Generation Technique Research Articles

Power Harmonic Suppression based direct vector control for robust DFIG operation during grid voltage distortions

AbstractThis paper proposes a Power Harmonic Suppression (Damping) (PHS) based reference current generation scheme, which focuses on simultaneous mitigation of harmonic pulsations in electromagnetic torque, stator real and reactive powers, and DC‐link voltage while retaining sinusoidal stator and grid currents and unity power factor at grid side. The crux of this PHS method lies in its ability to eliminate the harmonic powers developed in the DFIG stator and grid at 100Hz, 200Hz, 300Hz, 400Hz, and 600 Hz owing to second, fifth, and seventh harmonic grid voltages. This is achieved as a result of the elimination of natural fluxes developed in the DFIG air gap due to second, fifth, and seventh harmonic distortions in grid voltage. For this, a single concurrent reference current generation technique is developed for RSC and GSC to mitigate the oscillations of electromagnetic torque, stator real and reactive powers, and ripples in DC‐link voltage. Further, the reference rotor currents (positive, negative, fifth, and seventh sequence) are compared with the actual rotor currents using a PI controller to retain sinusoidal stator and grid currents. The proposed scheme's efficacy is validated with PSCAD/EMTDC simulations and using OPAL‐RT OP4510 real‐time simulator, on a 2.3 kVA DFIG.

How to improve ChatGPT performance for nephrologists: a technique guide.

The integration of ChatGPT into nephrology presents opportunities for enhanced decision-making and patient care. However, refining its performance to meet the specific needs of nephrologists remains a challenge. This guide offers a strategic roadmap for advancing ChatGPT's effectiveness in nephrological applications. Utilizing the advanced capabilities of GPT-4, we customized user profiles to optimize the model's response quality for nephrological inquiries. We assessed the efficacy of chain-of-thought prompting versus standard prompting in delineating the diagnostic pathway for nephrogenic diabetes insipidus-associated hypernatremia and polyuria. Additionally, we explored the influence of integrating retrieval-augmented generation on the model's proficiency in detailing pharmacological interventions to decelerate the progression from chronic kidney disease (CKD) G3 to end-stage kidney disease (ESKD), comparing it to responses without retrieval-augmented generation. In contrast to the standard prompting, the chain-of-thought method offers a step-by-step diagnostic process that mirrors the intricate thought processes needed for diagnosing nephrogenic diabetes insipidus-related hypernatremia and polyuria. This begins with an initial assessment, notably including a water deprivation test. After evaluating the outcomes of this test, the approach continues by identifying potential causes. Furthermore, if a patient's history suggests lithium usage, the chain-of-thought model adjusts by proposing a more customized course of action. In response to "List medication treatment to help slow progression of CKD G3 to ESKD?", GPT-4 only provides a general summary of medication options. Nevertheless, a specialized GPT-4 model equipped with a retrieval-augmented generation system delivers more precise responses, including renin-angiotensin system inhibitors, sodium-glucose cotransporter-2 inhibitors, and mineralocorticoid receptor antagonists. This aligns well with the 2024 KDIGO guidelines. GPT-4, when integrated with chain-of-thought prompting and retrieval-augmented generation techniques, demonstrates enhanced performance in the nephrology domain. This guide underscores the transformative potential of chain-of-thought and retrieval-augmented generation techniques in optimizing ChatGPT for nephrology, and highlights the ongoing need for innovative, tailored AI solutions in specialized medical fields.

Evaluation of a concentrated solar power-driven system designed for combined production of cooling and hydrogen

