Design Of Diagnostic System Research Articles

Performance characterization of x-ray crystal spectroscopy highly oriented pyrolytic graphite reflectors based on x-ray diffractometry experiments.

The use of Highly Oriented Pyrolytic Graphite (HOPG) reflectors is often proposed in the design of X-ray Crystal Spectroscopy (XCS) diagnostic systems for the next-generation tokamak devices, including the ITER project. This study introduces an experimental study based on the X-Ray Diffractometry (XRD) method to evaluate the performance of HOPG reflectors. The experimental method provides both the angular responses and the reflectivities of the HOPG reflectors. A demonstrative XRD experiment is conducted, and the details of the experiment are introduced. This method enables precise studies on HOPG reflectors, facilitating the design of XCS diagnostic systems for future tokamaks.

In-vessel design of a two-color heterodyne laser interferometer system for SPARC.

This article covers the in-vessel design of the SPARC interferometry diagnostic system, highlighting unique aspects of the systems design and port plug integration in preparation for "day-1" plasma operations as a critical diagnostic for density feedback control. An early decision for the diagnostic was to deploy two lasers in the infrared wavelength spectrum, allowing the system to have a higher optical throughput. The optimization of the in-vessel geometry for the diagnostic follows a similar approach, focusing on de-risking possible damage to the plasma facing optical components by moving them further from the plasma with an orientation that provides a greater possibility for protective features to be added. The inclusion of in-vessel optical assemblies requires detailed design efforts of custom all-metal parts, designed to remain functional when subjected to harsh operational conditions, in many cases for the entire SPARC lifetime. The details presented here were included in the design to ensure that the assemblies can not only withstand a major electromagnetic disruption or thermal event but also maintain good stability through normal operations. The design also addresses more nuanced effects, such as the transient heat loading of the plasma facing mirrors. Through the utilization of modeling and design tools, these effects were brought into the design and simulation workflow, further reducing uncertainty as the system moves toward system commissioning.

Research on the Design of Medical Imaging Assisted Diagnosis System Based on Machine Learning

The design and application of medical imaging diagnostic assistance systems have become an indispensable facet of modern medicine. Technologies based on machine learning can handle vast and intricate medical imaging data, offering precise diagnostic support and significantly enhancing the efficiency and accuracy of clinical diagnoses. This study focuses on the development and implementation of an efficient medical imaging diagnostic assistance system utilizing machine learning algorithms such as Principal Component Analysis (PCA), Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP). Through the architectural design of intelligent diagnostic assistance systems, data preprocessing, and feature vectorization, combined with the application of classification algorithms, a robust diagnostic support system capable of addressing diverse medical scenarios has been constructed. Performance evaluation experiments indicate that this system demonstrates high accuracy and robustness in processing medical imaging data, holding potential for clinical implementation. The findings of this study provide new insights for the advancement of intelligent diagnostic systems in medical imaging and lay a solid foundation for the development of future clinical diagnostic tools.

Evaluation of the Diagnostic Sensitivity of Digital Vibration Sensors Based on Capacitive MEMS Accelerometers.

In recent years, there has been an increasing use of digital vibration sensors that are based on capacitive MEMS accelerometers for machine vibration monitoring and diagnostics. These sensors simplify the design of monitoring and diagnostic systems, thus reducing implementation costs. However, it is important to understand how effective these digital sensors are in detecting rolling bearing faults. This article describes a method for determining the diagnostic sensitivity of diagnostic parameters provided by commercially available vibration sensors based on MEMS accelerometers. Experimental tests were conducted in laboratory conditions, during which vibrations from 11 healthy and faulty rolling bearings were measured using two commercial vibration sensors based on MEMS accelerometers and a piezoelectric accelerometer as a reference sensor. The results showed that the diagnostic sensitivity of the parameters depends on the upper-frequency band limit of the sensors, and the parameters most sensitive to the typical fatigue faults of rolling bearings are the peak and peak-to-peak amplitudes of vibration acceleration. Despite having a lower upper-frequency range compared to the piezoelectric accelerometer, the commercial vibration sensors were found to be sensitive to rolling bearing faults and can be successfully used in continuous monitoring and diagnostics systems for machines.

