Current Technique Research Articles

Analysis of Multicrystalline Si Solar Cell Improvement Using Laser-Beam-Induced Current Technique

Analysis of Multicrystalline Si Solar Cell Improvement Using Laser-Beam-Induced Current Technique

Inverter With Paralleled Modules to Extend Current Capacity and Combat Motor Overvoltage in SiC-Based Adjustable Speed Drives

This article proposes a quasi-three-level (Q3L) PWM-based module-parallel inverter to address two major issues of deploying SiC MOSFETs in high-power cable-fed adjustable speed drives. Firstly, the maximum output power of SiC inverters is limited due to the lack of high current rating SiC devices, which cannot satisfy the demand of high-power applications such as heavy-duty electric vehicles and more electric aeroplanes. The second issue comes from the fast-switching speed of SiC MOSFETs, where power cables act like transmission lines under steep rising/falling voltages (high <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$dv/dt$</tex-math></inline-formula> ), with back and forth voltage reflections that result in serious overvoltage oscillations at motor terminals. To address these issues, a SiC module-parallel inverter is adopted with elevated current capacity, where the phase voltage is maintained at the mid-point of a coupled inductor connected to the output nodes of each paralleled half-bridge module. By actively controlling the switching delay between the paralleled half-bridge-legs, Q3L PWM waveforms are generated at the inverter output nodes which can mitigate the motor overvoltage due to the voltage reflections through power cables. An active current control technique is also proposed to facilitate the current balance between the paralleled half-bridge-legs. The proposed Q3L PWM-based module-parallel inverter is experimentally verified using a SiC-based cable-fed motor drive system. The results show that the proposed approach can extend the current capabilities of SiC devices as well as mitigate the motor overvoltage, enabling the adoption of SiC devices in high-power motor drives.

Identification of congenital valvular murmurs in young patients using deep learning-based attention transformers and phonocardiograms.

One in every four newborns suffers from congenital heart disease (CHD) that causes defects in the heart structure. The current gold-standard assessment technique, echocardiography, causes delays in the diagnosis owing to the need for experts who vary markedly in their ability to detect and interpret pathological patterns. Moreover, echo is still causing cost difficulties for low-and middle-income countries. Here, we developed a deep learning based attention transformer model to automate the detection of heart murmurs caused by CHD at an early stage of life using cost-effective and widely available phonocardiography (PCG). PCG recordings were obtained from 942 young patients at four major auscultation locations, including the aortic valve (AV), mitral valve (MV), pulmonary valve (PV), and tricuspid valve (TV), and they were annotated by experts as absent, present, or unknown murmurs. A transformation to wavelet features was performed to reduce the dimensionality before the deep learning stage for inferring the medical condition. The performance was validated through 10-fold cross-validation and yielded an average accuracy and sensitivity of 90.23% and 72.41%, respectively. The accuracy of discriminating between murmurs' absence and presence reached 76.10% when evaluated on unseen data. The model had accuracies of 70%, 88%, and 86% in predicting murmur presence in infants, children, and adolescents, respectively. The interpretation of the model revealed proper discrimination between the learned attributes, and AV channel was found important (score > 0.75) for the murmur absence predictions while MV and TV were more important for murmur presence predictions. The findings potentiate deep learning as a powerful front-line tool for inferring CHD status in PCG recordings leveraging early detection of heart anomalies in young people. It is suggested as a tool that can be used independently from high-cost machinery or expert assessment. With additional validation on external datasets, more insights on the generalizability of deep learning tools could be obtained before being implemented in real-world clinical settings.

A Drain Current Extraction Technique Using Source Parasitic Resistance and Inductance in SiC Power MOSFETs

A Drain Current Extraction Technique Using Source Parasitic Resistance and Inductance in SiC Power MOSFETs

Monolithic integration of deep ultraviolet and visible light-emitting diodes for radiative sterilization application

The demand for effective sterilization methods, particularly in the wake of the Covid-19 pandemic, has sparked interest in the use of deep ultraviolet (DUV) radiation for disinfection. The high risk of skin/eye exposure to the high-energy DUV radiation requires the integration of DUV and visible (VIS) LED chips to sterilize and indicate its operation simultaneously in the portable sterilization devices. However, conventional double-chip integration suffers from high power consumption and fabrication complexity. This study sets out to explore the monolithic integration of DUV and VIS LEDs for the radiative sterilization application. This is accomplished by cascading AlGaN/AlGaN/AlGaN multiple quantum wells (QWs) and GaN/InGaN/GaN QWs through the compositional grading AlGaN cascade region. The inevitable overflown electrons from DUV QWs are deliberately introduced into the VIS QWs, allowing for the electron–hole recombination and the simultaneous emission of VIS light. Both experiment and simulation results confirm the feasibility of the proposed dual-wavelength LED integration. The proposed DUV&amp;VIS LED shows an external quantum efficiency and wall-plug efficiency of 2.03% and 1.54% at 40 mA, respectively. This study establishes a quantitative framework for the monolithic integration of DUV and VIS LEDs for radiative sterilization, which has the potential to replace the current technique of using discrete DUV and VIS double-chip configurations.

