Conventional Methods Research Articles

Noninvasive Online Capacitor Monitoring Method for Three-Level Converter Based on Active Neutral-Point Current Adjustment

Capacitor monitoring is one of the effective measures to assess the health status of power converters. Although some conventional methods have been proposed, they require adding auxiliary current sensors, changing operation conditions, or deteriorating the output performance. The lack of noninvasive online capacitor monitoring methods is a great problem in an AC/DC/AC system. To address this urgent need, this article proposes a truly noninvasive online capacitor monitoring method specifically for three-level neutral-point-clamped converters. The word “noninvasive” means no additional sensors, no changes to operation conditions, and little influence on the output performance. It monitors capacitors by actively adjusting the neutral-point (NP) current to induce a ripple in the NP voltage. Then, it reconstructs the injected NP current based on the switching states and three-phase currents, and obtains the response ripple in the NP voltage through a recursive least square (RLS) filter. Finally, according to the relationship between the injected NP current and the response ripple in the NP voltage, it estimates the capacitance simply via a RLS observer. Both numerical simulation and experimental results are provided as well to verify the correctness and feasibility of the proposed method.

Recent Advancement in Multicomponent Synthesis of Fused Coumarin Derivatives.

The synthesis of coumarin derivatives has been an essential topic since its discovery in 1820. In bioactive compounds, the coumarin moiety serves as a backbone, as many such bioactive compounds with the coumarin moiety play a significant role in their bioactivities. Given this moiety's relevance, several researchers are developing fused-coumarin derivatives to create new drugs. Mostly the approach done for this purpose was a multicomponent reaction based. Over the years, the multicomponent reaction has gained enormous popularity, and this approach has evolved as a replacement for conventional synthetic methods. Because of all these perspectives, we have reported the various fused-coumarin derivatives synthesized using multicomponent reactions in recent years.

Hypothesizing the Green Synthesis of Tamoxifen Loaded Magnetic Nanoparticles for the Treatment of Breast Cancer.

Breast cancer is the second leading cause of death all over the world and is not only limited to females but also affects males. For estrogen receptor-positive breast cancer, tamoxifen has been considered the gold-line therapy for many decades. However, due to the side effects associated with the use of tamoxifen, its use is only limited to individuals in high-risk groups and limits its clinical application to moderate and/or lower-risk groups. Thus, there is a necessity to decrease the dose of tamoxifen, which can be achieved by targeting the drug to breast cancer cells and limiting its absorption to other body parts. Artificial antioxidants used in the formulation preparation are assumed to upsurge the risk of cancer and liver damage in humans. The need of the hour is to explore bio-efficient antioxidants from natural plant sources as they are safer and additionally possess antiviral, anti-inflammatory, and anticancer properties. Objectives of the study and research: The objective of this hypothesis is to prepare tamoxifen-loaded PEGylated NiO nanoparticles using green chemistry, tumbling the toxic effects of the conventional method of synthesis for targeted delivery to breast cancer cells. Significance of the research work: The significance of the work is to hypothesize a green method for the synthesis of NiO nanoparticles that are eco-friendly, cost-effective, decrease multidrug resistance, and can be used for targeted therapy. Garlic extract contains an organosulfur compound (Allicin) which has drug-metabolizing, anti-oxidant, and tumour growth inhibition effects. In breast cancer, allicin sensitizes estrogen receptors, increasing the anticancer efficacy of tamoxifen and reducing offsite toxicity. Thus, this garlic extract would act as a reducing agent and a capping agent. The use of nickel salt can help in targeted delivery to breast cancer cells and, in turn, reduces drug toxicity in different organs. Future directions/recommendations: This novel strategy may aim for cancer management with less toxic agents acting as an apt therapeutic modality.