CAD System Design for Two-class Brain Tumor Classification using Transfer Learning

Background: The occurrence of brain tumors is rapidly increasing, mostly in the younger generation. Tumors can directly destroy all healthy brain cells and spread rapidly to other parts. However, tumor detection and removal still pose a challenge in the field of biomedicine. Early detection and treatment of brain tumors are vital as otherwise can prove to be fatal. Objective: This paper presents the Computer Aided Diagnostic (CAD) system design for two classification of brain tumors employing the transfer learning technique. The model is validated using machine learning techniques and other datasets. Methods: Different pre-processing and segmentation techniques were applied to the online dataset. A two-class classification CAD system was designed using pre-trained models namely VGG16, VGG19, Resnet 50, and Inception V3. Later GLDS, GLCM, and hybrid features were extracted which were classified using Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Probabilistic Neural Network (PNN) techniques Results: The overall classification accuracy using Inception V3 is observed as 83%. 85% accuracy was obtained using hybrid GLCM and GLDS features using the SVM algorithm. The model has been validated on the BraTs dataset which results in 84.5% and 82% accuracy using GLCM + GLDS + SVM and Inception V3 technique respectively. Conclusion: 2.9% accuracy improvement was attained while considering GLCM + GLDS + SVM over kNN and PNN. 0.5% and 1.2% accuracy improvement were attained for CAD system design based on GLCM + GLDS + SVM and Inception v3 model respectively.

Использование бесклеточных антигенных фракций возбудителя псевдотуберкулёза для получения диагностических сывороток

The development of simple, express, and at the same time sensitive and selective methods for diagnosing pseudotuberculosis remains an urgent task of practical public health. The effectiveness of the test systems largely depends on the activity and the specificity of a diagnostic pseudotuberculous sera. Achieving high sensitivity and specificity of diagnostic test systems largely depends on the presence of specific pseudotuberculous sera with a high antibody titer, which, in turn, is ensured by the proper selection of the animal producer, the physicochemical properties of the immunogen, its dose, multiplicity and duration. introduction and use of adjuvants. Currently, diagnostic serum for Yersinia pseudotuberculosis for an approximate agglutination test on a glass slides is produced in the Russian Federation (FSUE SPbNIIVS FMBA of RUSSIA). Obtaining of pseudotuberculous sera for a wider scope of their use, in particular, in the design of diagnostic immunobiological preparations and test systems for pseudotuberculosis, is relevant and in demand in healthcare practice. The purpose of this study was to obtain and characterize hyperimmune specific sera against cellfree antigenic fractions of the causative agent of pseudotuberculosis. Chinchilla rabbits were used as sera producers. Cell-free antigenic fractions (urea extract, outer membranes, and proteinlipopolysaccharide complex) obtained by treating Yersinia pseudotuberculosis 3704 O:1b strain cells with a 9 M urea solution followed by differential centrifugation were used as immunogens. The presence of specific antibodies in experimental pseudotuberculosis sera was determined in the test tube agglutination reaction, passive hemagglutination reaction and agarose gel double immunodiffusion test. The proposed schemes of immunization of rabbits with subcellular fractions of the pseudotuberculosis microbe make it possible to prepare hyperimmune sera with activity in the standard tubeagglutination test ≥1:1600, in the indirect hemagglutination test ≥1:3200. There was no crossreactivity of antibodies in the immunodiffusion test with heterologous, closely related antigenically microorganisms (Yersinia enterocolitica 628, Salmonella typhi 21, Shigella flexneri 1964). The most important antigenic material is concentrated in the surface structures of pathogens of bacterial infectious diseases, against which, during immunization of laboratory animals, specific antibodies are produced that can interact with antigenic epitopes of outer membrane proteins and O-specific side chains of lipopolysaccharide (O-antigen) exposed on the surface of the microorganism. The data obtained confirm the effectiveness of the use of surface cell-free antigenic fractions of the pathogen to produce highly active specific pseudotuberculosis sera, suitable for use as a source of specific antibodies in the design of diagnostic immunobiological preparations and test systems for pseudotuberculosis.

