Transformation Frequency Research Articles

ConvEQ: Convolutional neural network for earthquake phase classification using short time frequency transform

We present ConvEQ as a tool for discriminating seismic phases, leveraging artificial intelligence technique (Convolutional Neural Network) for short-time Frequency Transform of the seismic signal. Timely detection of the vertical (P) wave from an earthquake can generate a warning several tens of precious seconds before the more destructive waves strike. We propose a train-for-each-station approach for an Internet-of-Things-based Smart Earthquake Early Warning System, where lightweight neural networks trained for the seismic data belonging to each station are implemented on edge devices directly interfaced with seismometers. The approach has the potential to get the most from the sparse seismic network for Pakistan and other third-world countries. We train networks for multi-station and single-station data and achieve 96% and 99% accuracy, respectively, proving that train-for-each-station maximizes accuracy. The total processing time (including preprocessing and inference) is about 30ms for each event, thus suitable for real-time deployment. We further compare the performance of ConvEQ on simulated real-time data with several state-of-the-art contemporary algorithms. Our proposed approach demonstrates a robust response on diverse metrics. The ConvEQZ classifies the vertical seismic signal component with high accuracy and the ConvEQX can classify any seismic data component, inculcating robustness against connectivity issues.

TFormer: A time–frequency Transformer with batch normalization for driver fatigue recognition

Within the framework of the advanced human-cybernetic interfaces (HCI), Cross-subject electroencephalogram (EEG)-based driver fatigue recognition is emerging as a pivotal application in the paradigm of Industry 5.0. Recognizing the importance of ensuring driver safety through proactive monitoring, it is essential to offer a general EEG decoding system to improve road safety. This work investigated the use of Transformers for the challenging cross-subject EEG decoding task due to the great success the Transformers have achieved in various applications. Previous research focused on using Transformers to capture global temporal information, but less work targeted global frequency-domain patterns. Furthermore, in order to leverage a standard Transformer architecture grounded in natural language processing for EEG decoding, it is imperative to account for inherent characteristics in EEG and make pertinent adjustments accordingly. In this work, we proposed a time–frequency Transformer (TFormer) that can automatically learn the global time–frequency patterns from raw EEG data. TFormer consisted of three components: convolutional stems for input embedding, time–frequency multi-head cross-attention (TF-MCA) for integrating time-domain patterns into frequency points, and self-attention to further learn global time-frequency patterns. Moreover, we analyzed TFormer’s internal settings and found batch normalization (BN) more suitable for cross-subject EEG decoding than layer normalization (LN). The experiment results demonstrated the superiority of the proposed model compared to existing methods. Overall, our work contributes to the development of Transformer models in EEG decoding and illustrates a different way to leverage Transformers for decoding raw EEG data.

Assessment of the state-of-the-art of solid-state transformer technology: design, control and application

The structure and principle of operation of conventional iron-and-copper conventional transformers has not changed for the past decades largely to its high efficiency and reliability. However, conventional transformers are generally heavy and limited to transformation of AC power only in a unidirectional pattern. However, due to the increasing integration of distributed generation to grid, bidirectional power flow within the present grid systems is now inevitable. This makes solid-state transformer (SST), as a more flexible, portable, and cost-effective alternative, to gain considerable attention in recently. The SST is designed using power electronic components and a high frequency transformer (HFT). As an energy router, SST is crucial to the deployment of internet energy system due to its compact size, improved controllability, resiliency, bidirectional power flow, and variety of applications. Various designs and control approaches have been proposed for the SST to suit various applications. Thus, this paper reviews the extent of research works carried out on the SST vis-à-vis classifications, design, control and applications. The review is centered on establishing the state-of-the-art and identify future research prospects in the design, control, and applications of SST.

