Conventional Techniques Research Articles

MTPA Control for DC-Biased Hybrid Excitation Machine Using MTPA Control Law and Virtual Signal Injection

This paper proposes an overall maximum torque per ampere (MTPA) control for dc-biased hybrid excitation machine (dc-biased HEM) using MTPA control law and virtual signal injection. The mathematical model of the machine is first analyzed in synchronous rotating reference frame. According to the characteristic of the mathematical model, a control strategy is proposed and consists of two parts, i.e., an MTPA control law and a virtual dc-biased signal injection. The MTPA control law is employed to calculate the dominant current references while ensuring the rapid dynamic performance. The virtual dc-biased signal injection method is a correction scheme for MTPA current deviation caused by parameter variations. Unlike conventional signal injection techniques, this signal injection method considers the nonlinearity of inductance and does not involve any filters. Thus, it can extract MTPA criterion more accurately and easily. In general, simulation and experimental results validate that the control strategy combined with MTPA control law and virtual signal injection can achieve accurate MTPA operation and rapid dynamic magnetization capability for the dc-biased HEM in real time.

Approach towards surveillance-based diagnosis of acute respiratory illness in India: Expert recommendations

BackgroundEmerging infectious diseases, often zoonotic, demand a collaborative “One-Health” surveillance approach due to human activities. The need for standardized diagnostic and surveillance algorithms is emphasized to address the difficulty in clinical differentiation and curb antimicrobial resistance. ObjectiveThe present recommendations are comprehensive diagnostic and surveillance algorithm for ARIs, developed by the Indian Council of Medical Research (ICMR), which aims to enhance early detection and treatment with improved surveillance. This algorithm shall be serving as a blueprint for respiratory infections landscape in the country and early detection of surge of respiratory infections in the country. ContentThe ICMR has risen up to the threat of emerging and re-emerging infections. Here, we seek to recommend a structured approach for diagnosing respiratory illnesses. The recommendations emphasize the significance of prioritizing respiratory pathogens based on factors such as the frequency of occurrence (seasonal or geographical), disease severity, ease of diagnosis and public health importance. The proposed surveillance-based diagnostic algorithm for ARI relies on a combination of gold-standard conventional methods, innovative serological and molecular techniques, as well as radiological approaches, which collectively contribute to the detection of various causative agents.The diagnostic part of the integrated algorithm can be dealt at the local microbiology laboratory of the healthcare facility with the few positive and negative specimens shipped to linked viral disease research laboratories (VRDLs) and other ICMR designated laboratories for genome characterisation, cluster identification and identification of novel agents.

Approaching code-compliant Non-Destructive Testing of carbon steel weld using Total Focusing Method technique

The Total Focusing Method (TFM) technique is an advanced Non-Destructive Testing (NDT) Ultrasonic Testing (UT) technique. It is usually used together with special acquisition configurations such as Full Matrix Capture (FMC) or Plane Wave Imaging (PWI) and can supply synthetic post-processing results based on a quantity of ultrasonic signals. It is believed that it will accelerate the UT technique revolution toward the artificial intelligence era. Compared to the conventional UT technique, or even advanced UT techniques, such like Time of Flight Diffraction (TOFD) and Phased Array (PA) techniques, this advanced UT technique is well known for its easy-to-interpret and high-resolution representative images. For these reasons, it is used more and more in the flaw sizing and characterization assessment. Following the publication of the standard ISO 23864, TFM technique become a member among all standardized weld inspection techniques. This article presents some related study results toward a code-compliant weld inspection regarding the related standards, dealing especially with the sensitivity calibration which is a key point for the applicability of amplitude-based acceptance criteria.

Broadband Love Wave Phase Velocity Maps Based on Modified Double‐Beamforming of Ambient Noise Cross‐Correlations

AbstractAmbient noise tomography has become a popular method in the past two decades to image the crust and uppermost mantle structure. To date, broadband Rayleigh wave signals can be obtained from ambient noise, which can be utilized to study the earth's interior structure from the surface down to ∼200–300 km depths. However, it is hard to extract intermediate‐ and long‐period (>50 s) Love wave signals from ambient noise using conventional data processing techniques for ambient noise. Array‐based data processing techniques can enhance weak signals. In this study, we adopt a modified algorithm of the double‐beamforming method to extract broadband Love wave signals from ambient noise. We validate the accuracy of the dispersion curves measured from our method by comparing them to those measured from the conventional method. Then, we use a finite frequency ambient noise tomography method to construct broadband Love wave phase velocity maps across the contiguous USA. These phase velocity maps are consistent with those obtained from conventional methods at short periods (<40 s). Finally, we analyze the resolution of our double‐beamforming method based on checkerboard tests and find that the resolution of phase velocity maps based on our method is close to the aperture of the subarrays used in our double‐beamforming method.