The current technique of hydrogen generation and cooling production raises climatic problems, which calls for the investigation of cleaner alternatives. No research has been conducted to propose and analyze a solar thermal power cycle-based water electrolysis for on-site production of hydrogen and the detailed exergy analysis of a series flow double-effect absorption chilling machine employed for large solar cooling. In this regard, a helically coiled tube embedded central receiver is used in the heliostat field for the simultaneous production of solar cooling and green hydrogen to meet the increasing needs of energy and fuel sustainably. The combined system is composed of four sub-systems including a heliostat field, LiBr–H2O operated series-flow double effect absorption refrigeration cycle (DEARC), organic Rankine cycle (ORC), and proton exchange membrane (PEM) electrolyzer. The system was examined from energy and exergy perspectives in the Engineering Equation Solver (EES) using library data of REFPROP for obtaining the thermodynamic properties of working fluids. The results of the thermodynamic model for the combined system are validated. Furthermore, the effect of ambient temperature, solar irradiation, and type of ORC working fluid on the hydrogen and cooling production, and the overall energy and exergy efficiency of the combined system are examined. A computational fluid dynamics simulation applying ANSYS-FLUENT was used to examine how coil curvature ratio and coil torsion affect solar heat transfer fluid (SHTF) pressure and temperature leaving the receiver. The coil curvature ratio affects temperature increase more than coil torsion. Solar heat transfer fluid outlet temperature increases by 36.9 % when the coil curvature ratio increases from 0.063 to 0.095 at direct normal irradiations (DNI) 1200 W/m2, input temperature of 92°C, and coil torsion of 0.032. The response of the cogeneration cycle to varying operating parameters is examined. Application of R141b as the ORC fluid results in a large increase of cooling exergy efficiency from 14.98 % to 63.48 % when the ambient temperature is increased from 15 °C to 45 °C whereas the exergy efficiency of the combined system is marginally improved. The exergy distribution presents for ORC operating on R141b, out of 100 % input solar exergy, 16.42 % is generated as hydrogen exergy and 13.67 % cooling exergy, 66.79 % is the exergy destroyed, and the rest 3.12 % is exergy loss. Exergy analysis is utilized to identify the sources of losses in useful energy within the components of the system considered and provides a more realistic and meaningful assessment than the traditional energy analysis. The results justify a need for the promotion of exergy analysis through policy decisions in the context of the global energy and environment crisis.

Time conditioning for arbitrary contrast phase generation in interventional computed tomography

Minimally invasive ablation techniques for renal cancer are becoming more popular due to their low complication rate and rapid recovery period. Despite excellent visualisation, one drawback of the use of computed tomography (CT) in these procedures is the requirement for iodine-based contrast agents, which are associated with adverse reactions and require a higher x-ray dose. The purpose of this work is to examine the use of time information to generate synthetic contrast enhanced images at arbitrary points after contrast agent injection from non-contrast CT images acquired during renal cryoablation cases. To achieve this, we propose a new method of conditioning generative adversarial networks with normalised time stamps and demonstrate that the use of a HyperNetwork is feasible for this task, generating images of competitive quality compared to standard generative modelling techniques. We also show that reducing the receptive field can help tackle challenges in interventional CT data, offering significantly better image quality as well as better performance when generating images for a downstream segmentation task. Lastly, we show that all proposed models are robust enough to perform inference on unseen intra-procedural data, while also improving needle artefacts and generalising contrast enhancement to other clinically relevant regions and features.

Exploring Innovative Approaches to Synthetic Tabular Data Generation

The rapid advancement of data generation techniques has spurred innovation across multiple domains. This comprehensive review delves into the realm of data generation methodologies, with a keen focus on statistical and machine learning-based approaches. Notably, novel strategies like the divide-and-conquer (DC) approach and cutting-edge models such as GANBLR have emerged to tackle a spectrum of challenges, spanning from preserving intricate data relationships to enhancing interpretability. Furthermore, the integration of generative adversarial networks (GANs) has sparked a revolution in data generation across sectors like healthcare, cybersecurity, and retail. This review meticulously examines how these techniques mitigate issues such as class imbalance, data scarcity, and privacy concerns. Through a meticulous analysis of evaluation metrics and diverse applications, it underscores the efficacy and potential of synthetic data in refining predictive models and decision-making software. Concluding with insights into prospective research trajectories and the evolving role of synthetic data in propelling machine learning and data-driven solutions across disciplines, this work provides a holistic understanding of the transformative power of contemporary data generation methodologies.