Integration of a facile sustainable resonance Rayleigh scattering switchable-based system for feasible determination of centrophenoxine, a nootropic and antioxidant agent; application to crude materials and dosage forms

The proposed research adheres to a certain methodology to ensure that the technique used for analyzing the centrophenoxine drug is sustainable and green. It is important to highlight that several tools that have been recently developed were utilized as potential indicators of environmental sustainability and applicability. The present research presents a novel and entirely innovative method utilizing ultrasensitive spectrofluorimetry for the detection of centrophenoxine (CPX) drug. The employed methodology in this study involved the utilization of one-step, one-pot, and direct spectrofluorimetric technique, which was found to be both efficient and environmentally sustainable in the validation and assessment of the drug. Simply, when CPX and erythrosine B reagent were combined in an acidic environment, the highly resonance Rayleigh scattering product was immediately produced. The sensitivity limits were observed to be within the range of 15–47 ng mL−1, whereas the linearity was assessed to be in the range of 50–2000 ng mL−1. The optimal settings for all modifiable parameters of the system were ascertained through an analysis of centrophenoxine-erythrosine B complexes. Moreover, the system demonstrated compliance with International Council for Harmonization (ICH) specifications without encountering any issues. The suggested process was then rated on different recent environmental safety measuring metrics to see how good it was for the environment. Fortunately, the WAC standards that combine ecological and functional elements utilizing the Green/Red/Blue (RGB 12) design also acclaimed the current analytical technique as a white one. Additionally, a new applicability evaluation tool (BAGI) was employed to estimate the practicability of the planned method in the analytical chemistry field.

New combined control strategy of on-board bidirectional battery chargers for electric vehicles

This paper aims to develop a bidirectional on-board battery charger for electric vehicles (EVs). The studied battery charger is composed of a bidirectional ac-dc converter as the first stage of conversion and a bidirectional dc-dc converter as the second stage. The first one is controlled by a predictive direct power control strategy based on a space vector modulation technique known as P-SVM-DPC, and the second is used to regulate the battery current and regulate the power direction flow by using a direct current control technique. The choice of its topology has taken into consideration the grid-to-vehicles (G2V) and vehicle-to-grid (V2G) power flow directions. During charging or discharging, the DC/DC converter acts likes a buck or boost converter. Using MATLAB/Simulink software, the performance of the battery charger is examined in various operating modes, such as fast charging and quick discharging.

Evaluation of spatial precision and accuracy of cone-beam CT using an in vitro phantom model.

High accuracy and precision are essential in stereotactic neurosurgery, as targeting errors can significantly affect clinical outcomes. Image registration is a vital step in stereotaxis, and understanding the error associated with different image registration methods is important to inform the choice of equipment and techniques in stereotactic neurosurgery. The authors aimed to quantify the test-retest reliability and stereotactic accuracy of cone-beam CT (CBCT) compared with the current clinical gold-standard technique (i.e., CT). Two anthropomorphic phantom models with 40 independent unique steel spheres were developed to compare CBCT frame and stereotactic space registration with the clinical gold standard (CT). The cartesian coordinates of each sphere were compared between the imaging modalities for test-retest reliability and overall accuracy. Both imaging modalities showed similar levels of fiducial deviation from the expected geometry. The equivalence test demonstrated mean differences between CT and CBCT registration of -0.082 mm (90% CI -0.27 to 0.11), -0.045 mm (90% CI -0.43 to 0.34), and -0.041 mm (90% CI -0.064 to 0.018) for coordinates in the x-, y-, and z-axes, respectively. The mean euclidean distance difference between the two modalities was 0.28 mm (90% CI 0.27-0.29). Accuracy and precision were comparable between CBCT and CT image registrations. These findings suggest that CBCT registration can be used as a clinically equivalent substitute to gold-standard CT acquisition.