Combining range-general and range-specific techniques for late-time focused image reconstructiona).

Synthetic aperture sonar (SAS) is an acoustic method for detecting objects in an environment. Conventional SAS image reconstruction techniques invert a forward model based on geometric scattering and straight-line propagation. Acoustic features that do not fit this model, such as multiple scattering and late-time returns, appear out of focus. This paper describes an image reconstruction technique that selectively applies range-general and range-specific methods to improve the focus of late-time returns while maintaining image quality away from the focal plane. The technique is demonstrated on experimental data and compared with a range-specific algorithm.

ETVO: Effectively Measuring Tactile Internet With Experimental Validation

The next frontier in communications is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">teleoperation</i> – manipulation and control of remote environments with haptic feedback. Compared to conventional networked applications, teleoperation poses widely different requirements, ultra-low latency (ULL) is primary. Realizing ULL communication demands significant redesign of conventional networking techniques, and the network infrastructure envisioned for achieving this is termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tactile Internet</i> (TI). The design of meaningful performance metrics is crucial for seamless TI communication. However, existing performance metrics fall severely short of comprehensively characterizing TI performance due to their inability to capture how well sensed signals are reproduced. We take Dynamic Time Warping(DTW) as the basis of our work and identify necessary changes for characterizing TI performance. Through substantial refinements to DTW, we design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Effective Time- and Value-Offset (ETVO)</i> – a new method for measuring the fine-grained performance of TI systems. Through an in-depth objective analysis, we demonstrate the improvements of ETVO over DTW. Through subjective experiments, we demonstrate that existing QoS and QoE methods fall short of estimating the TI session performance accurately. Using subjective experiments, we demonstrate the behavior of the proposed metrics, their ability to match theoretically derived performance, and finally, their ability to reflect user satisfaction in a practical setting.

An improved low‐carbon intelligent agriculture system with energy optimization principles using wireless IoT environment

AbstractA low‐carbon agricultural plan and intelligent agriculture control system are designed and completed with a green economy to foster the significant expansion of green resources and environmental assets. This research employs a fixed‐effect panel model to examine the farming cumulative energy savings in 11 cities and regions along the Yamuna Tributary Commercial Region, focusing on both static and dynamic factors. The directional linear equation and the Malmquist–Luenberger (ML) indicator are the key analytical tools. Unlike conventional techniques, regression analysis using cross‐sectional data is the primary tributary. Research has found that a 5.9% decrease in the literacy rate of the working force had a large impact on farming energy efficiency and that for each 1% rise in crop losses, farm energy efficiency would decrease by 0.487%. In addition to mitigating the detrimental effects of industrial design and mechanization on agricultural output, this effort aids in the advancement of smart city design. This activity paves the way for cutting‐edge technology by encouraging people to move to cities, educating the workforce, and giving money to the agricultural sector. The ability of proposed model gives the smart agriculture system with intelligent control principles and energy optimization principles in the deployed wireless‐IoT platform. On this research problem, this paper contributes to new knowledge by approaching agricultural energy efficiency from a green economy perspective, conducting regression analyses of panel data on the most important influencing factors using the advanced statistical analysis tool Eviews, and then drawing the conclusion from the evaluation of metrics that affect farming energy efficiency in our nation. Enhancing productivity while also fostering long‐term growth in the agricultural ecosystem is crucial. It contributes for smart and green technologies adaptation in agriculture filed.

From unstructured accident reports to a hybrid decision support system for occupational risk management: The consensus converging approach

Introduction: Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts’ subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology. Method: To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights. Conclusions and Practical Applications: The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

Comparative Clinical Study of Using Piezoelectric Ultrasound Surgery Versus Conventional Technique in Sinus Lifting Procedure with Simultaneous Implant Insertion

Comparative Clinical Study of Using Piezoelectric Ultrasound Surgery Versus Conventional Technique in Sinus Lifting Procedure with Simultaneous Implant Insertion