Characterization of cracking-healing cycle of asphalt mixture based on air-coupled second harmonic measurement using Duffing-van der pol oscillator

Second harmonic generation (SHG) technique using Rayleigh surface wave has been successfully attempted for the non-destruction evaluation of asphalt mixture during the self-healing process. The air-coupled transducer placement is desirable for the convenient implementation of SHG for the large-scale detection. Nevertheless, the pronounced attenuation of ultrasonic wave by air-coupled propagation will cause the second harmonic induced by microcracks to be barely detectable, particularly under the influence of inevitable noise. In this study, a novel approach by combining Duffing oscillator and van der pol equation is developed to identify and quantify the second harmonic in noise-contained signals based on the phase trajectory and periodic trajectory area exponent. The multi-physics finite element model is first established to simulate the air-asphalt propagation of Rayleigh waves, and proof-of-concept numerical simulations are carried out to estimate the second harmonic amplitude of noise-contaminated signals. The air-coupled SHG experiments are conducted, and the approach of the Duffing-van der pol oscillator is applied to extract the second harmonic in experimental data, and the self-healing behavior of asphalt mixture is analyzed through the variation of the nonlinear parameter of SHG measurements. The results indicate that the contactless SHG method combined with the Duffing-van der pol oscillator can effectively realize the convenient and reliable estimation of the nonlinear ultrasonic signal during the cycle of asphalt deterioration and healing, which is important for the property characterization of asphalt mixture considering its high attenuation to the ultrasonic waves.

Wideband Low Phase-Noise Signal Generation Using Coaxial Resonator in Cascaded Phase Locked Loop

The generation of high-quality wideband frequency sweeps presents a significant challenge, particularly in modern telecommunication, radar, and measurement systems where miniaturization is paramount. While phase-locked loops (PLLs) have become the dominant technique for signal generation, their application in broadband sweeps necessitates fractional-N operation. This, in turn, degrades phase noise and introduces unwanted spurs. This paper proposes a novel approach for broadband signal generation. By cascading two PLLs and utilizing a coaxial resonator, we achieve a high-performance oscillator that operates without the excessive fractional spurs, maintaining their level below −80 dBc across the entire frequency band. The prototype demonstrates non-degraded phase noise performance, reaching −102 dBc/Hz at 100 kHz offset and −121 dBc/Hz at 1 MHz offset for signals at 10 GHz. Despite significant frequency jumps, our design achieves lock times below 41 µs. These results, supported by theoretical analysis, validate the proposed method’s effectiveness in generating low-noise broadband frequency sweeps, ideal for local oscillator applications.

Lead Generation Methodologies and Strategies

The research challenge is first articulated, along with the necessity for more investigation into lead generation techniques and methodologies across sectors. The project's focus on the vital role that lead generation plays in fostering corporate sustainability and growth. This study intends to provide insights and solutions applicable to a wide range of sectors by analyzing the complexities and difficulties related to lead generation. The research explores traditional and digital lead generation channels commonly utilized in the automotive sector, including but not limited to, dealership networks, online advertising, social media marketing, content marketing, search engine optimization (SEO), and email campaigns. It evaluates the effectiveness of each method in terms of lead quantity, lead quality, cost-effectiveness, and conversion rates, considering their applicability to Suzuki's marketing goals and budgetary constraints. The present study in automobile sector with lead generation strategy is essential for car businesses to thrive in a competitive market. It helps identify effective ways to attract and convert potential buyers, ensuring sustained sales growth. This research aims to uncover key strategies and insights to improve customer acquisition in the automotive industry. The study incorporates statistical tools like 5-point Likert scale, Anova and Regression techniques are used with necessary recommendations.

Insights into Cold Plasma Treatment on the Cereal and Legume Proteins Modification: Principle, Mechanism, and Application.

Cereal and legume proteins, pivotal for human health, significantly influence the quality and stability of processed foods. Despite their importance, the inherent limited functional properties of these natural proteins constrain their utility across various sectors, including the food, packaging, and pharmaceutical industries. Enhancing functional attributes of cereal and legume proteins through scientific and technological interventions is essential to broadening their application. Cold plasma (CP) technology, characterized by its non-toxic, non-thermal nature, presents numerous benefits such as low operational temperatures, lack of external chemical reagents, and cost-effectiveness. It holds the promise of improving proteins' functionality while maximally retaining their nutritional content. This review delves into the pros and cons of different cold plasma generation techniques, elucidates the underlying mechanisms of protein modification via CP, and thoroughly examines research on the application of cold plasma in augmenting the functional properties of proteins. The aim is to furnish theoretical foundations for leveraging CP technology in the modification of cereal and legume proteins, thereby enhancing their practical applicability in diverse industries.