Time series analysis of L-band PALSAR-2 images in Istanbul and Kocaeli, Turkey

ABSTRACT Conducting long measurements of infrastructure deformation is a critical engineering task. Conventional methods are both time-consuming and expensive, limiting their use for large-scale applications. The synergy of synthetic aperture radar (SAR) and geographic information systems (GIS) offers a complementary approach. This study focuses on the feasibility of using time series analysis of L-band PALSAR-2 images to discover land displacements in Istanbul and Kocaeli, significant industrial and residential areas in Turkey. PALSAR-2 phase and intensity information were analyzed. For phase analysis, 14 L-band images from 2014 to 2021 were taken into account. Small baseline subset (SBAS) analysis was performed using 44 pairs, and results of the velocity, coherence and backscattering values are presented. Coherence of all pairs and their correlations were calculated. Principal Component Analysis (PCA) reduced the dimension of coherence pairs, enhancing feature extraction and the final geocoded velocity map revealed a fastest subsidence rate of −58 mm/yr and a mean subsidence of −20 mm/yr. These findings were confirmed through mean vertical velocity from Sentinel-1 datasets and field observations. The results showed that immature land subsidence in the mentioned areas are growing slowly, which can be taken as a serious risk in future.

Probabilistic Causal Effect Estimation With Global Neural Network Forecasting Models.

We introduce a novel method to estimate the causal effects of an intervention over multiple treated units by combining the techniques of probabilistic forecasting with global forecasting methods using deep learning (DL) models. Considering the counterfactual and synthetic approach for policy evaluation, we recast the causal effect estimation problem as a counterfactual prediction outcome of the treated units in the absence of the treatment. Nevertheless, in contrast to estimating only the counterfactual time series outcome, our work differs from conventional methods by proposing to estimate the counterfactual time series probability distribution based on the past preintervention set of treated and untreated time series. We rely on time series properties and forecasting methods, with shared parameters, applied to stacked univariate time series for causal identification. This article presents DeepProbCP, a framework for producing accurate quantile probabilistic forecasts for the counterfactual outcome, based on training a global autoregressive recurrent neural network model with conditional quantile functions on a large set of related time series. The output of the proposed method is the counterfactual outcome as the spline-based representation of the counterfactual distribution. We demonstrate how this probabilistic methodology added to the global DL technique to forecast the counterfactual trend and distribution outcomes overcomes many challenges faced by the baseline approaches to the policy evaluation problem. Oftentimes, some target interventions affect only the tails or the variance of the treated units' distribution rather than the mean or median, which is usual for skewed or heavy-tailed distributions. Under this scenario, the classical causal effect models based on counterfactual predictions are not capable of accurately capturing or even seeing policy effects. By means of empirical evaluations of synthetic and real-world datasets, we show that our framework delivers more accurate forecasts than the state-of-the-art models, depicting, in which quantiles, the intervention most affected the treated units, unlike the conventional counterfactual inference methods based on nonprobabilistic approaches.

Dynamic-Linearization-Based Predictive Control of a Voltage-Source Inverter

In pursuit of accurate and fast trajectory tracking of power converters, an explicit model is commonly used in the finite control-set model predictive control (FCS-MPC) framework to predict precise behaviors of controlled variables. In reality, however, the model mismatch is inevitable, which causes the inherent challenges of parameter sensitivity and model uncertainties of the FCS-MPC method. This article proposes a dynamic-linearization-based predictive control architecture to circumvent such model dependence while keeping the attractive features of the conventional FCS-MPC method. By integrating the data-driven feature of the dynamic-linearization approach, the detailed model used in the FCS-MPC controller is replaced by a virtual equivalent data model, creating a data-driven predictive control architecture. The suggested method selects optimal control action solely based on the input-output data, exhibiting strong rejection against parameter variations whilst inheriting the distinctive property of the conventional FCS-MPC method. Finally, the proposed design is validated through comparative simulation and experimental results on a three-level neutral-point-clamped inverter.

Remote field-programmable gate array laboratory for signal acquisition and design verification

A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embedded-system design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.