Proteomics techniques in protein biomarker discovery

AbstractProtein biomarkers represent specific biological activities and processes, so they have had a critical role in cancer diagnosis and medical care for more than 50 years. With the recent improvement in proteomics technologies, thousands of protein biomarker candidates have been developed for diverse disease states. Studies have used different types of samples for proteomics diagnosis. Samples were pretreated with appropriate techniques to increase the selectivity and sensitivity of the downstream analysis and purified to remove the contaminants. The purified samples were analyzed by several principal proteomics techniques to identify the specific protein. In this study, recent improvements in protein biomarker discovery, verification, and validation are investigated. Furthermore, the advantages, and disadvantages of conventional techniques, are discussed. Studies have used mass spectroscopy (MS) as a critical technique in the identification and quantification of candidate biomarkers. Nevertheless, after protein biomarker discovery, verification and validation have been required to reduce the false‐positive rate where there have been higher number of samples. Multiple reaction monitoring (MRM), parallel reaction monitoring (PRM), and selected reaction monitoring (SRM), in combination with stable isotope‐labeled internal standards, have been examined as options for biomarker verification, and enzyme‐linked immunosorbent assay (ELISA) for validation.

High-Sodium-Concentration Sodium Oxythioborosilicate Glass Synthesized via Ambient Pressure Method with Sodium Polysulfides.

The practical utilization of all-solid-state sodium batteries necessitates the development of a mass synthesis process for high-alkali-content sulfide glass electrolytes, which are characterized by both high ionic conductivity and remarkable formability. Typically, vacuum sealing and quenching are conventional techniques employed during the manufacturing process. In this paper, we present a novel approach, a pioneering method for the production of sulfide glass electrolytes with high alkali concentrations, achieved through ambient-pressure heat treatment and a gradual cooling process. We enhance the glass-forming ability of Na3BS3 by incorporating a small quantity of SiO2. The ionic conductivity of the resulting Na3BS3·0.225SiO2 (molar ratio) glass exhibited 1.5 × 10-5 S cm-1 at 25 °C, surpassing that of Na3BS3 glass. An all-solid-state cell utilizing Na3BS3·0.225SiO2 glass is successfully operated as a secondary battery at 60 °C. Our findings suggest that sodium oxythioborosilicate glass with electrochemical properties identical to those of Na3BS3 can be prepared without the need for quenching. These results propel the advancement of research in the domain of mass production processes tailored for high-alkali-content sulfide glass.

Genetic And Environmental Associations Of Nonspecific Chromosomal Aberrations.

Nonspecific structural chromosomal aberrations (CAs) are found in around 1% of circulating lymphocytes from healthy individuals but the frequency may be higher after exposure to carcinogenic chemicals or radiation. CAs have been used in the monitoring of persons exposed to genotoxic agents and radiation. Previous studies on occupationally exposed individuals have shown associations between the frequency of CAs in peripheral blood lymphocytes and subsequent cancer risk. The cause for CA formation are believed to be unrepaired or insufficiently repaired DNA double-strand breaks or other DNA damage, and additionally telomere shortening. CAs include chromosome (CSAs) and chromatid type aberrations (CTAs). In the present review, we first describe the types of CAs, the conventional techniques used for their detection and some aspects of interpreting the results. We then focus on germline genetic variation in the frequency and type of CAs measured in a genome-wide association study (GWAS) in healthy individuals in relation to occupational and smoking-related exposure compared to non-exposed referents. The associations (at p<10-5) on 1473 healthy individuals were broadly classified in candidate genes from functional pathways related to DNA damage response/repair, including PSMA1, UBR5, RRM2B, PMS2P4, STAG3L4, BOD1, COPRS and FTO; another group included genes related to apoptosis, cell proliferation, angiogenesis and tumorigenesis, COPB1, NR2C1, COPRS, RHOT1, ITGB3, SYK, and SEMA6A; a third small group mapped to genes KLF7, SEMA5A and ITGB3 which were related to autistic traits, known to manifest frequent CAs. Dedicated studies on 153 DNA repair genes showed associations for some 30 genes, expression of which could be modified by the implicated variants. We finally point out that monitoring of CAs is so far the only method of assessing cancer risk in healthy human populations, and the use of the technology should be made more attractive by developing automated performance steps and incorporating artificial intelligence methods into the scoring.

