An enhanced gain non-isolated quadratic boost DC-DC converter with continuous source current.

In this paper, a non-isolated quadratic boost DC-DC converter has been proposed. The proposed converter provides high output voltage gain with a lower component count on the structure. In addition, the input side inductor provides continuous source current and the output voltage is positive. Since the proposed topology possesses the continuous source current, it simplifies the filter design at the input side further making the converter suitable for photovoltaic applications. Another important feature of this converter includes the utilization of the same switch ground that omits the additional control power supply in the system design. The detailed mathematical modeling of the proposed topology including the steady state analysis for different modes of operations, voltage stress calculations of the components, and power loss calculations have been precisely demonstrated in this work. The simulation has been carried out in Matlab/Simulink software. Finally, a 250 W experimental prototype has been developed and tested in the laboratory environment and the peak efficiency of the proposed topology has been found 92% at 50% duty cycle, which validates the correctness of the theoretical and simulation outcomes of the proposed work.

Failure Severity Prediction for Protective-Coating Disbondment via the Classification of Acoustic Emission Signals.

Structural health monitoring is a popular inspection method that utilizes acoustic emission (AE) signals for fault detection in engineering infrastructures. Diagnosis based on the propagation of AE signals along any surface material offers an attractive solution for fault identification. However, the classification of AE signals originating from failure events, especially coating failure (coating disbondment), is a challenging task given the AE signature of each material. Thus, different experimental settings and analyses of AE signals are required to classify the various types of coating failures, and they are time-consuming and expensive. Hence, to address these issues, we utilized machine learning (ML) classification models in this work to evaluate epoxy-based-protective-coating disbondment based on the AE principle. A coating disbondment experiment consisting of coated carbon steel test panels for the collection of AE signals was implemented. The obtained AE signals were then processed to construct the final dataset to train various state-of-the-art ML classification models to divide the failure severity of coating disbondment into three classes. Consequently, methods for the extraction of useful features, the handling of data imbalance, and a reduction in the bias of ML models were also effectively utilized in this study. Evaluations of state-of-the-art ML classification models on the AE signal dataset in terms of standard metrics revealed that the decision forest classification model outperformed the other state-of-the-art models, with accuracy, precision, recall, and F1 score values of 99.48%, 98.76%, 97.58%, and 98.17%, respectively. These results demonstrate the effectiveness of utilizing ML classification models for the failure severity prediction of protective-coating defects via AE signals.

Wearable Two-Dimensional Nanomaterial-Based Flexible Sensors for Blood Pressure Monitoring: A Review.

Flexible sensors have been extensively employed in wearable technologies for physiological monitoring given the technological advancement in recent years. Conventional sensors made of silicon or glass substrates may be limited by their rigid structures, bulkiness, and incapability for continuous monitoring of vital signs, such as blood pressure (BP). Two-dimensional (2D) nanomaterials have received considerable attention in the fabrication of flexible sensors due to their large surface-area-to-volume ratio, high electrical conductivity, cost effectiveness, flexibility, and light weight. This review discusses the transduction mechanisms, namely, piezoelectric, capacitive, piezoresistive, and triboelectric, of flexible sensors. Several 2D nanomaterials used as sensing elements for flexible BP sensors are reviewed in terms of their mechanisms, materials, and sensing performance. Previous works on wearable BP sensors are presented, including epidermal patches, electronic tattoos, and commercialized BP patches. Finally, the challenges and future outlook of this emerging technology are addressed for non-invasive and continuous BP monitoring.

Recent Advances in Nondestructive Method and Assessment of Corrosion Undercoating in Carbon-Steel Pipelines.