An Open-Loop VCO-ADC Based on a Linearized Current Control Technique

An Open-Loop VCO-ADC Based on a Linearized Current Control Technique

G-ACP: a machine learning approach to the prediction of therapeutic peptides for gastric cancer

Conventional Gastrointestinal (GI) cancer treatments are quite expensive and have major hazards. Nowadays, a different strategy places more emphasis on creating tiny biologically active peptides that do not cause severe poisoning. Anticancer peptides (ACPs) are found through experimental screening, which is time-dependent and frequently fraught with difficulties. Gastric ACPs are emerging as a promising GI cancer treatment in the current day. It is crucial to identify novel gastric ACPs to have an improved knowledge of their functioning processes and treatment of gastric cancer. As a result of the post-genomic era’s massive production of peptide sequences, rapid and effective ACPs using a computational method are essential. Several adaptive statistical techniques for distinguishing ACPs and non-ACPs have recently been developed. A variety of adapted statistically significant methods have been developed to differentiate between ACPs and non-ACPs. Despite significant progress, there is no specific model for the prediction of gastric ACPs because the specific model will predict a particular type of peptide more accurately and quickly. To overcome this, an initiative is taken for the creation of a reliable framework for the accurate identification of gastric ACPs. The current technique in particular contains four possible features along with one hybrid feature encoding mechanisms which are the target-class motif previously indicated by Amino Acid Composition, Dipeptide Composition, Tripeptide Composition (TPC), Pseudo Amino Acid Composition (PAAC), and their Hybrid. Machine Learning algorithms make high-performance and accurate prediction tools. Moreover, highly variable and ideal deep feature selection is done using an ANOVA-based F score for feature pruning. Experiments on a range of algorithms are carried out to identify the optimal operating strategy due to the diverse nature of learning. Following analysis of the empirical results, Naïve Bayes with TPC and Hybrid feature space outperforms other methods with 0.99 accuracy score on the testing dataset. To find the model generalization an external validation is carried out. In external datasets, the Extra Trees with PAAC features outperforms with the accuracy of 0.94. The comparison study shows that our suggested model will predict gastric ACPs more accurately and will be useful in drug development and gastric cancer. The predictive model can be freely accessed at https://github.com/humeraazad10/G-ACP.git. Communicated by Ramaswamy H. Sarma

Contralateral C7 Nerve Transfer

Complex brachial plexus injuries with multiple or complete root avulsions make intraplexic reconstruction impossible in some cases. Such cases necessitate the use of extraplexic nerve donors such as the spinal accessory nerve or intercostal nerves. The contralateral C7 root represents a donor with a high axon count and can be used as an axon source in such cases. We summarise current indications, surgical technique and functional results after a contralateral C7 transfer in cases of brachial plexus injury, describing some of our own cases and including a selective literature review.

Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models.

The World Health Organization has highlighted that cancer was the second-highest cause of death in 2019. This research aims to present the current forecasting techniques found in the literature, applied to predict time-series cancer incidence and then, compare these results with the current methodology adopted by the Instituto Nacional do Câncer (INCA) in Brazil. A set of univariate time-series approaches is proposed to aid decision-makers in monitoring and organizing cancer prevention and control actions. Additionally, this can guide oncological research towards more accurate estimates that align with the expected demand. Forecasting techniques were applied to real data from seven types of cancer in a Brazilian district. Each method was evaluated by comparing its fit with real data using the root mean square error, and we also assessed the quality of noise to identify biased models. Notably, three methods proposed in this research have never been applied to cancer prediction before. The data were collected from the INCA website, and the forecast methods were implemented using the R language. Conducting a literature review, it was possible to draw comparisons previous works worldwide to illustrate that cancer prediction is often focused on breast and lung cancers, typically utilizing a limited number of time-series models to find the best fit for each case. Additionally, in comparison to the current method applied in Brazil, it has been shown that employing more generalized forecast techniques can provide more reliable predictions. By evaluating the noise in the current method, this research shown that the existing prediction model is biased toward two of the studied cancers Comparing error results between the mentioned approaches and the current technique, it has been shown that the current method applied by INCA underperforms in six out of seven types of cancer tested. Moreover, this research identified that the current method can produce a biased prediction for two of the seven cancers evaluated. Therefore, it is suggested that the methods evaluated in this work should be integrated into the INCA cancer forecast methodology to provide reliable predictions for Brazilian healthcare professionals, decision-makers, and oncological researchers.