Fault Identification Method of Transformer Winding based on Gramian Angular Difference Field and Convolutional Neural Network

Background:: As the frequency of transformer winding faults becomes higher and higher, the frequency response analysis used to detect the winding status has attracted more and more attention. At present, there is still a lack of reliable and intelligent technologies for detecting the state of transformer windings in this field. Objective:: This paper focuses on studying a high-precision method for transformer fault diagnosis, which can be easily and effectively applied to daily life. Methods:: By changing the detection method, the traditional detection method can not distinguish the problem that the detection data are highly overlapping when identifying the same fault of the head and tail symmetric points, and the problem that the phase is too similar is changed. In order to solve the problem that the fault samples of transformer frequency response curve are scarce and the one-dimensional data cannot be read by partial deep learning method, the one-dimensional data of frequency response curve is first converted into characteristic index and then into a three-dimensional image by moving window calculation method and Gramian Angular difference field transformation. The fault classification is realized by a convolutional neural network. Results:: The accuracy of the final model for slice classification reached 100%. Conclusion:: Illustrative examples show that the method is distinguishable from different fault types. The traditional method only uses the amplitude of the frequency response curve, but this method displays the two features of the amplitude-phase together in the image. Compared with the traditional method, more features and samples are added to further improve the accuracy of the method. The accuracy of diagnosis results reached 100%, which showed the feasibility of the method.

Metal-organic frameworks (MOFs) wrapped palladium nanoparticles-loaded Fe3O4@CFR core-shell magnetic nanohybrid as heterogeneous catalyst with robust catalytic properties

Metal-organic frameworks (MOFs) wrapped palladium nanoparticles supported on Fe3O4@catechol formaldehyde resin (Fe3O4@CFR@Pd@MOF) as magnetic nanohybrid catalyst was successfully fabricated by employing the redox capacity of CFR shell of Fe3O4@CFR core-shell microsphere for in situ formation of Pd NPs, followed by surface coating with Cu-MOF(HKUST-1). The research results demonstrated that the complementary properties of the various components within this hybrid material yielded magnetic recyclable nanocatalysts with exceptional catalytic performance. The Fe3O4@CFR@Pd@MOF catalyst exhibited remarkable advantages in the catalytic reduction of nitrophenol, demonstrating a transformation frequency (TOF) value of up to 4651 h−1. Additionally, the nanocatalyst was proved to have robust catalytic capacity in the reduction of organic dyes and Suzuki-Miyaura reaction. The HKUST-1 framework played a triple role: facilitating dye enrichment, Cu2+ assisting Pd NPs catalysis, and mitigating Pd NPs leaching. Moreover, the synthesized catalyst exhibited favorable magnetic properties, enabling facile separation by magnets for efficient recycling and reutilization.

Complex-Valued Noisy Sinewaves Frequency Estimator Based on DTFT Linearization and Interpolation

This paper proposes an Interpolated Discrete-Time Fourier Transform (LIpDTFT) estimator for complex-valued noisy sinewave frequency based on the Linearization of the DTFT module behavior around the spectrum peak. It belongs to a class of LIpDTFT estimators that compensate the poor Discrete Fourier Transform (DFT) frequency resolution by interpolating two DTFT samples located very close to an initial frequency estimate. As compared with other DTFT-based frequency estimators recently proposed in the literature, the proposed LIpDTFT estimator ensures a smaller interpolation error and a lower processing effort. Moreover, if the rectangular window is adopted, it almost attains the unbiased Cramér-Rao Lower Bound (CRLB) even if a very small number of samples is analyzed. The accuracies ensured in different operating conditions by the proposed frequency estimator and other state-of-the-art DTFT-based algorithms are compared to each other through computer simulations.

Development of a 90–600 MHz Meter-wave Solar Radio Spectrometer

Radio observation is important for understanding coronal mass ejections (CMEs), coronal shock waves, and high-energy electron acceleration. Here, we developed a new Chashan broadband solar radio spectrometer at a meter wavelength for observing the (super)fine structure of the solar radio burst spectrum. In the signal-receiving unit, we adopt an antenna system consisting of a 12 m large-aperture parabolic reflector and dual-line polarized logarithmic periodic feed source, as well as a high-precision Sun-tracking turntable system, all of which ensure the high-precision acquisition of solar radiation signals. For the digital receiver, we use a high-speed analog-to-digital converter with a sampling rate of 1.25 GSPS to directly sample the signal amplified and filtered by the analog receiver, simplifying the structure of the analog receiver, and design a 16k-point fast Fourier transform algorithm in the field programmable gate array to perform time–frequency transformation on the sampled signals. The default frequency and temporal resolution of the system are 76.294 kHz and 0.839 ms (up to 0.21 ms), respectively. The noise coefficient of the system is less than 1 dB, the dynamic range is more than 60 dB, and the sensitivity is as high as 1 sfu. We have observed a large number of radio bursts, including type I radio storms, hundreds of type III, ∼20 type II, and ∼15 type IV bursts in the past year. These high-quality data are useful in the further study of CMEs and associated particle acceleration and the origins of solar radio bursts.