Study of Structural Elucidation, Degradation Kinetics and Antimicrobial Analysis of Copper (II) Sesame-Soap Complexes with Urea and Thiourea Ligand in Non-aqueous Solvents

Abstract: Surfactants have great importance in biological and drug industries and the complexes of metallic soaps with various ligands are used in approximately every region of national economy. Therefore, our keen interest to study of degradation kinetics and biological importance of Cu (II) surfactants in non-aqueous and non-polar solvent benzene. Present research work has been initiated with synthesis, systematic study of structural elucidation, thermal degradation, kinetics and biocidal activities of copper (II) sesame-soap complexes with macrocyclic nitrogen and sulphur containing donar ligands like urea and thiourea. The thermal degradation of copper (II) sesame-soap complexes were carried out for analysis of degradation kinetics and estimation of kinetic and thermodynamic parameters using different methods at heating rate 10oC min-1. Copper (II) sesame-soap complexes of such ligands have also been analysed against Staphylococus aureus. This research work consolidates the synthesis of copper (II) sesame soap-urea and thiourea complexes by conventional methods and the structure of these complexes were assigned according to elemental analysis and molecular weight determinations. IR, NMR and ESR spectral studies have also been done to understand structural aspects. The anti-microbial activities of copper (II) sesame-soap urea and thiourea complexes have been evaluated by testing against Staphylococus aureus. The present research work includes information of thermal analysis using TGA technique to find out their kinetic and thermodynamic parameters by using diverse equations such as Coats-Redfern equation, Horowitz-Metzger equation, Broido equation, Piloyan–Novikova equation. Moreover, the results obtained from anti-microbial screening have been used to analyze the anti-microbial activities of copper (II) sesame-soap urea and thiourea complexes against gram-positive bacteria Staphylococus aureus. These results show that complexes of copper ion co-ordinated with different nitrogen, oxygen and sulphur containing ligands, are very important in the field of pharmaceutical chemistry due to its significant role in the inhibition activity. The study of these complexes concludes that the synthesized complexes were found to possess appreciable bactericidal properties at different concentrations because chelation increases the anti-microbial potency.

Online Magneto-Thermal Coupled Model of PMECCs Using Measured Boundaries

Online modeling of the magneto-thermal coupled field (MCF) in permanent magnet eddy current couplings (PMECCs) has long been challenging since it exists in the copper layer and cannot be directly measured. This article proposes an online magneto-thermal coupled model (OMCM) to reconstruct the MCF using measured boundaries. The magnetic field (MF), eddy current field (ECF), and temperature field (TF) in the MCF are coupled in an identical space-time united discretization system. Virtual boundary nodes are added to the system, which can adapt to different boundary conditions in electromagnetic and thermal analysis. The measured data of single-point sensors are spanned to continuous boundary conditions. An approximate solution of the MCF is derived based on the measured boundaries and the general solutions of Laplace's equation and Poisson's equation in magneto-thermal analysis. The three-dimensional distribution of the MF, ECF, and TF at each spatial node and time segment can be simultaneously calculated in matrix form by the OMCM. Furthermore, the output torque is estimated and compared with the conventional methods. A verification process is carried out to assess the reconstructed results, showing that the OMCM can yield results close to the verification sensors for various slips and air gaps.

Optimizing Ship Airwake Database for Predicting Autospectra Using Deep Learning

Airwakes are shed behind a ship’s superstructure, and they represent a highly turbulent and rapidly distorting flow field. Because of this complexity, they require a baseline: A database of flow velocity points, preferably through experiments and also through high-fidelity computational fluid dynamics simulations. However the database is prohibitively costly, and in situ measurements, at a constant wind angle, are often affected by the weather. A previously developed perturbation scheme helps to generate the autospectrum in closed form from a given wind database, specifically, from the numerically extracted autospectrum for this database. The primary objective of this paper is to demonstrate how deep neural networks can be fruitfully exploited to reduce the dependence on a database of flow velocity points in generating the autospectra, by reducing the amount of in situ data required to achieve the same accuracy as established methods. Two machine learning approaches are developed to predict the autospectrum for a data set when provided with a subset of the wind data set at the input and are assessed using cross-validation. In the first, a neural network is applied to predict the perturbation scheme. Though this method did not predict the autospectrum better than the previously developed perturbation scheme, it laid the foundation for the second method. In the second, a neural network is applied to the autospectra which can be numerically extracted from a database. An improvement is found in the average error computed over all data sets for the model-predicted autospectrum compared to the average error in the autospectrum predicted using the conventional method. The model provided better results for 51.4% of the data sets compared to the conventional method, with many data sets showing marked improvement. The database measured from the Naval Academy YP676 training vessel is used for training and testing the neural network.

