proven to be an NPC (Non-deterministic Polynomial complete) problem. Although some heuristic methods are designed for this, it is usually difficult to fix the heuristic functions. In this paper, the Genetic algorithm optimized with heuristic, i.e., the heuristic Genetic algorithm, is firstly proposed to generate a counterexample for the probabilistic model checking MDP model of MCPSs. The diagnostic subgraph serves as a compact counterexample, and diagnostic paths of MDP constitute an AND/OR tree for constructing a diagnostic subgraph. Indirect path coding of the Genetic algorithm is used to extend the search range of the state space, and a heuristic crossover operator is used to generate more effective diagnostic paths. A prototype tool based on the probabilistic model checker PAT is developed, and some cases (dynamic power management and some communication protocols) are used to illustrate its feasibility and efficiency.
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Veselovska, Nataliya, Olha Yalina, and Vasyliy Yanishevskiy. "DEVELOPMENT OF AN ALGORITHM FOR DIAGNOSING DENTAL DEFECTS IN REDUCERS OF SELF-PROPELLED AGRICULTURAL MACHINES." ENGINEERING, ENERGY, TRANSPORT AIC, no. 3(110) (October 30, 2020): 16–23. http://dx.doi.org/10.37128/2520-6168-2020-3-2.
Повний текст джерелаАнотація:
Intensive use of agricultural production technologies has recognized the intensive characteristics of agricultural producers, which allowed to present the need for frequent diagnostics, maintenance and repair. This transition is due to objective trends in the development of agricultural machinery. The solution to this problem involves the development and creation of effective tools for monitoring and diagnosing the state of the transmission elements of agricultural machinery; assessment of the value of the residual life of individual elements of the system and the study of energy conversion processes in these elements; construction of mathematical models on the basis of which it is possible to predict the occurrence of dangerous damage depending on the operating conditions of the equipment. Diagnostic means must ensure the assessment of the condition of the working surfaces of the interacting transmission elements, control of wear of their surfaces and assessment of the performance of the unit as a whole or the kinematic pair without their disassembly during operation. The time of collection of diagnostic information and allocation of informative signs and parameters of signals should be minimized. Control methods should be as simple as possible, and the means that implement them - compact, with built-in algorithms for information processing and decision-making. The most informative parameter that carries the maximum information about the state of the unit of a working machine or unit is mechanical oscillations (vibrations) - elastic waves propagating in continuous media. Measurement of vibroacoustic characteristics on bearing supports of mechanisms allows to recognize such defects and damages as imbalance and skew of shafts; damage to plain and rolling bearings; damage to gears in gears; damage to couplings; damage to electrical machines. With the systematic use of modern diagnostic methods it is possible to avoid serious damage to the transmission elements and reduce operating costs for maintenance of agricultural machinery due to the fact that the repair is carried out only when the measurement results indicate its need.
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Liss, Michał, Roman Zinko, Oleh Polishchuk, Marcin Łukasiewicz, and German R. Betancur. "Principles of formation of a vehicle’s braking system complex diagnostics." MATEC Web of Conferences 332 (2021): 01018. http://dx.doi.org/10.1051/matecconf/202133201018.
Повний текст джерелаАнотація:
On-board diagnostics of technical condition of the brake system elements provide control during the inter-control period of operation. The use of ABS requires further development of on-board diagnostic tools based on the development of new diagnostic methods using multilevel structural and effect schemes, primarily in terms of efficiency parameters. Increase of the information content by increasing the number of measured kinematic parameters and improving the algorithms for generating control signals for the brake system actuators showed effectiveness of diagnostics. To implement diagnostics of technical state of the brake system elements on board of a vehicle, a structural diagram of a diagnostic device, which uses telecommunication means of connection with an expert system and a voltage strain gauge in the coupling device of a truck has been proposed. It is advisable to carry out in-depth element-by-element diagnostics under bench conditions, with the maximum possible exclusion of random factors affecting the process. To summarize all information about the state of the vehicle's braking system, it is necessary to use an expert system.
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Kalra, Lalit, Craig J. Smith, John Hodsoll, Andy Vail, Saddif Irshad, and Dulka Manawadu. "Elevated C-reactive protein increases diagnostic accuracy of algorithm-defined stroke-associated pneumonia in afebrile patients." International Journal of Stroke 14, no. 2 (September 10, 2018): 167–73. http://dx.doi.org/10.1177/1747493018798527.