Semi-Analytical and Data-Driven Models of Electric Field Strength Considering the Actual Structure of Medium Frequency Transformers

Semi-Analytical and Data-Driven Models of Electric Field Strength Considering the Actual Structure of Medium Frequency Transformers

Review on a Power Quality Issues of a Distribution Transformer with Connected Load

In this paper a Voltage control system for power distribution which can solve the power quality problem in distribution. the proposed system consists of a frequency transformer connected to a power transformer receiving connection from both sides. this automatic connection is achieved using high or medium frequency transformers. The input of the converter is single-phase alternating current and the output is single-phase alternating current with different voltage amplitudes. the output voltage is stabilized by a closed-loop controller that controls the voltage at one level. The proposed system meets the smart distribution in terms of improved ability, protection of equipment and efficiency

Implementation of variable frequency transformer for integration of stand alone wind energy conversion system with battery energy storage

The conventional power electronics interface used for integration of wind energy conversion system (WECS) with load produces harmonics which deteriorates the quality of power supplied. To overcome this drawback, a variable frequency transformer (VFT) has been used as a link between WECS and load. For this, first a detailed steady-state modeling of VFT has been carried out. The derived model is then used for the analysis of the steady-state performance of VFT which acts as a link between WECS with battery energy storage and load for standalone operation. For this purpose, an equivalent circuit of VFT for standalone operation is developed. A set of equations for the output voltage and power delivered by VFT is derived from its equivalent circuit. The analytical solution of these equations helps in assessing the behavior of VFT. To validate the analytical results obtained from equivalent circuit, an experimental study is also carried out. The experimental study is conducted on a 3 phase, 400 V (L-L), 50 Hz, 2.2 kW, 1440 rpm, 4 pole, wound rotor induction machine. On comparison between analytical and experimental results, it is evident that both results are in good agreement. Further, the analysis is extended for variable load under various values of wind turbine generator speed and load power factor.

4.5 kW High Frequency Transformer Design for Dual Active Bridge (DAB)

4.5 kW High Frequency Transformer Design for Dual Active Bridge (DAB)

Phthalates Boost Natural Transformation of Extracellular Antibiotic Resistance Genes through Enhancing Bacterial Motility and DNA Environmental Persistence.

The environmental dissemination of extracellular antibiotic resistance genes (eARGs) in wastewater and natural water bodies has aroused growing ecological concerns. The coexisting chemical pollutants in water are known to markedly affect the eARGs transfer behaviors of the environmental microbial community, but the detailed interactions and specific impacts remain elusive so far. Here, we revealed a concentration-dependent impact of dimethyl phthalate (DMP) and several other types of phthalate esters (common water pollutants released from plastics) on the natural transformation of eARGs. The DMP exposure at an environmentally relevant concentration (10 μg/L) resulted in a 4.8-times raised transformation frequency of Acinetobacter baylyi but severely suppressed the transformation at a high concentration (1000 μg/L). The promotion by low-concentration DMP was attributed to multiple mechanisms, including increased bacterial mobility and membrane permeability to facilitate eARGs uptake and improved resistance of the DMP-bounded eARGs (via noncovalent interaction) to enzymatic degradation (with suppressed DNase activity). Similar promoting effects of DMP on the eARGs transformation were also found in real wastewater and biofilm systems. In contrast, higher-concentration DMP suppressed the eARGs transformation by disrupting the DNA structure. Our findings highlight a potentially underestimated eARGs spreading in aquatic environments due to the impacts of coexisting chemical pollutants and deepen our understanding of the risks of biological-chemical combined pollution in wastewater and environmental water bodies.

Factors influencing Agrobacterium-mediated genetic transformation efficiency in bread wheat (Triticum aestivum L.)