Design of Medical Diagnostic System Based on Artificial Intelligence

Design of Medical Diagnostic System Based on Artificial Intelligence

Facile Strategy to Output Fluorescein from Nucleic Acid Interactions.

Biomolecular operations, which involve the conversion of molecular signals or interactions into specific functional outputs, are fundamental to the field of biology and serve as the important foundation for the design of diagnostic and therapeutic systems. To maximize their functionalities and broaden their applicability, it is crucial to develop novel outputs and facile chemical transformation methods. With this aim, in this study, we present a straightforward method for converting nucleic acid signals into fluorescein outputs that exhibit a wide range of functionalities. This operation is designed through a DNA-templated reaction based on riboflavin-photocatalyzed oxidation of dihydrofluorescein, which is readily prepared by simple NaBH4 reduction of the fluorescein with no complicated chemical caging steps. The templated photooxidation exhibits high efficiency (kapp = 2.7 × 10-3/s), generating a clear fluorescein output signal distinguishable from a low background, originating from the high stability of the synthesized dihydrofluorescein. This facile and efficient operation allows the nucleic acid-initiated activation of various fluorescein functions, such as fluorescence and artificial oxidase activity, which are applied in the design of novel bioanalytical systems, including fluorescent and colorimetric DNA sensors. The operation presented herein would expand the scope of biomolecular circuit systems for diagnostic and therapeutic applications.

Оценка диагностических параметров сложных систем на водном транспорте на основе энтропийного подхода

The purpose of the work is to develop and improve the methods of computer modeling and digitalization of the choice of diagnostic parameters of complex technical systems to the maximum informativeness. The entropy method 
 of estimating the technical state of systems is based on the existence of a functional dependence of random variables characterizing the behavior of elements, which is always between two critical states: absolute integrity and complete additivity. The complexity of the interaction of elements in stochastic systems determines the need to search for effective algorithms based on fundamental entropy models and methods for finding the best solutions for monitoring and control. Models and algorithms for estimating the parameters of the technical condition and operational indicators of systems in the process of monitoring are improved on the MATLAB digital platform and its applications. It is noted that for systems of high complexity, a method of control is proposed, based on the entropic assessment of information as the most universal and applicable in technical, biological, economic, social and other systems that are difficult to analyze and formalize. It is shown that the entropic method of selecting the most informative control nodes in the design of diagnostic systems for technical means and technological processes at water transport facilities can significantly increase reliability and ensure the trouble-free operation of controlled facilities. The entropy method of estimating information content, based on the difference in entropies of the initial and subsequent states of the system, is used to solve specific problems given in the examples. An algorithm for ranking diagnostic parameters based on the method of pair comparisons is proposed, on the basis of which the minimax problem is solved: the maximum information content of the selected diagnosable nodes of the system is ensured with a minimum set of controlled parameters.

Designing Effective Instructional Feedback Using a Diagnostic and Visualization System: Evidence from a High School Biology Class

Although instructional feedback plays an essential role in regulating learning and improving performance, few studies have systematically investigated the needs of teachers and students for instructional feedback systems or developed designs and experiments, especially at the high school level. To address this research need, the present study investigated the needs of selected students and teachers in a high school in Hubei Province, China, and designed and developed a diagnostic visual feedback system for an experimental study with 125 students from a 10th-grade biology class in the same high school. The results showed that this diagnostic visual feedback report improved student performance (ES = 0.37) and that functions such as misconception location, knowledge diagnosis, and knowledge alert were well received by students. These findings have multiple implications for facilitating the design and development of diagnostic visual feedback systems.

