Non-destructive evaluation of material hardness using micromagnetic sensors and complex neural networks

Introduction: Quality assurance and Quality monitoring is much needed in hospitals because it helps in rapid advancement in healthcare sector. In order to provide patient-oriented services, health systems have changed how treatment is delivered in recent years. Patients are now the primary emphasis, and new organisational models have been implemented. It is challenging for a healthcare professional to change a patient's perception of the quality of care because a healthcare institution is very tough to quantify. Understanding and defining patient satisfaction will make it easier to create an evaluation process and helps organisation to take useful measurements of the factors that affect patient satisfaction. "The degree to which a patient's expectations for their medical experience and their appraisal of the quantity of care they actually receive line up, according to the definition of patient satisfaction". Another good experience of patient there should be minimum waiting time. Long wait times for patients to be seen area significant issue hospitals may have. This can lead to patient dissatisfaction and affect a hospital attempts to uphold its excellent reputation in the community. Aims and Objectives: This study aims at evaluating quality assurance and quality monitoring in the PET-CT by Analysing Waiting Time, Investigating the nature and potential value of praises, suggestions, and complaints made to analyse patient satisfaction index. Methodology: It is a quantitative retro prospective study Retrospective data collected from January 2022 to June 2022, Prospective data collected for the month of July 2022, which aims at quality assurance and Quality monitoring in PET-CT department by analysing-Patient Waiting Time-Collection of data started with visiting respective department and observing the Patient's Arrival time, patient procedure start time and procedure out time. Patient satisfaction index-For calculating Patient satisfaction Index, Collection of feedback forms January to July which is filled by patients. Results: From above, outcomes results Recommendations and preventive measures has been plotted and Root cause analysis tool is also applied and plotted fishbone diagrammed for the respective department to increase patient satisfaction, enhance patient safety and to decrease the waiting time. By Fishbone Diagrams and value stream mapping shows factors like Environment, Equipments, Patients of the departments affects in increasing and decreasing waiting time. RCA done on patient satisfaction index which shows some criteria there should be training of staff on educating patient correctly. Conclusion: Factors that are obtained from Fishbone Analysis and value stream mapping are needed to be worked upon so that Patient satisfaction can be enhanced. Staff should have communication with patients and their relatives about rescheduling cancellation appointment. Also, it tells whether staff passed every information about procedure to patient correctly. There should be proper hand hygiene training for staff of respective department. Listening to the patient observing the patient behaviour, communication skills of the staff etc all are an important for maintaining long term relationship with the patient.

The goal of this study is to increase target identification accuracy and defect detection performance using the enhanced YOLO-V7 target defect detection algorithm. You Only Look Once version 7 is referred to as YOLO-V7 and is a well-liked real-time target identification technique. To make high-quality goods, however, it is essential to find flaws, and the traditional YOLO-V7 has certain restrictions when it comes to addressing specific flaws. To get around these restrictions, we implemented a number of changes to YOLO-V7. The study's enhanced YOLO-V7 target defect detection algorithm may find use in the areas of industrial automation, quality assurance, and safety monitoring.

Structural health monitoring (SHM) systems have been developed to evaluate structural responses to extreme events such as natural and man-made hazards. Additionally, the increasing volume of users and vehicle sizes can lead to the sudden damage and collapse of bridge structures. Hence, structural monitoring and dynamic characteristic analyses of bridge structures are critical and fundamental requirements for bridge safety. SHM can overcome the weaknesses of visual inspection practices, such as lack of resolution. However, because of computational limitations and the lack of data analysis methods, substantial quantities of SHM data have been poorly interpreted. In this paper, the SHM of bridges based on dynamic characteristics is used to assess the "health state" of bridge structures. A comprehensive SHM system using vibration-based techniques and modal identification for bridge structures are well defined. Some advanced concepts and applications regarding bridge safety evaluation methods, including damage detection and load-carrying capacity, are reviewed. For the first time, the pros and cons of each vibration technique are comprehensively evaluated, providing an advantage to the authority or structural owner when developing a bridge management database. This information can then be used for continuous structural monitoring to access and predict the bridge structure condition.