Barium Titanate Synthesis in Water Vapor: From Mechanism to Ceramics Properties

A facile and environmentally benign method for single-phase barium titanate synthesis in a water vapor medium was studied to reveal the mechanism of phase transformation of the initial simple oxide mixture and estimate the capability of the product to be used as a raw material for low-frequency dielectric ceramics. The composition and structure of the reactants’ mixture, treated in vapor at 130–150 °C as well as at 230 °C for various time periods, were investigated by means of XRD, SEM, TEM, EDX, and FTIR methods. The kinetics of the occurring phase transformation can be described using the Johnson–Mehl–Avrami–Erofeev equation. The reaction between the initial oxides was considered as a topochemical process with an apparent activation energy of 75–80 kJ mol−1. A crucial role in this process belonged to the water vapor medium, which facilitated the generation of the reaction zone and the spreading inward of the solid particles. The synthesized tetragonal barium titanate powder (mean particle size of 135 nm) was sintered using a conventional technique at 1250 °C to obtain ceramics with grains of about 2 μm. Capacitance measurements identified a permittivity and dielectric loss factor of the ceramics that reached 3879 and 6.7 × 10−3, respectively, at 1 kHz and room temperature.

Nucleic-Acid-Based Molecular Fungal Diagnostics: A Way to a Better Future.

The world has seen a tremendous increase in the number of fungal infections during the past two decades. Recently, the World Health Organisation released the pathogen priority list for fungal infections, signifying the importance of these infections in the fields of research and public health. Microbiology laboratories demand an upgrade in the diagnostic system to keep up with the increased burden of these infections. Diagnosis of fungal infections using conventional techniques has always faced limitations in terms of specificity, sensitivity, and turnaround time. Although these methods are the core pillars of the diagnosis, there is an increased need for molecular approaches. Molecular techniques have revolutionised the field of fungal diagnostics. The diverse array of molecular techniques, including techniques like Polymerase Chain Reaction (PCR), have emerged as a cornerstone in fungal diagnostics. Molecular techniques have transformed fungal diagnostics, providing powerful tools for the rapid and accurate identification of pathogens. As these technologies continue to evolve, their integration into routine clinical practice holds the promise of improving patient outcomes through timely and targeted antifungal interventions. This review will cover the molecular approaches involved in fungal diagnostics, moving from the basic techniques to the advanced-level nucleic-acid-based molecular approaches providing a high throughput and decreased turnaround time for the diagnosis of serious fungal infections.

Enhanced classification performance through GauGAN‐based data augmentation for tomato leaf images

AbstractThis study investigated a data augmentation method for plant disease classification and early diagnosis based on a generative adversarial neural network (GAN). In the development of classification models using deep learning, data imbalance is a primary factor that reduces classification performance. To address this issue, tomato disease images from the public dataset PlantVillage were used to evaluate the performance of the GauGAN algorithm. The images generated by the proposed GauGAN model were used to train a MobileNet‐based classification model and compared with methods trained with conventional data augmentation techniques and cut‐mix and mix‐up algorithms. The experimental results demonstrate that based on F1‐scores, GauGAN‐based data augmentation outperformed conventional methods by more than 10%. In addition, after the model was retrained on data collected in the field, it efficiently generated various disease images. The evaluation results from those images also revealed a data augmentation effect of about 10% compared with traditional augmentation techniques.

Disposable impedance sensors based on novel hybrid MoS2 nanosheets and microparticles to detect Escherichia Coli DNA.

The rapid and accurate detection of pathogenic bacteria is essential for food safety and public health. Conventional detection techniques, such as nucleic acid sequence-based amplification and polymerase chain reaction, are time-consuming and require specialized equipment and trained personnel. Here, we present quick, disposable impedance sensors based on the novel hybrid MoS2 nanomaterial for detecting Escherichia coli DNA. Our results indicate that the proposed sensors operate linearly between 10- 20 and 10-15 M concentrations, achieving an impressive detection limit of 10-20 M with the highest sensitivity observed at a 0.325 nM probe concentration sensor. Furthermore, the electrochemical impedance spectroscopy biosensors exhibited potential selectivity for Escherichia coli DNA over Bacillus subtilis and Vibrio proteolyticus DNA sequences. The findings offer a promising avenue for efficient and precise DNA detection, with potential implications for broader biotechnology and medical diagnostics applications.