Повний текст джерелаАнотація:
Background and aim Pyrexia-dependent clinical algorithms may under or overdiagnose stroke-associated pneumonia. This study investigates whether inclusion of elevated C-reactive protein as a criterion improves diagnosis. Methods The contribution of C-reactive protein ≥30 mg/l as an additional criterion to a Centers for Disease Control and Prevention-based algorithm incorporating pyrexia with chest signs and leukocytosis and/or chest infiltrates to diagnose stroke-associated pneumonia was assessed in 1088 acute stroke patients from 37 UK stroke units. The sensitivity, specificity, and positive predictive value of different approaches were assessed using adjudicated stroke-associated pneumonia as the reference standard. Results Adding elevated C-reactive protein to all algorithm criteria did not increase diagnostic accuracy compared with the algorithm alone against adjudicated stroke-associated pneumonia (sensitivity 0.74 (95% CI 0.65–0.81) versus 0.72 (95% CI 0.64–0.80), specificity 0.97 (95% CI 0.96–0.98) for both; kappa 0.70 (95% CI 0.63–0.77) for both). In afebrile patients (n = 965), elevated C-reactive protein with chest and laboratory findings had sensitivity of 0.84 (95% CI 0.67–0.93), specificity of 0.99 (95% CI 0.98–1.00), and kappa 0.80 (95% CI 0.70–0.90). The modified algorithm of pyrexia or elevated C-reactive protein and chest signs with infiltrates or leukocytosis had sensitivity of 0.94 (95% CI 0.87–0.97), specificity of 0.96 (95% CI 0.94–0.97), and kappa of 0.88 (95% CI 0.84–0.93) against adjudicated stroke-associated pneumonia. Conclusions An algorithm consisting of pyrexia or C-reactive protein ≥30 mg/l, positive chest signs, leukocytosis, and/or chest infiltrates has high accuracy and can be used to standardize stroke-associated pneumonia diagnosis in clinical or research settings. Trial Registration http://www.isrctn.com/ISRCTN37118456
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Nalepa, B., and A. Gwiazda. "Synchronization of Measurement Data with the Reference System." IOP Conference Series: Materials Science and Engineering 1182, no. 1 (October 1, 2021): 012052. http://dx.doi.org/10.1088/1757-899x/1182/1/012052.
Повний текст джерелаАнотація:
Abstract Today, most devices are equipped with sensors that enable the collection of process data that influences the control parameters. Sensors are to replace human senses and provide information from the environment to the control algorithm. In many cases, the sensors used are subject to reference measurements from an external diagnostic system, which is to eliminate possible errors in the installation of sensors or the operation of the control algorithm. For this purpose, external systems are most often used that do not duplicate any errors from the sensors used. Most often these are vision systems that are based on the measurement of markers mounted on the tested object. The most important effect of the reference system is the verification of sensor data or the correction of the control algorithm that processes sensor data. Often, when comparing two measurement systems, there is a problem with unequal sampling rates. The main problem is comparing the two graphs as the number of data / arguments is different. In order to compare two graphs, many methods are used to reduce or extend the number of arguments. The use of data expansion or reduction methods may result in an additional error. In this article, methods of reducing measurement data from IMU sensors and encoders connected to servo motors will be presented. There is a significant difference in sampling time for the two measurement systems shown. At the beginning, the methods and applications of algorithms in the literature will be discussed. Then, the measurement data will be compared with the data processed by the algorithms. The final point of work will be the combination of the data processed by the algorithm with the measurements. Two measurements were also compared in order to estimate the measurement error and a comparative algorithm was proposed.
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Levin, Vladimir M., and Ammar A. Yahya. "Adaptive management of technical condition of power transformers." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 3862. http://dx.doi.org/10.11591/ijece.v10i4.pp3862-3868.
Повний текст джерелаАнотація:
Ensuring reliable operation of power transformers as part of electric power facilities is assigned to the maintenance and repair system, whose important components are diagnostics and monitoring of the technical condition. Monitoring allows you to answer the question of whether the transformer abnormalities and how to do they manifest, while diagnostics allow determining the nature, the severity of the problem, determine the cause and possible consequences. The article presents the results of the authors ' research on creating an algorithm for adaptive control of the technical condition of power transformers using diagnostic and monitoring data. The developed algorithm implements the decision-making procedure for ensuring the reliable operation of oil-filled transformer equipment as part of the substations of electric power facilities. The decision-making procedure is based on the method of statistical Bayesian identification the states of a transformer based on the results of dissolved gas analysis (DGA) in oil. The method is characterized by high reliability of recognizing defects in the transformer and the ability to adapt the probabilities of the obtained solutions to the newly received diagnostic information. These results illustrate the effectiveness of the developed approach and the possibility of its application in the operation of oil-filled transformer equipment.
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Feldman, A., G. Provan, and A. Van Gemund. "A Model-Based Active Testing Approach to Sequential Diagnosis." Journal of Artificial Intelligence Research 39 (September 29, 2010): 301–34. http://dx.doi.org/10.1613/jair.3031.
Повний текст джерелаАнотація:
Model-based diagnostic reasoning often leads to a large number of diagnostic hypotheses. The set of diagnoses can be reduced by taking into account extra observations (passive monitoring), measuring additional variables (probing) or executing additional tests (sequential diagnosis/test sequencing). In this paper we combine the above approaches with techniques from Automated Test Pattern Generation (ATPG) and Model-Based Diagnosis (MBD) into a framework called FRACTAL (FRamework for ACtive Testing ALgorithms). Apart from the inputs and outputs that connect a system to its environment, in active testing we consider additional input variables to which a sequence of test vectors can be supplied. We address the computationally hard problem of computing optimal control assignments (as defined in FRACTAL) in terms of a greedy approximation algorithm called FRACTAL-G. We compare the decrease in the number of remaining minimal cardinality diagnoses of FRACTAL-G to that of two more FRACTAL algorithms: FRACTAL-ATPG and FRACTAL-P. FRACTAL-ATPG is based on ATPG and sequential diagnosis while FRACTAL-P is based on probing and, although not an active testing algorithm, provides a baseline for comparing the lower bound on the number of reachable diagnoses for the FRACTAL algorithms. We empirically evaluate the trade-offs of the three FRACTAL algorithms by performing extensive experimentation on the ISCAS85/74XXX benchmark of combinational circuits.