Ultrasound-based structural health monitoring methodology employing active and passive techniques

Structural health monitoring is a critical requirement in many composites. Numerous monitoring strategies rely on measurements of temperature or strain (or both), however these are often restricted to point-sensing or to the coverage of small areas. Spatially-continuous data can be obtained with optical fiber sensors. In this work, we report high-resolution distributed Brillouin sensing over standard fibers that are embedded in composite structures. A phase-coded, Brillouin optical correlation domain analysis (B-OCDA) protocol was employed, with spatial resolution of 2 cm and sensitivity of 1 °K or 20 micro-strain. A portable measurement setup was designed and assembled on the premises of a composite structures manufacturer. The setup was successfully utilized in several structural health monitoring scenarios: (a) monitoring the production and curing of a composite beam over 60 h; (b) estimating the stiffness and Young’s modulus of a composite beam; and (c) distributed strain measurements across the surfaces of a model wing of an unmanned aerial vehicle. The measurements are supported by the predictions of structural analysis calculations. The results illustrate the potential added values of high-resolution, distributed Brillouin sensing in the structural health monitoring of composites.

Corrosion characterisation of coated steel involves various approaches via Non- destructive Testing (NDT) and Structural Health Monitoring (SHM) techniques. This paper evaluates and discusses the application of the recent development of a portable lamp-pulsed thickness tester and Commercial Off-the-Shelf (COTS) ultra-high frequency (UHF) radio frequency identification (RFID) sensing. The findings affirm the efficacy of the portable lamp-pulsed thickness tester and COTS UHF RFID sensing for corrosion characterization. Furthermore, these methods are compared with other NDT&E and SHM techniques such as pulsed eddy current (PEC), eddy current pulsed thermography (ECPT), low frequency (LF) RFID, and chipless RFID sensing and monitoring. This study compares the principles, merits, limitations, and accuracy of different corrosion characterization approaches and discusses their future development.

Non destructive testing of civil engineering infrastructures is becoming of primary importance for their diagnosis, residual time life estimation and/or structural health monitoring. A particularity of civil engineering application is the large size of the survey zones and the expected low cost of inspection. In this context non contact ultrasonics may offer the possibility to built robots that can automatically scan large areas (or eventually be integrated in moving vehicles) to recover mechanical properties of material or to perform imagery for geometrical information recovery. In this paper we present two possible applications of in situ laser ultrasonics : one is the detection of voids in tendon duct with the impact echo method, the other is the use of surface waves to recover mechanical properties of the first centimetres of concrete structures (here after called cover concrete).

This paper is an extended paper from the 24th International Conference on Mechatronics Technology, ICMT 2021. The basic mechanical characteristic that gauges the stiffness of a solid material is known as the Young’s modulus. To evaluate the Young’s modulus, destructive material testing is frequently used. This paper describes how to determine a material’s dynamic Young’s modulus using Digital Image Correlation (DIC) in conjunction with numerical back-analysis. Three different materials (brass, aluminum, and steel) were examined for their static and dynamic reactions. A static transverse displacement was first applied at the free end of the beam before it was released and the beam was allowed to vibrate freely. The resulting vibrations at the free end were monitored using the DIC method, following which the natural frequencies of the beam were derived by applying the Fast Fourier Transform (FFT) to the DIC measured time history. The Young’s modulus corresponding to the fundamental natural frequency of the beam was then obtained via modal back-analysis using the finite element program Ansys 2022 R1. In this way, the Young’s modulus of the material may be calculated using a combination of numerical and DIC techniques, thus allowing for the non-contact evaluation of the structural integrity without subjecting the material to destructive testing. Potential applications of this method include bridge and building assessments, and structural health monitoring (SHM).

Several industries use non-destructive testing (NDT) methods to check the properties of parts and structures for flaws and oddities without affecting the part's stability or causing damage to the part being checked. It involves surveillance. Situation of the materials (Material State Awareness (MSA)) and the structural health monitoring (SHM). Using NDT procedures can help eliminate potential losses and risks associated with component failures while reducing both money and time by not sacrificing its reliability and upcoming utilization. On the other hand, machine learning (ML) and artificial intelligence (AI) techniques are practical instruments that can aid in automated data collection and analysis, offering fresh insights and perhaps enhancing detection efficiency at a high cost quickly and easily. The present research provides an overview of modern AI-ML NDT algorithms and their applications of associated with smart technologies, such as digital twins and machine vision (MV).

The use of robotics is beginning to play a key role in automating the data collection process in Non Destructive Testing (NDT). Increasing the use of automation quickly leads to the gathering of large quantities of data, which makes it inefficient, perhaps even infeasible, for a human to parse the information contained in them. This paper presents a solution to this problem by making the process of NDT data acquisition an autonomous one as opposed to an automatic one. In order to achieve this, the robotic data acquisition task is treated as an optimisation problem, where one seeks to find locations with the highest indication of damage. The resulting algorithm combines damage detection technology from the field of data-driven Structural Health Monitoring (SHM) with novel ideas in uncertainty quantification which enable the optimisation routine to be probabilistic. The algorithm is sequential; a decision is made at every iteration regarding the next optimal physical location for making an observation. This is achieved by modelling a two-dimensional field of novelty indices across a part/structure which is derived from a robust outlier analysis procedure. The value of this autonomous approach is that the output is not only measured data, but the most desirable information from an NDT inspection – the probability that a component contains damage. Furthermore, the algorithm also minimises the number of observations required, thus minimising the time and cost of data gathering.