Online Diagnostic Method and System Design of Arc Plasma Jet Characteristics Based on Wavelet Transform and Linear Regression Theory

Online Diagnostic Method and System Design of Arc Plasma Jet Characteristics Based on Wavelet Transform and Linear Regression Theory

Hybrid computer aided diagnostic system designs for screen film mammograms using DL‐based feature extraction and ML‐based classifiers

AbstractMammography is most popular imaging method used often in predicting breast cancer within women above age of 38 years. Various computer‐assisted algorithms have been employed for classifying breast masses as normal or malignant using screen film mammographic (SFM) images. In present research work, exhaustive experimentations have been carried out with nine deep learning‐based convolutional neural networks (CNNs) belonging to the three different categories of CNN architectures including (a) simple convolution‐based series models, that is VGG16 and VGG19 (b) simple convolution‐based directed acyclic graph (DAG) model, that is GoogleNet and (c) dilated convolution‐based DAG models, that is ResNet18, ResNet50, Inceptionv3, XceptionNet, ShuffleNet and MobileNet‐V2, for binary classification of the mammographic masses with SFM images. The experimental work has been carried out using 518 mammographic images taken from DDSM dataset with 208 images ϵ benign class and 310 images ϵ malignant class, respectively. The encoder‐decoder based semantic segmentation network model, that is ResNet50 has been used for the segmentation of mammographic masses from SFM images. The segmented masses images obtained from the ResNet50 model are subjected to classification experiments. To design a robust hybrid classifier design, that is, deep learning (DL)‐based feature extraction and machine learning (ML)‐based classification, the first step is to obtain an optimal DL‐based feature extractor for classification task. The optimal feature set obtained by the best performing CNN Model, that is, VGG 19 has been subjected to correlation‐based feature extraction and ML‐based classifiers including (i) adaptive neuro fuzzy classifier‐linguistic hedges (ii) principal component analysis‐ support vector machine classifier and (iii) GA‐SVM classifier to yield an optimal hybrid computer aided diagnostic (CAD) system design. It is found that hybrid CAD system using VGG19 as feature extractor and ANFC‐LH as classifier yields 96% the highest classification accuracy. The other performance parameters yield values, that is 96% sensitivity, 96% specificity, 96% F‐score, 96% precision and 92% MCC, which indicates a best prediction of binary classification. The test images which were misclassified by these hybrid CAD system designs were analysed subjectively by experienced participating radiologist. The results obtained by present work suggests that proposed hybrid CAD system with VGG19 Network model acting as feature extractor and ANFC‐LH classifier can be employed for the differential diagnosis of benign as well as malignant mammographic masses using SFM images in a routine clinical setting.

SCF‐ChemBio: Chemical Biology Tour de France

Chemical biology is a steadily growing field that has traditionally struggled to clearly define its boundaries in a short sentence. However, it can be stated that through the development of chemical and physicochemical tools, concepts and methods, chemical biology aims to address or stimulate biological questions at the molecular level in living organisms. Chemical biologists design and develop molecular tools that can probe or modulate biological processes, in order to understand their function, and sometimes to modify it for specific applications, but also to observe and analyze these tools in complex biological environments. Essentially positioned as a fundamental approach, chemical biology often remains very close to potential applications as it builds molecular objects capable of reacting to a significant biological stimulus. Chemical biology therefore finds natural development in fields such as health for the design of drugs and diagnostic systems or the environment for applications in crop science and ecology.

Optimization of the magnetic diagnostics for plasma equilibrium reconstruction in EAST long-pulse operation

Plasma equilibrium is critical for experimental operation and data analysis in tokamaks. The number and location of magnetic diagnostics provide the magnetic field and flux information that influences the accuracy of the plasma equilibrium reconstruction. In this research, the truncated singular value decomposition method is applied to minimize the number of magnetic diagnostics for plasma equilibrium reconstruction based on Experimental Advanced Superconducting Tokamak existing magnetic diagnostics system. The reconstructed results with a reduced number of magnetic diagnostics are consistent with results obtained using full magnetic diagnostics by EFIT code. This implies that the approach of minimizing the number of magnetic diagnostics in reconstruction can reduce the demand for magnetic diagnostics while ensuring the accuracy of plasma equilibrium reconstruction with feasibility and reliability. Reduced magnetic diagnostics can increase tolerance for the absence of certain existing magnetic diagnostics and could be a reference for the redundancy design of magnetic diagnostics system in future fusion devices.