Reconstructing 3D De-Blurred Structures from Limited Angles of View through Turbid Media Using Deep Learning

Recent studies in transillumination imaging for developing an optical computed tomography device for small animal and human body parts have used deep learning networks to suppress the scattering effect, estimate depth information of light-absorbing structures, and reconstruct three-dimensional images of de-blurred structures. However, they still have limitations, such as knowing the information of the structure in advance, only processing simple structures, limited effectiveness for structures with a depth of about 15 mm, and the need to use separated deep learning networks for de-blurring and estimating information. Furthermore, the current technique cannot handle multiple structures distributed at different depths next to each other in the same image. To overcome the mentioned limitations in transillumination imaging, this study proposed a pixel-by-pixel scanning technique in combination with deep learning networks (Attention Res-UNet for scattering suppression and DenseNet-169 for depth estimation) to estimate the existence of each pixel and the relative structural depth information. The efficacy of the proposed method was evaluated through experiments that involved a complex model within a tissue-equivalent phantom and a mouse, achieving a reconstruction error of 2.18% compared to the dimensions of the ground truth when using the fully convolutional network. Furthermore, we could use the depth matrix obtained from the convolutional neural network (DenseNet-169) to reconstruct the absorbing structures using a binary thresholding method, which produced a reconstruction error of 6.82%. Therefore, only one convolutional neural network (DenseNet-169) must be used for depth estimation and explicit image reconstruction. Therefore, it reduces time and computational resources. With depth information at each pixel, reconstruction of 3D image of the de-blurred structures could be performed even from a single blurred image. These results confirm the feasibility and robustness of the proposed pixel-by-pixel scanning technique to restore the internal structure of the body, including intricate networks such as blood vessels or abnormal tissues.

Zirconium oxide thin films deposited via DC magnetron sputtering: corrosion and morphological investigation

Purpose The purpose of this study is to investigate the structural, surface roughness and corrosion properties of the zirconium oxide thin films deposited onto SS304 substrates using the direct current (DC) magnetron sputtering technique. Design/methodology/approach DC sputtering at different powers – 80, 100 and 120 W – was used to deposit ZrO2 thin films onto different substrates (Si/SS304) without annealing of the substrate. Atomic force microscope (AFM), energy-dispersive X-ray spectroscopy (EDS), Tafel extrapolation and contact angle techniques were applied to investigate the surface roughness, chemical compositions, corrosion behavior and hydrophobicity of these films. Findings Results showed that the thickness of the deposited film increased with power increase, while the corrosion current decreased with power increase. AFM images indicated that the surface roughness decreased with an increase in DC power. EDS analysis showed that the thin film has a stoichiometric ZrO2 (Zr:O 1:2) composition with basic uniformity. Water contact angle measurements indicated that the hydrophobicity of the synthesized films decreased with an increase in surface roughness. Originality/value DC magnetron sputtering technique is infrequently used to deposition thin films. The obtained thin films showed good hydrophobic and anticorrosion properties. Finally, results are compared with other deposition techniques.

An early discovery of intrusion attack using novel optimized deep learning for internet of things

In the past couple of years, neural networks have gained widespread use in network security analysis. This type of analysis is usually performed in a nonlinear and highly correlated manner. Due to the immense amount of data traffic, the current models are prone to false alarms and poor detection. Deep-learning models can help security researchers identify and extract data features that are related to an attack. They can also minimize the data’s dimensionality and detect intrusions. Unfortunately, the complexity of the network structure and hidden neurons of a deep-learning model can be set by error-prone procedures. In order to improve the performance of deep learning models, a new algorithm is proposed. This method combines a gradient boost regression and particle swarm optimization. The proposes a method called the Spark-DBN-SVM-GBR algorithm. The simulations conducted proposed algorithm revealed that it has a better accuracy rate than other deep learning models and the experiments conducted on the PSO-GBR algorithm revealed that it performed better than the current optimization technique when detecting unauthorized attack activities.

An enhanced predictive current control technique for interior permanent magnet synchronous motor drives with extended voltage space vectors for electric vehicles

SummaryPermanent magnet synchronous motors (PMSM) are widely employed in the application of electric vehicles (EVs) due to their simplicity of operation. Model predictive current control (MPCC) is an advanced technique used to control the PMSM owing advantages like multi‐variable cost function and good dynamic performance. Cost function of predictive current control (PCC) does not require the flux weighting factor; hence, it is simple in the selection of suitable voltage vector (VV). The conventional PCC (C‐PCC) applied to interior PMSM (IPMSM) contains more torque and flux ripples as it includes only one set of voltage vectors for all speed ranges. In proposed PCC (P‐PCC), the magnitude and location of voltage vectors is to be selected as large and small VVs to reduce torque ripples. The P‐PCC in this article utilizes two set of extended voltage vectors (large and small VVs) based on the applied speed change in dynamic conditions and hence reduces the ripple content. Incorporating maximum torque per ampere (MTPA) control in this article serves the purpose of optimizing the machine performance. By adding MTPA to the proposed method, it is aimed to enhance the machine performance with less ripples and improved torque response. Simulation results are presented for conventional and P‐PCC to highlight the effectiveness and efficacy of the P‐PCC. The P‐PCC is experimentally verified with 3.7 kW PMSM using d‐SPACE controller.