This paper presents a design of Ka-Band microstrip patch antenna array for structural health monitoring (SHM) applications. Following a successful SHM system for wind turbine blades, the antenna is developed for the frequency band 33.4 GHz – 36 GHz. Horn antennas in the current system could be replaced with microstrip patch array for better integration, possible radome utility, smaller dimensions with similar antenna gain, and lower signal loss due to interconnects. The array is constructed from 4x4 radiating elements, probe fed with capacitive coupling. This approach assures dual-polarization capabilities in the future in contrast to standard aperture approaches. A novelty is also the introduction of integrated cavities, which improve antenna bandwidth and patterns. Simulation results show relative impedance bandwidth (return loss better than 15 dB) of 13.1 %, at resonance frequency f o = 35 GHz. Gain is about 17.3 dBi, and side-lobe level (SLL) is lower than -15 dB on E-plane (ϕ = 0°).

Quality Assurance Monitoring of a Citywide Transportation Protocol Improves Clinical Indicators of Intravenous Tissue Plasminogen Activator Therapy: A Community-based, Longitudinal Study

In this paper an ultra high bandwidth signal generation MMIC is presented. Based on an fundamental oscillator working at 75 GHz followed by a two stage frequency quadrupler, an output frequency range from 231.2-309.1 GHz is achieved, resulting in an overall tuning range of almost 78 GHz. The maximum output power of the transmitter MMIC is -2.8 dBm with a power variation of 7.5 dB over the complete 78 GHz tuning range. In combination with a low phase noise of approx. -75 dBc/Hz @ 1 MHz offset of the 300 GHz output signal, various high resolution distance measurements or imaging applications especially in the field of industrial quality assurance or in-line process monitoring become possible. With a power consumption of the complete MMIC of 700 mW at a single supply voltage of 5 V the realization of low-power sensors for industrial use can be achieved. The use of the well-established B11HFC SiGe BiCMOS technology provided by Infineon Technologies AG allows a reliable and cost-effective production as well.

Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open challenge in computer vision. Several recent approaches have tried to tackle this problem in an end-to-end fashion. In this paper, we deviate from recent works, and advocate a two-step approach where feature learning and clustering are decoupled. First, a self-supervised task from representation learning is employed to obtain semantically meaningful features. Second, we use the obtained features as a prior in a learnable clustering approach. In doing so, we remove the ability for cluster learning to depend on low-level features, which is present in current end-to-end learning approaches. Experimental evaluation shows that we outperform state-of-the-art methods by large margins, in particular \(+26.6\%\) on CIFAR10, \(+25.0\%\) on CIFAR100-20 and \(+21.3\%\) on STL10 in terms of classification accuracy. Furthermore, our method is the first to perform well on a large-scale dataset for image classification. In particular, we obtain promising results on ImageNet, and outperform several semi-supervised learning methods in the low-data regime without the use of any ground-truth annotations. The code is available at www.github.com/wvangansbeke/Unsupervised-Classification.git.KeywordsUnsupervised learningSelf-supervised learningImage classificationClustering

As the design, construction and maintenance of civil structures continue to evolve, it is becoming imperative that these structures be monitored for their health. To meet this need, the discipline of Civil Structural Health Monitoring (CSHM) has emerged. It involves the new subject Civionics that integrates the civil structures and electronics and aims to assist civil engineers in realizing the full benefits of structural health monitoring. Against this background, a new Journal of Civil Structural Health Monitoring (JCSHM) has recently been established by ISHMII and Springer. The aim of the journal is to advance the understanding and the application of Structural Health Monitoring (SHM) and Civionics in civil engineering infrastructure. The journal on CSHM has articles that address performance monitoring for structures with strain, load, temperature, deformation, vibration and environmental measurements with structural modeling for load capacity ratings and service life estimation. The journal, also, addresses integrating traditional visual assessment and field/laboratory destructive testing programs with the state-of-the-art internal condition assessment through nondestructive evaluation of concrete, steel, fiber reinforced polymers, masonry and wood structures. Articles based on original basic research as well as novel applications to civil infrastructure systems including highways bridges, railway bridges, pipelines and buildings, are included. Range of topics cover following subjects: sensors, instrumentation, model validation, system identification, monitoring strategies, data mining, data management, interpretation of data for decision making processes.