Assessment of the efficiency of Agrobacterium tumefaciens mediated transformation in four bread wheat varieties was focused and upon optimization of experiments done to obtain tangible results. The four varieties, Fielder, Navojoa, Baj and Kachu/saul, were transformed with optimized protocols and randomly selected from a selection medium amalgamated with phosphinothricin (Glufosinate). Initially, the variety Fielder has proceeded with 36 different concentrations of synthetic auxins 2,4-D and picloram as 36 treatments. The treatment 30 (48 μg of 2,4-D and 64 μg of picloram) has shown higher efficiency and is comparable in terms of the three traits studied. Additionally, antioxidants and growth regulators were adjusted to obtain better results towards reasonable transformation frequency. Additional varieties were also tested and selected against the selectable marker PPT based on the optimization for the variety Fielder. Selected genotypes were subjected for gusA or Bar gene PCR amplification. The result revealed the frequency of transgenesis in the range of 60 to 70% with the varieties studied. However, the whole experiment needs further repetitions for confirmatory results. Transgenic genotypes of the Fielder variety showed moderate to strong GUS expression in leaves, anthers, seeds, seed coats, and roots compared to non-transgenic control plant tissues. PPT leaf painting assay showed the lack of necrosis on the painted area of leaves in transgenic genotypes compared to the non-transgenic ones.

Simple DFS and AOA simultaneous measurement scheme without directional ambiguity with co-frequency self-interference signal cancellation

A photonic method based on a dual-polarization dual-parallel Mach–Zehnder modulator (DPol-DPMZM) for the simultaneous measurement of the Doppler frequency shift (DFS) and angle of arrival (AOA) of microwave signals is proposed and demonstrated by simulation. The upper arm of each sub-DPMZM is driven by the echo and self-interference signals from the antenna, while the lower arm is driven by the reference signal 1 and reference signal 2. The phase and amplitude of the reference signal 1 are adjusted to match the interference signals for achieving the self-interference cancellation (SIC). At the central office (CO), the DFS and AOA can be acquired in real time without directional ambiguity by processing the two downconverted low-frequency tones in the photocurrent. The simulation results show that the presence of the SI signal will seriously interfere with the observation of the SOI frequency and waveform, and the self-interference cancellation depth of about 42 dB can be obtained after the SIC. The measurement errors of the DFS without direction ambiguity are within 0.2 Hz. After the Hilbert transformation of the intermediate frequency (IF) signal, the AOA can be measured from −87.31∘ to +87.31∘ with errors less than 3.9°. The system has a large bandwidth, excellent real-time performance, and better invisibility, and is expected to be used in modern electronic warfare systems.

Physics-Guided Deep Learning for Plate Permeability Estimation With Single to Multiple Frequency Transformation of Eddy-Current Testing

Physics-Guided Deep Learning for Plate Permeability Estimation With Single to Multiple Frequency Transformation of Eddy-Current Testing

Using transcriptome analysis to reveal the effects of bromoacetamide at drinking water ambient concentrations on the transformation of eDNA carrying ARGs

Antibiotic resistance genes (ARGs) are frequently detected in drinking water. Transformation of extracellular antibiotic resistance genes (eARGs) is an important pathway for horizontal gene transfer, which facilitates the spread of ARGs among different species. Although most studies have pointed out that the presence of some common carbon-containing disinfection by-products, such as trihalomethanes, can increase the frequency of transformation by increasing the content of reactive oxygen species (ROS), nitrogen-containing disinfection by-products (N-DBPs), such as bromoacetamide (BAcAm), can also induce bacterial resistance to antibiotics by stimulating ROS. However, it is unclear whether such N-DBPs can similarly promote the transformation of ARGs in bacteria. In this study, the effect of BAcAm on the transformation of eDNA carrying ARGs was investigated, and found that DNA integration and repair (ruv, rec), efflux pumps (emr), and genes related to membrane proteins (bam, opm) were up-regulated in E. coli after addition of BAcAm. Furthermore, in addition to analyzing common functional genes, in terms of metabolic gene expression, which is usually overlooked, the genes related to amino acid biosynthesis (pot, arg, lys) were up-regulated while genes related to sulfur metabolism, which has a higher metabolic cost (thi, sssu) were down-regulated, which suggests that E. coli is able to effectively defend itself against the threat posed by BAcAm to its survival. In conclusion, the factors affecting the conversion of eDNA carrying ARGs into E. coli cells by BAcAm were suggested and elaborated that E. coli carrying ARGs can adapt to various unfavorable environments by regulating relevant metabolic mechanisms and survive in drinking water environment for a long period of time.

Frequency, Severity, Risk Factors, and Outcome of Hemorrhagic Transformation in Anterior and Posterior Stroke.