Local Ordering, Distortion, and Redox Activity in (La0.75Sr0.25)(Mn0.25Fe0.25Co0.25Al0.25)O3 Investigated by a Computational Workflow for Compositionally Complex Perovskite Oxides.

Mixing multiple cations can result in a significant configurational entropy, offer a new compositional space with vast tunability, and introduce new computational challenges. For applications such as the two-step solar thermochemical hydrogen (STCH) generation techniques, we demonstrate that using density functional theory (DFT) combined with Metropolis Monte Carlo method (DFT-MC) can efficiently sample the possible cation configurations in compositionally complex perovskite oxide (CCPO) materials, with (La0.75Sr0.25)(Mn0.25Fe0.25Co0.25Al0.25)O3 as an example. In the presence of oxygen vacancies (VO), DFT-MC simulations reveal a significant increase of the local site preference of the cations (short-range ordering), compared to a more random mixing without VO. Co is found to be the redox-active element and the VO is the preferentially generated next to Co due to the stretched Co-O bonds. A clear definition of the vacancy formation energy (Evf) is proposed for CCPO in an ensemble of structures evolved in parallel from independent DFT-MC paths. By combining the distribution of Evf with VO interactions into a statistical model, the oxygen nonstoichiometry (δ), under the STCH thermal reduction and oxidation conditions, is predicted and compared with the experiments. Similar to the experiments, the predicted δ can be used to extract the enthalpy and entropy of reduction using the van't Hoff method, providing direct comparisons with the experimental results. This procedure provides a full predictive workflow for using DFT-MC to obtain possible local ordering or fully random structures, understand the redox activity of each element, and predict the thermodynamic properties of CCPOs, for computational screening and design of these CCPO materials at STCH conditions.

Spatiotemporal Inconsistency Learning and Interactive Fusion for Deepfake Video Detection

With the rise of the metaverse, the rapid advancement of Deepfakes technology has become closely intertwined. Within the metaverse, individuals exist in digital form and engage in interactions, transactions, and communications through virtual avatars. However, the development of Deepfakes technology has led to the proliferation of forged information disseminated under the guise of users’ virtual identities, posing significant security risks to the metaverse. Hence, there is an urgent need to research and develop more robust methods for detecting deep forgeries to address these challenges. This paper explores deepfake video detection by leveraging the spatiotemporal inconsistencies generated by deepfake generation techniques, and thereby proposing the interactive spatioTemporal inconsistency learning and interactive fusion (ST-ILIF) detection method, which consists of phase-aware and sequence streams. The spatial inconsistencies exhibited in frames of deepfake videos are primarily attributed to variations in the structural information contained within the phase component of the Fourier domain. To mitigate the issue of overfitting the content information, a phase-aware stream is introduced to learn the spatial inconsistencies from the phase-based reconstructed frames. Additionally, considering that deepfake videos are generated frame-by-frame and lack temporal consistency between frames, a sequence stream is proposed to extract temporal inconsistency features from the spatiotemporal difference information between consecutive frames. Finally, through feature interaction and fusion of the two streams, the representation ability of intermediate and classification features is further enhanced. The proposed method, which was evaluated on four mainstream datasets, outperformed most existing methods, and extensive experimental results demonstrated its effectiveness in identifying deepfake videos. Our source code is available at https://github.com/qff98/Deepfake-Video-Detection

Portfolio Generator using Generative AI in Web Development

The rise of generative intelligence (AI) has led to significant transformations in various industries, notably web development. This paper introduces a unique strategy for creating personalized portfolios for web developers through the utilization of generative AI techniques. By exploiting generative AI models, developers can automate the process of generating distinct portfolio designs customized to individual preferences, resulting in saving time and effort. This paper delves into the methodology, execution, and potential advantages of utilizing generative AI in portfolio generation for web development.