Analysis and prediction of erosion behavior of epoxy composites using statistical and machine learning techniques

This work reports on the application of different machine learning (ML) techniques and statistical methods to analyze and predict the erosion wear performance of ramie fiber-reinforced epoxy composites. These composites, fabricated by conventional hand layup technique, are subjected to high-temperature erosion wear following a test schedule based on the design-of-experiments. The experimental database is used to determine the relative significance and contribution of individual parameter on the erosion wear rate using the analysis of variance. It suggests the impact velocity as the most significant factor contributing 81.2%, followed by impingement angle (13.56%), erodent temperature (0.06%) and stand-off distance (0.05%) to the wear rate. The data generated from experimentation are further processed to predict the erosion performance of the composites with a ML approach following four different algorithms such as support vector machine, decision tree, random forest and gradient boosting machine (GBM). The feature scores for different parameters in the above models are also evaluated to ascertain the contribution of each parameter, which consequently revealed that impact velocity and impingement angle with respective mean feature scores of 0.7538 and 0.1824 have maximum influence on the erosion rate. The performances of four different ML models are compared on the basis of their coefficient of determination (R2 value). It is found that the GBM outperforms other models with a R2 value as high as 0.9543 and has thus emerged as the best-performing prediction model. The possible mechanisms causing the wear loss are identified using electron microscopy.

Synchronized measurement method for urban distribution networks based on proportional-integral control

In the context of the rising prevalence of renewable energy, the need for precise and synchronized measurements in urban distribution networks has become increasingly critical. Conventional synchronization techniques, which predominantly depend on Global Navigation Satellite System (GNSS) timing signals, are often plagued by significant sampling time errors. Addressing this challenge, this study introduces an innovative automatic synchronization measurement method employing proportional-integral (PI) control. This method is composed of three integral components: the monitoring of local clock phases via pulses per second (PPS) signals, modulation of the clock phase through a PI controller, and the estimation of grid phasors from the synchronized sampled data. The test results of this method on a hardware platform demonstrate its capability to significantly reduce the sampling error from 2 × 10−3 V to an impressive 1 × 10−5 V, while also meeting the requirements of the synchronized phasor measurement standards outlined in C37.118.1. These advancements underscore the significant potential of PI-controlled simultaneous sampling in enhancing the operational efficiency and reliability of urban distribution networks, particularly in environments with a high integration of renewable energy sources.

Concurrent process and feedrate scheduling with convoluted basis functions and its application to fluid jet polishing

Non-traditional laser and fluid jet processes exhibit time-dependent material removal characteristics. The feedrate profile must be planned carefully along the toolpath for accurate surface profile generation while ensuring that the kinematic limits of machine tools are not violated. Conventional methods iteratively solve a deconvolution/convolution problem on the dwell-time density (reciprocal of the feedrate profile) that is computationally heavy, may leave significant residual processing errors, and even generate infeasible feed profiles with the manufacturing equipment. This paper proposes a novel approach that fully addresses the shortcomings above. Dwell-time density is first expressed as a continuous B-spline profile. The associated dwell basis functions (DBF) are convolved with the process influence function (PIF) to generate new process basis functions (PBF). This approach conveniently allows the posing of the problem as a concurrent linear least-squares problem on the control points shared by the DBFs and PBFs while ensuring the numerical stability of the solution and smoothness of the feed profile. To mitigate excessive acceleration peaks and any ringing effect around the edges of the toolpath, this paper also presents methodologies for stabilizing the scheduled feedrate profile by introducing knot vector adjustments (adaptive knot dropping) and linear edge constraints. The effectiveness of the proposed method is demonstrated and validated through simulation case studies and experimentally in fluid jet processing of precision optics. Results indicate that the proposed technique overcomes the limitations of conventional strategies and allows high-frequency surface components beyond the first zero-power frequency of the process footprint to be tracked while still generating a smooth feed profile within the acceleration limits of a machine tool. This ability stems from the localization characteristics associated with the basis functions. By improving the accuracy of high-frequency components, the proposed method exhibits the potential to fabricate topographies with sharper edges, which has been a challenge for conventional techniques.

Machine learning-based model inference for spectral response of photonic crystals

Photonic Crystals (PhCs) are materials with a periodic arrangement of dielectric or metallic components that can manipulate the flow of photons. Conventional techniques for computing the spectrum response of these artificial structures are time-consuming, laborious, and susceptible to human errors. This paper presents a novel approach incorporating Machine Learning (ML) in forming PhC-based periodic structures. The structures are designed from the Two-Dimensional (2D) PhCs having air holes in a dielectric material. These crystalline structures work on the Guided Mode Resonance (GMR) principle and find their use in numerous applications, including optical filters in Near Infrared Range (NIR). However, the conventional methods for analyzing the output spectra of PhCs need to be revised due to the complexities in designing compound optical filters. Therefore, an automated process employing ML is required. As a result, in our work, we form 2D crystalline structures using the Finite-Difference Time Domain (FDTD) method and predict various spectral responses of PhCs via ML-based linear regression. The proposed mathematical models provide efficient results and considerably less simulation efforts and time compared to the traditional manual methods.