Investigation of the thermal performance of a spiral fin-and-tube heat exchanger by numerical methods

Spiral fin-and-tube heat exchangers (SPF HEX) are broadly employed heat transfer (HT) equipment for heating and cooling applications. Therefore, developing a model that predicts the HT characteristics could provide an advantage when selecting the required device considering operation conditions. The efficacy of the HT characteristics of a three-dimensional SPF HEX was examined under various boundary conditions with the help of a commercial computational fluid dynamics (CFD) solver and predicted by employing machine learning (ML) approaches as a rarely studied novel work. Three-dimensional, steady, and incompressible Reynolds-Averaged Navier-Stokes (RANS) formulas, including continuity, momentum and energy were solved to obtain numerical data. k-ε Realizable turbulence model, which has two additional transport equations, was employed. The outlet temperature of water was calculated at various water inlet velocity, air inlet velocity, and air inlet temperature with a constant water inlet temperature. First, the methods of CFD were utilized to obtain sixty cases, then the outlet temperatures of water were obtained at various conditions. The current CFD technique was verified with a work in literature. After collecting a labeled dataset, this data is fed into four recognized ML algorithms: Support Vector Machine (SVM), Linear Regression (LR), Decision Tree Regression (DT), and Random Forest (RF). In the presented results, the SVM algorithm achieved the highest accuracy rate with values of 0.1765 mean squared error (MSE), 0.3294 mean absolute error (MAE), and 0.9940 R-squared (R2). Furthermore, these results indicate that ML algorithms can deliver high prediction performance, potentially reducing the need for many experimental and numerical studies.

Optimizing Current Injection Technique for Enhancing Resistivity Method

Geo-electrical resistivity methods are widely used in various fields and have significant applications in scientific and practical research. Despite the widespread use of resistivity methods, current injection is a critical step in the process of resistivity methods, and the quality of current injection significantly impacts the accuracy of the resistivity measurements. One primary challenge is optimizing current injection techniques to enhance resistivity methods. The developed current injector model for the resistivity meter instrument enhances performance by increasing the voltage source to 400 Volts, extending measurement coverage. It provides three injection current options, 0.5A, 0.8A, and 1A, for efficient accumulator use, considering electrode distances and estimating earth resistance using Contact Resistance Measurement (CRM) to estimate the earth resistance. CRM mode ensures proper electrode connection before injection, thus improving measurement efficiency. The embedded TTGO LoRa ESP32 SX1276 facilitates wireless communication over 1.5 km, addressing challenges in remote and internet-limited areas. The model demonstrates reliability, validity, and durability in CRM mode and current injection measurement. Regarding reliability, we determine the relative error of the model by carrying out measurements repeatedly. In lab-scale testing, the average Relative Error in CRM mode is 0.65%, and in earth resistance measurement testing, it is 1.58%. These relative errors are below the 2% maximum error applied in the “Supersting”, a commercial resistivity instrument. The model's validity is defined by comparing the model with the measuring instrument; we have absolute error. In lab scale testing, the average Absolute Error in CRM mode is 3.08%, and in earth resistance measurement testing, it is 3.73%. The model's durability is tested by injecting current for a minute. After one minute of current injection, the power resistor component's temperature is stable at 30°C.

A Dynamic Voltage-Based Current Estimation Technique for DC Motor Speed Control Applications

A Dynamic Voltage-Based Current Estimation Technique for DC Motor Speed Control Applications

Alternative Performance Indices-Based Control Technique for a Unity Power Factor Three-Phase Rectifier

The implementation of a unity power factor (UPF) three-phase rectifier has the potential to enhance the power factor (PF). However, the PF and total harmonic distortion (THD) experience degradation in low-output regions due to the utilization of a “critical input inductor” under rated load conditions. In this study, an analysis of the operation principle of a UPF three-phase rectifier is conducted and a reference compensation current technique based on alternative performance indices is proposed. The optimal control algorithm is utilized to calculate the single harmonic phase delay of each phase and determine the compensator gain, taking into account factors such as the single harmonic distortion limit, THD limit, PF, and active power consumption. The output of the current compensator serves as the desired current source, which in turn drives the bidirectional switches. The simulation and experimental results demonstrate that the proposed control technique can significantly mitigate power supply current distortion, and the input power factor has been effectively improved within a wider load range.