Bridging Languages through Images: A Multilingual Text-to-Image Synthesis Approach

This research investigates the challenges posed by the predominant focus on English language text-to-image generation (TTI) because of the lack of annotated image caption data in other languages. The resulting inequitable access to TTI technology in non-English-speaking regions motivates the research of multilingual TTI (mTTI) and the potential of neural machine translation (NMT) to facilitate its development. The study presents two main contributions. Firstly, a systematic empirical study employing a multilingual multi-modal encoder evaluates standard cross-lingual NLP methods applied to mTTI, including TRANSLATE TRAIN, TRANSLATE TEST, and ZERO-SHOT TRANSFER. Secondly, a novel parameter-efficient approach called Ensemble Adapter (ENSAD) is introduced, leveraging multilingual text knowledge within the mTTI framework to avoid the language gap and enhance mTTI performance. Additionally, the research addresses challenges associated with transformer-based TTI models, such as slow generation and complexity for high-resolution images. It proposes hierarchical transformers and local parallel autoregressive generation techniques to overcome these limitations. A 6B-parameter transformer pretrained with a cross-modal general language model (CogLM) and fine-tuned for fast super-resolution results in a new text-to-image system, denoted as It, which demonstrates competitive performance compared to the state-of-the-art DALL-E-2. Furthermore, It supports interactive text-guided editing on images, offering a versatile and efficient solution for text-to-image generation.. Keywords: Text-to-image generation, Multilingual TTI (mTTI), Neural machine translation (NMT), Cross-lingual NLP, Ensemble Adapter (ENSAD), Hierarchical transformers, Super- resolution, Transformer-based models, Cross-modal general language model (CogLM).

Enhanced Solar Coronal Imaging: A GAN Approach with Fused Attention and Perceptual Quality Enhancement

The activity of the solar corona has a significant impact on all aspects of human life. People typically use images obtained from astronomical telescopes to observe coronal activities, among which the Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) is particularly widely used. However, due to resolution limitations, we have begun to study the application of generative adversarial network super-resolution techniques to enhance the image data quality for a clearer observation of the fine structures and dynamic processes in the solar atmosphere, which improves the prediction accuracy of solar activities. We aligned SDO/AIA images with images from the High-Resolution Coronal Imager (Hi-C) to create a dataset. This research proposes a new super-resolution method named SAFCSRGAN, which includes a spatial attention module that incorporates channel information, allowing the network model to better capture the corona’s features. A Charbonnier loss function was introduced to enhance the perceptual quality of the super-resolution images. Compared to the original method using ESRGAN, our method achieved an 11.9% increase in Peak Signal-to-Noise Ratio (PSNR) and a 4.8% increase in Structural Similarity (SSIM). Additionally, we introduced two perceptual image quality assessment metrics, the Natural Image Quality Evaluator (NIQE) and Learned Perceptual Image Patch Similarity (LPIPS), which improved perceptual quality by 10.8% and 1.3%, respectively. Finally, our experiments demonstrated that our improved model surpasses other models in restoring the details of coronal images.

Synthetic Data for Deep Learning in Computer Vision & Medical Imaging: A Means to Reduce Data Bias

Deep-learning (DL) performs well in computer-vision and medical-imaging automated decision-making applications. A bottleneck of DL stems from the large amount of labelled data required to train accurate models that generalise well. Data scarcity and imbalance are common problems in imaging applications that can lead DL models towards biased decision making. A solution to this problem is synthetic data. Synthetic data is an inexpensive substitute to real data for improved accuracy and generalisability of DL models. This survey reviews the recent methods published in relation to the creation and use of synthetic data for computer-vision and medical-imaging DL applications. The focus will be on applications that utilised synthetic data to improve DL models by either incorporating an increased diversity of data that is difficult to obtain in real life, or by reducing a bias caused by class imbalance. Computer-graphics software and generative networks are the most popular data generation techniques encountered in the literature. We highlight their suitability for typical computer-vision and medical-imaging applications, and present promising avenues for research to overcome their computational and theoretical limitations.