Carbon-steel pipelines have mostly been utilized in the oil and gas (OG) industry owing to their strength and cost-effectiveness. However, the detection of corrosion under coating poses challenges for nondestructive (ND) pipeline monitoring techniques. One of the challenges is inaccessibility because of the pipeline structure, which leads to undetected corrosion, which possibly leads to catastrophic failure. The drawbacks of the existing ND methods for corrosion monitoring increase the need for novel frameworks in feature extraction, detection, and characterization of corrosion. This study begins with the explanations of the various types of corrosion in the carbon-steel pipeline in the OG industry and its prevention methods. A review of critical sensors integrated with various current ND corrosion monitoring systems is then presented. The importance of acoustic emission (AE) techniques over other ND methods is explained. AE data preprocessing methods are discussed. Several AE-based corrosion detection, prediction, and reliability assessment models for online pipeline condition monitoring are then highlighted. Finally, a discussion with future perspectives on corrosion monitoring followed by the significance and advantages of the emerging AE-based ND monitoring techniques is presented. The trends and identified issues are summarized with several recommendations for improvement in the OG industry.

Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach.

Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed.

Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier.

Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.

Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates.

Damage assessment is a key element in structural health monitoring of various industrial applications to understand well and predict the response of the material. The big uncertainty in carbon fiber composite materials response is because of variability in the initiation and propagation of damage. Developing advanced tools to design with composite materials, methods for characterizing several damage modes during operation are required. While there is a significant amount of work on the analysis of acoustic emission (AE) from different composite materials and many loading cases, this research focuses on applying an unsupervised clustering method for separating AE data into several groups with distinct evolution. In this paper, we develop an adaptive sampling and unsupervised bivariate data clustering techniques to characterize the several damage initiations of a composite structure in different lay-ups. An adaptive sampling technique pre-processes the AE features and eliminates redundant AE data samples. The reduction of unnecessary AE data depends on the requirements of the proposed bivariate data clustering technique. The bivariate data clustering technique groups the AE data (dependent variable) with respect to the mechanical data (independent variable) to assess the damage of the composite structure. Tensile experiments on carbon fiber reinforced composite laminates (CFRP) in different orientations are carried out to collect mechanical and AE data and demonstrate the damage modes. Based on the mechanical stress-strain data, the results show the dominant damage regions in different lay-ups of specimens and the definition of the different states of damage. In addition, the states of the damage are observed using Scanning Electron Microscope (SEM) analysis. Based on the AE data, the results show that the strong linear correlation between AE and mechanical energy, and the classification of various modes of damage in all lay-ups of specimens forming clusters of AE energy with respect to the mechanical energy. Furthermore, the validation of the cluster-based characterization and improvement of the sensitivity of the damage modes classification are observed by the combined knowledge of AE and mechanical energy and time-frequency spectrum analysis.

An enhanced dynamic transmission opportunity scheme to support varying traffic load over wireless campus networks.

Transmission opportunity (TXOP) is a key factor to enable efficient channel bandwidth utilization over wireless campus networks (WCN) for interactive multimedia (IMM) applications. It facilitates in resource allocation for the similar categories of multiple packets transmission until the allocated time is expired. The static TXOP limits are defined for various categories of IMM traffics in the IEEE802.11e standard. Due to the variation of traffic load in WCN, the static TXOP limits are not sufficient enough to guarantee the quality of service (QoS) for IMM traffic flows. In order to address this issue, several existing works allocate the TXOP limits dynamically to ensure QoS for IMM traffics based on the current associated queue size and pre-setting threshold values. However, existing works do not take into account all the medium access control (MAC) overheads while estimating the current queue size which in turn is required for dynamic TXOP limits allocation. Hence, not considering MAC overhead appropriately results in inaccurate queue size estimation, thereby leading to inappropriate allocation of dynamic TXOP limits. In this article, an enhanced dynamic TXOP (EDTXOP) scheme is proposed that takes into account all the MAC overheads while estimating current queue size, thereby allocating appropriate dynamic TXOP limits within the pre-setting threshold values. In addition, the article presents an analytical estimation of the EDTXOP scheme to compute the dynamic TXOP limits for the current high priority traffic queues. Simulation results were carried out by varying traffic load in terms of packet size and packet arrival rate. The results show that the proposed EDTXOP scheme achieves the overall performance gains in the range of 4.41%-8.16%, 8.72%-11.15%, 14.43%-32% and 26.21%-50.85% for throughput, PDR, average ETE delay and average jitter, respectively when compared to the existing work. Hence, offering a better TXOP limit allocation solution than the rest.