Design and construction of Langmuir probe diagnostic system for determination of temperature, ion and electron density and potential in IR-IECF device

در این مقاله، هدف طراحی و ساخت سامانه تشخیصی پروب لانگمویر جهت اندازه ­گیری پارامترهای پلاسما در دستگاه گداخت به ­روش محصورسازی الکترواستاتیکی اینرسی ایران (IR-IECF) به­ منظور مشخصه­ یابی چشمه‌­ی پلاسمایی این دستگاه می‌­باشد. بدین‌­منظور، یک سامانه تشخیصی پروب لانگمویر برای اندازه‌­گیری پارامترهای مختلف پلاسما از جمله دمای الکترون­، چگالی الکترون، چگالی یون، و پتانسیل پلاسما طراحی و ساخته شده است. با در نظر گرفتن مشخصات چشمه پلاسمایی دستگاه IR-IECF، الکترود مورد استفاده برای پروب یک مفتول از جنس تنگستن با قطر و طول به ترتیب 3/0 و 5/1 میلی­ متر و محافظ آن لوله‌­ای سرامیکی از جنس آلومینا با قطر داخلی 7/0 سانتی‌­متر انتخاب شده است. جنس اتصلات و پایه نگه‌­دارنده پروب بر روی محفظه خلأ نیز استیل ضدزنگ می‌باشد. مدار الکتریکی پروب قادر است ولتاژ و جریان به ترتیب 500 ولت و 1 میلی ­آمپر را تأمین نماید. نتایج به ­دست آمده نشان می ­دهد در بین دو الکترود و در فاصله 25 میلی­ متری از الکترود بیرونی، دمای الکترون برابر باeV 1/50، چگالی الکترون برابر با 31/m 1015×1/3، پتانسیل پلاسما برابر با V 198 و چگالی یون برابر با 3 1/m1015×4/0 می ­باشد.

Development of the diagnostic tools for the COMPASS-U tokamak and plans for the first plasma

The COMPASS-U tokamak (R = 0.894 m, a = 0.27 m, Bt = 5 T, Ip = 2 MA) is a new medium-size device with fully metallic plasma facing components, currently under construction at the Institute of Plasma Physics of the Czech Academy of Sciences in Prague. It features a unique combination of parameters, such as a high temperature of the tokamak walls up to 500 °C allowing a high recycling regime, a high magnetic field connected with a high plasma density above 1020m−3 and with a high heat flux (perpendicular to divertor targets) density at the outer strike-point up to 90 MW/m2 in attached conditions. These parameters of the device pose strict constraints and requirements on the design of individual diagnostic systems. Strategy and present status of the development of the diagnostic systems for COMPASS-U are provided. Plans for a diagnostic set for the first plasma are reviewed. The review of the diagnostics systems involves the high-temperature compatible slow (up to 20 kHz) and fast (up to several MHz) inductive and non-inductive magnetic sensors (including Thick Printed Copper coils and Hall sensors), the sub-millimetre interferometer with an unambiguous channel, Electron Cyclotron Emission, the interlock and overview cameras, high resolution Thomson scattering, radiation diagnostics (neutron diagnostics, soft and hard X-ray diagnostics, bolometers, impurity monitors, effective ion charge), probe diagnostics (including rail probes) and manipulators.

What Are the Users’ Needs? Design of a User-Centered Explainable Artificial Intelligence Diagnostic System

The application of artificial intelligence (AI) systems has surged in the high-risk area of medicine, and these systems must explain their decisions to different users. However, existing explainable AI (XAI) design practices in the medical domain are mostly focused on domain experts, such as physicians, and there is a lack of XAI design practices for consumer users. Therefore, we developed a library of XAI user needs in the medical domain, which can be used as an auxiliary tool for the development of user-centered XAI design solutions in this domain. Moreover, through empirical research, based on our XAI user Needs Library, we designed an XAI-based electrocardiogram diagnostic system prototype for consumer users and conducted a user evaluation. The results provide the empirical experience of the design space of XAI and promote consumer user-centered XAI practices.