Enhancing medical image classification with generative AI using latent denoising diffusion probabilistic model and wiener filtering approach

In the evolving landscape of medical diagnostics, a paradigm shift is being catalysed by the advent of generative artificial intelligence. Medical X-ray, CT and MRI images are essential diagnostic tools used by healthcare professionals to assess various musculoskeletal conditions. However, obtaining a sufficient number of medical images for training deep learning models can be challenging due to limited access to labelled data. Hence, the authors propose a novel Latent diffusion process for synthesizing medical images that closely resemble real patient images, aiming to address the challenge of limited access to labelled data in medical diagnostics. Leveraging deep learning and generative modelling techniques, this method synthesizes high-fidelity images that closely mimic real patient scans. By introducing noise and subsequently training the model to denoise, the approach captures intricate patterns inherent in authentic medical images. Among the four datasets utilized, the Diabetes Retinopathy dataset demonstrates superior performance, achieving the highest Mean Structural Similarity Index (MSSIM) score of 0.57 (compared to the dataset baseline of 0.62) and an accuracy of 93.75% when passed through the proposed pipeline. The Cataract dataset, registered a MSSIM score of 0.51 (versus the dataset baseline of 0.53) and an accuracy score of 97.52%, while the Knee OA dataset follows closely with MSSIM and accuracy scores of 0.65 (in contrast to the dataset baseline of 0.63) and 68.66% respectively. The results obtained are then compared with the results generated by the other state of the art models.

Short-term wind speed interval prediction using improved quality-driven loss based gated multi-scale convolutional sequence model

Efficient estimation of the uncertainty associated with wind speed forecast is crucial for evaluating wind farms' power quality and operation. Typically, the performance of prediction interval (PI) generating models; is restricted in terms of computational efficiency inheritably from the sequential data processing; whereas restrictions in forecast quality are derived from the PI generation techniques. This paper presents an efficient Gated Multi-Scale Convolutional Sequence Model (GMSCSM) to forecast wind speed PIs. GMSCSM while conserving the ‘recurrence’ of LSTMs also offers ‘parallel input’ advantage of CNNs for better computational efficiency which is highly desirable for short-term forecasting models. In addition, capturing features at various scales in the sequence, GMSCSM learns both local details and global context which is vital for multi-horizon forecasts. The model generates quality PIs utilizing an improved quality-driven loss which we proposed by invoking calibration assessment in its existing definition. Forecasts generated for eight different datasets obtained from two different wind farms show an improvement of 30 % and 6 % in the average coverage width criterion index while reducing the model training time to nearly half and one third of the respective values obtained from traditional and LSTM based models which showcases the proposed model's excellent prediction capability and efficiency.

Quantum fluctuations lead to glassy electron dynamics in the good metal regime of electron doped KTaO3

One of the central challenges in condensed matter physics is to comprehend systems that have strong disorder and strong interactions. In the strongly localized regime, their subtle competition leads to glassy electron dynamics which ceases to exist well before the insulator-to-metal transition is approached as a function of doping. Here, we report on the discovery of glassy electron dynamics deep inside the good metal regime of an electron-doped quantum paraelectric system: KTaO3. We reveal that upon excitation of electrons from defect states to the conduction band, the excess injected carriers in the conduction band relax in a stretched exponential manner with a large relaxation time, and the system evinces simple aging phenomena—a telltale sign of glassy dynamics. Most significantly, we observe a critical slowing down of carrier dynamics below 35 K, concomitant with the onset of quantum paraelectricity in the undoped KTaO3. Our combined investigation using second harmonic generation technique, density functional theory and phenomenological modeling demonstrates quantum fluctuation-stabilized soft polar modes as the impetus for the glassy behavior. This study addresses one of the most fundamental questions regarding the potential promotion of glassiness by quantum fluctuations and opens a route for exploring glassy dynamics of electrons in a well-delocalized regime.

D-π-A chromophores based on novel macroacceptors - Fused (azinylmethylene)malononitriles: Linear and nonlinear optical properties in solution and in poled polymer films

D-π-A chromophores based on novel macroacceptors − fused azinylmethylenemalononitriles (fused azine = pyrido [2,3-b]pyrazine, quinoline, quinoxaline and quinoxalin-2-one) − have been synthesized and their linear and nonlinear optical properties in gas, solution and in poled PMMA-based films have been investigated by DFT, UV–vis and second harmonic generation technique. The transition from chromophores with an azine acceptor to ones with azinylmethylenemalononitrile acceptors leads to a 10-fold increase in calculated μβ values. Poled polymer films doped with studied chromophores at 25 wt% load exhibit quadratic nonlinear optical activity with d33 values up to 42 pm/V.

Advancements in efficient Terahertz generation techniques for diverse applications in spectroscopic studies

Advancements in efficient Terahertz generation techniques for diverse applications in spectroscopic studies