Background: There are few data on hemorrhagic transformation in posterior circulation strokes (PCS) compared to anterior circulation strokes (ACS). The aim of this study was to retrospectively analyze the incidence of hemorrhagic transformation, its different subtypes, the associations with different risk factors, and the outcome of ACS and PCS patients. Methods: A retrospective analysis of consecutive ischemic stroke patients with hemorrhagic transformation was performed. Clinical and demographic data were collected from electronic patient records. Results: Included were 186 ACS patients and 67 PCS patients. The median age was 77 years, with PCS patients being slightly younger than ACS patients. ACS patients were more likely to be treated with acetylsalicylic acid before stroke. ACS and PCS patients had comparable frequencies and severity of hemorrhagic transformation. After excluding ACS patients who received thrombectomy, PCS patients developed hemorrhagic transformation more frequently compared to ACS patients. Risk factors for hemorrhagic transformation did not differ between ACS and PCS patients and included vitamin K antagonist use before stroke and thrombectomy in ACS patients. There was no correlation between hemorrhagic transformation and stroke outcome. Conclusions: Hemorrhagic transformation occurs with similar frequency in PCS and ACS patients but is more common in PCS patients after the exclusion of ACS patients undergoing thrombectomy.

FSformer: A combined frequency separation network and transformer for LDCT denoising

Low-dose computed tomography (LDCT) has been widely concerned in the field of medical imaging because of its low radiation hazard to humans. However, under low-dose radiation scenarios, a large amount of noise/artifacts are present in the reconstructed image, which reduces the clarity of the image and is not conducive to diagnosis. To improve the LDCT image quality, we proposed a combined frequency separation network and Transformer (FSformer) for LDCT denoising. Firstly, FSformer decomposes the LDCT images into low-frequency images and multi-layer high-frequency images by frequency separation blocks. Then, the low-frequency components are fused with the high-frequency components of different layers to remove the noise in the high-frequency components with the help of the potential texture of low-frequency parts. Next, the estimated noise images can be obtained by using Transformer stage in the frequency aggregation denoising block. Finally, they are fed into the reconstruction prediction block to obtain improved quality images. In addition, a compound loss function with frequency loss and Charbonnier loss is used to guide the training of the network. The performance of FSformer has been validated and evaluated on AAPM Mayo dataset, real Piglet dataset and clinical dataset. Compared with previous representative models in different architectures, FSformer achieves the optimal metrics with PSNR of 33.7714 dB and SSIM of 0.9254 on Mayo dataset, the testing time is 1.825 s. The experimental results show that FSformer is a state-of-the-art (SOTA) model with noise/artifact suppression and texture/organization preservation. Moreover, the model has certain robustness and can effectively improve LDCT image quality.

Direct single‐phase isolated improved gamma Z‐source AC–AC converter suitable for DVR application

AbstractA direct single‐phase isolated improved gamma Z‐source AC–AC converter is designed and manufactured in proposed paper. This converter contains an impedance source structure and its advantages. In general, these advantages are as follows. Firstly, the proposed structure has a wide range of output voltage as boost and buck as well as in‐phase or out‐of‐phase states. Secondly, the proposed converter is electrically isolated thanks to a high frequency transformer (HFT), which significantly reduces the volume of the structure; therefore, in applications such as dynamic voltage restorer (DVR), there is no need to install a coupling transformer with the grid frequency which is very bulky and full of losses. Furthermore, the HFT of the proposed converter is a three winding transformer which also acts as the inductance needed for impedance source network. In addition, the safe commutation method has been utilized, which reduces the voltage spikes on the power switches. Finally, the analysis and design of this converter has been done, and then confirmed by a small‐scale laboratory prototype.

Analysis of single switch step up DC-DC converter with switched inductor-switched capacitor cells for PV system

The presented work exhibits high gain and increased efficiency for DC-DC converter. Additionally, this topology significantly improves the voltage conversion ratio when compared with other DC-DC converters reported recently. The non-existence of high frequency transformer ensures compactness and low cost and henceforth, it is apt for clean energy applications. The analysis of the high gain converter in steady state is carried out in continuous conduction mode (CCM). Initially, the proposed converter performance is analyzed using MATLAB/Simulink platform and prototype of the same with a power rating of 200 V, 100 W is built and tested. The reliability and robustness of the converter is perceived from the experimental results and peak efficiency achieved is around 93%.