ПРИМЕНЕНИЕ ВЗВЕШЕННЫХ КОДОВ С СУММИРОВАНИЕМ ПРИ СИНТЕЗЕ СХЕМ ВСТРОЕННОГО КОНТРОЛЯ ПО МЕТОДУ ЛОГИЧЕСКОГО ДОПОЛНЕНИЯ

This work considers the application of Boolean complement method for the organization of self-checking circuits of built-in control for the devices synthesized on being Field-Programmable Gate Arrays. Review is given for the application of Boolean complement method while using various noise-resistant codes. The example is demonstrated for control circuit synthesis with Boolean complement method. Algorithm for control system synthesis by Boolean complement method with the use of weight-based sum codes by module M is formulated. As an example, weighted codes are considered with the summation of weight categories by module M = 3 and M = 4 for these purposes. The given codes have only two control categories that simplifies their application for task solution on the design of functional diagnostics system by Boolean complement method. The comparative analysis of both codes with their use in the systems with Boolean complement has been pursued. The application of Boolean complement method on the basis of weight-based sum codes for synthesis of discrete devices has been suggested.

Application of local Monte Carlo method in neutronics calculation of EAST radial neutron camera

The Local Monte Carlo (LMC) method is used to solve the problems of deep penetration and long time in the neutronics calculation of the radial neutron camera (RNC) diagnostic system on the experimental advanced superconducting tokamak (EAST), and the radiation distribution of the RNC and the neutron flux at the detector positions of each channel are obtained. Compared with the results calculated by the global variance reduction method, it is shown that the LMC calculation is reliable within the reasonable error range. The calculation process of LMC is analyzed in detail, and the transport process of radiation particles is simulated in two steps. In the first step, an integrated neutronics model considering the complex window environment and a neutron source model based on EAST plasma shape are used to support the calculation. The particle information on the equivalent surface is analyzed to evaluate the rationality of settings of equivalent surface source and boundary. Based on the characteristic that only a local geometric model is needed in the second step, it is shown that the LMC is an advantageous calculation method for the nuclear shielding design of tokamak diagnostic systems.

Determination of temperature distribution on windings of oil transformer based on the laws of heat transfer

Object of research: development of a technology for determining the temperature of the winding of a power oil transformer, in particular, the analysis of thermal processes in the winding of a power transformer during short-term overloads, taking into account the influence of the environment.
 Investigated problem: temperature distribution in the winding of a power oil transformer taking into account short-term load surges in the problem of assessing the residual life of the insulation of the transformer winding by temperature aging. The calculation of the temperature distribution in the winding was carried out using the passport data and characteristics of the power oil transformer, including the winding, transformer oil, load currents.
 Main scientific results: a mathematical model was calculated, with the help of which the results of temperature distribution in the transformer winding were obtained during short-term load surges or constant work with an increased load. According to the presented model, the analysis of the cooling time of the transformer winding after short-term overloads is carried out. Comparing the results obtained on the simulation model with the known results of experimental studies of the temperature distribution in the winding of a power transformer, the adequacy of the mathematical model is proved. It is shown that the use of the laws of heat transfer in a homogeneous plate to analyze the temperature distribution in the transformer winding is not wrong, but requires clarifications and simplifications.
 The area of practical use of the research results: enterprises of the machine-building industry and energy companies specializing in the production and operation of transformer equipment. Innovative technological product: simulation model of heat distribution in a transformer winding, which can take into account the load of the transformer, the effect of the environment on the insulation of the transformer windings.
 An innovative technological product: a method for diagnosing the duration of the non-failure operation of a transformer, which makes it possible to ensure trouble-free operation and save money for the repair of transformer equipment.
 Scope of application of the innovative technological product: design and development of diagnostic systems for windings of power oil transformers