الكشف عن بعض عوامل الضراوة بين المبيضات المعزولة من المرضى وانتشار جين candidalysin ECE1

المبيضات البيضاء هي مسبب شائع لعدوى الجهاز التنفسي وداء المبيضات الفموي لدى الناس ، ومسببات أمراض الخميرة الانتهازية والأسباب الرئيسية للمرض والوفيات لدى الأشخاص الذين يعانون من نقص المناعة وتسبب التهابات سطحية على الأسطح المخاطية التي تصيب ملايين الأشخاص في جميع أنحاء العالم.  كان الهدف الرئيسي من هذه الدراسة هو التحقق من انتشار بعض عوامل الضراوة التي لها القدرة على تكوين الأغشية الحيوية (Biofilm)، والبروتينيز  (Proteinase)، وإنتاج الهيمولايسين( Hymolysine) بين  عزلات  C. albicans التي تشمل انتشار جين Candidalysin (ECE1).  تم جمع العينات خلال الفترة من ايار 2022 الى اب 2022  من 280 عينة (مسحة) لمرضى عراقيين  من مختلف الاعمار والاجناس غير مكررين  كانوا يعانون من داء المبيضات الفموي وامراض الجهاز التنفسي في مستشفيات بغداد (مستشفى مدينه الطب و مستشفى اليرموك التعليمي ومستشفى مدينه الامامين الطبية التعليمية).  تم زراعة العينات التي تم جمعها على وسط اكار  السابرويد  دكستروز (SDA) مضاف اليه كلورامفينيكول كمضاد للبكتيريا بتركيز 10 ميكجم / مل ، وتم حضن الاطباق الملقحة على وسط SDA عند 37 درجة مئوية لمدة تتراوح بين 24-48 ساعة.أظهرت نتائج الاستنبات أن 102 عينة كانت موجبه، منها58 (56.86%) من تجويف الفم و 44 (43.14%) من الجهاز التنفسي،  بينما 178 منها كانت سالبة , تم الكشف عن عزلات المبيضات باستخدام الطرق التقليدية والتي تشمل (النمو على وسط HiCrome Candida ، إنتاج الأنبوب الجرثومي Germ tube ، تكوين الابواغ الكلاميديه Clamydospore   وتم تأكيد نتئج التعريف باستخدام نضام VITEK-2  بينت النتائج ان من بين 102 عزلة C. albicans هي الاكثر شيوعا بين الانواع بنسبة 68.63 % ثم تلتها C. tropicalis  بنسبة %10.78  ثم C. kruzei %5.88  وC. kefyr %5.88 C. parapsilosis % 4.9  بينما كانت C. glabrata %  3.9  وتمثل النسبة الاقل بين الانواع . تم فحص حساسية عزلات المبيضات للأدوية المضادة للفطريات بطريقة انتشار القرص ، والتي أجريت على النحو الموصى به بواسطة(CLSI) M44-A -وثيقة  . أظهرت العزلات درجة عالية من الحساسية للأمفوتريسين-ب 93.14%    وكلوتريمازول 90.20% ونسياتين 85.92% ،تم التحقق من انتشار جينCandidalysin (ECE1)  بين 70 عزلة من C. albicans باستخدام تقنية تفاعل البلمرة المتسلسل  PCR  ، وأظهرت النتائج أن 41  58.57%  تحتوي على جين Ece1 في تجويف الفم والجهاز التنفسي. و 34 عزلة  48.57%  من C. albicans كانت منتجة قوية للغشاء الحيوي,  بينما 30 عزلة 42.86%  عزلة انتجت بروتيناز و 20 عزلة  28.57%  عزلة لديها القدرة على تحلل الدم.

Measuring Color Index of Transformer Oil–Enabling Single-Wavelength Spectroscopy With Artificial Neural Network-Fuzzy Logic Model

Conventionally, the color index of transformer oil is determined by a color comparator based on the American Society for Testing and Materials (ASTM) D 1500 standard. The equipment requires humans to operate, which leads to human error and limited number of samples tested per day. This work proposes the utilization of single-wavelength spectroscopy with 405 nm laser diode using artificial neural network (ANN) to determine the color index of transformer oil. Two ANN models were developed using data collected from 50 oil samples with different optical pathlengths of 1 to 10 mm, and laser output powers of 1 to 15 mW. The first model classified the input into different color indices and another model correlated the input parameters through regression analysis to determine the color index. A hybrid ANN-fuzzy logic model was also developed to improve the color index prediction. The root-mean-squared error (RMSE) obtained from the prediction by ANN regressor and ANN classifier are 0.5602 and 0.6416, respectively. The hybrid ANN-fuzzy logic model improves the RMSE especially for optical pathlengths <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula> 5 mm, which is required for measuring samples with high color index. This proposed method reduces the dependency on complex optoelectronic hardware to obtain highly accurate results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Unlike the conventional testing method for color index of transformer oil that requires human observation, the findings of this study enables the possibility of compact and smart portable device through the utilization of single wavelength spectroscopy with machine learning models. With no human involvement, more computational power with lesser hardware dependency, the maintenance cost and error can be reduced. This proposed method can potentially be applied to measure the color of other amber-colored liquid products such as olive oil, honey and others.

Green Synthesis and Antibiofilm Activity of Silver Nanoparticles by Camellia sinensis L. (White Tea Leaf)

Biosynthesis of nanoparticles refers to the production or synthesis of nanoparticles using organisms, such as bacteria, fungi, plants or their byproducts. This approach offers several advantages over conventional chemical methods, including eco-friendliness, cost-effectiveness, and potential for large-scale production. The silver nanoparticles (AgNPs) synthesized using aqueous Camellia sinensis L. (white tea leaf) extracts as reducing and stabilizing agents were reported and evaluated for antibiofilm activity against test microorganisms (Acinetobacter baumanii ATCC 19606 NRRLB 3704, Pseudomonas aeruginosa ATCC 27853 (Gram –), and Bacillus subtilis ATCC 6633, Staphylococcus haemolyticus ATCC 43252 (Gram +), and Candida albicans ATCC 10231) in the study. The synthesized AgNPs were observed and characterized using Uv-Vis spectroscopic analysis, scanning electron and transmission electron microscopy energy-dispersive spectra and Fourier transform infrared spectroscopy. The synthesized AgNP was also screened for antibiofilm activity against test microorganisms. Our results show that the synthesized AgNPs have the potential to be used for antibiofilm materials and different biomedical applications.

Introspective Deep Metric Learning.

This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods focus on learning a discriminative embedding to describe the semantic features of images, which ignore the existence of uncertainty in each image resulting from noise or semantic ambiguity. Training without awareness of these uncertainties causes the model to overfit the annotated labels during training and produce overconfident judgments during inference. Motivated by this, we argue that a good similarity model should consider the semantic discrepancies with awareness of the uncertainty to better deal with ambiguous images for more robust training. To achieve this, we propose to represent an image using not only a semantic embedding but also an accompanying uncertainty embedding, which describes the semantic characteristics and ambiguity of an image, respectively. We further propose an introspective similarity metric to make similarity judgments between images considering both their semantic differences and ambiguities. The gradient analysis of the proposed metric shows that it enables the model to learn at an adaptive and slower pace to deal with the uncertainty during training. Our framework attains state-of-the-art performance on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets for image retrieval. We further evaluate our framework for image classification on the ImageNet-1 K, CIFAR-10, and CIFAR-100 datasets, which shows that equipping existing data mixing methods with the proposed introspective metric consistently achieves better results (e.g., +0.44% for CutMix on ImageNet-1 K).

Application of deep learning with bivariate models for genomic prediction of sow lifetime productivity-related traits.

Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

Optimal proportional-integral speed control for closed-loop engine timing system

In internal combustion engines, adjusting the air-fuel ratio is essential to control the speed and minimize the burnt fuel. The throttle opening is the actuator to control the air-fuel. A better design for the used/conventional controller can give a better response without additional cost. In this work, the proposed controller gains of the proportional-integral (PI) controller are tuned to enhance the speed in constant and variable drive cycle modes. The tuning process is conducted based on two of the most efficient performance indices used in this field. The performance indices are integral absolute error (IAE) and integral time absolute error (ITAE). The optimization problem is solved using three reliable stochastic optimization algorithms to ensure mature convergence of the solutions, to avoid local optima solutions, and to ensure effective shrinking of the search space. The optimization algorithms are teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and genetic algorithm (GA). Different simulations are conducted to validate the results. The results are compared with conventional tuning methods regarding the system's time response.

Human Eye Activity Monitoring Using Continuous Wave Doppler Radar: A Feasibility Study.

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.

Improved Position Sensorless Resonant Frequency Tracking Control Method for Linear Oscillatory Machine

To improve the reliability of control system and obtain the maximum output efficiency, it is very necessary to carry out the position sensorless piston stroke control and the resonance frequency tracking control for linear oscillatory machine (LOM) at the same time. The conventional resonant frequency tracking control method based on model reference adaptive system (MRAS) has the problem of low estimation accuracy caused by inaccurate model parameters. In order to obtain more accurate resonant frequency, an improved position sensorless resonant frequency tracking control method is proposed in this article. In this method, the adjustable model is constructed based on the mechanical dynamics equation of LOM to track the system resonant frequency. A hybrid terminal sliding-mode observer (HTSMO) is designed to replace the conventional reference model, which can obtain more accurate velocity reference signal for adjustable model by introducing the current error feedback link, thereby further improving the resonant frequency tracking accuracy. Moreover, a second-order generalized integrator (SOGI) is introduced to filter the estimated velocity signal of HTSMO. The filtered velocity signal is not only used for the adjustable model to track the resonant frequency, but also used to obtain the smooth piston stroke signal for the stroke closed-loop control. Comprehensive simulation and experimental results have proved the effectiveness and the superiority of the proposed method.

Bidirectional recommendation in HR analytics through text summarization

For over a decade, online job portals have been providing their services to both job seekers and employers in search of hiring opportunities. Because of the high demand for recruitment, it is insufficient to use conventional hiring methods to find a suitable candidate to fill the position. Validating resumes online is challenging due to the potential for manual errors, making the process inherently risky. The bidirectional method comprises named entity recognition (NER) for extracting the required resumes for recruiters. Cosine similarity shows the match percentage of resumes for the job requirements and vice versa. In an attempt to tackle an issue of unregistered words, a solution called decoder attention with pointer network (DA-PN) has been introduced. This method incorporates the use of coverage mechanism to prevent word repetition through generated text summary. DA-PN+Cover method with mixed learning objective (MLO) (DA-PN+Cover+MLO) is utilized for protecting grow of increasing faults in generated text summary. Performance of proposed method is estimated using evaluation indicator recall oriented understudy for gisting evaluation (ROUGE) and attains an average of 27.47 which is comparatively higher than existing methods.

From Pseudo to Real: Generalized Subspace Method for Power Spectrum Reconstruction

Subspace methods are widely used in statistical signal processing owing to their super-resolution. Conventional subspace methods, such as multiple signal classification (MUSIC) identify parameters containing frequency and direction of arrival by calculating pseudo-spectra without physical amplitudes. However, the real value of amplitude plays a significant role in fault diagnosis and condition monitoring. Additionally, due to hardware limitations and increased frequency, uniform (Nyquist) sampling becomes challenging. Nonuniform sampling, an alternative to high-rate sampling is common in practice. Consequently, Nonuniform (sub-Nyquist) signal processing methods are urgently required. In this paper, a generalized subspace method (GSM) is proposed to bridge the pseudo spectrum with the real spectrum. The proposed method is a supplement to the existing subspace methods and is applicable to both uniform and nonuniform sampling. In the GSM, the Fourier coefficient of the autocorrelation function is derived by signal subspace from the perspective of basis matching. Then, an alias-free real power spectrum is obtained by combining the power spectrum reconstructed by GSM with the classical pseudo spectrum. Finally, simulation and experiment demonstrate the performance of the proposed method in power spectrum reconstruction. Even under the dual influence of undersampled and nonuniform, the proposed method is still effective.