Generator Speed Research Articles

Delphi Method Consensus on Statistical Analysis and Reporting Recommendations for Single-Arm Pregnancy Medication Safety Studies Investigating Pregnancy, Birth and Neonatal Health Outcomes: A Contribution from IMI-ConcePTION.

Standardised procedures for performing and reporting safety monitoring studies investigating medications use in pregnancy may help improve data quality and the speed of data generation. The objective of this study was to provide recommendations on the statistical analysis and reporting of single-arm pregnancy medication safety studies using primary source datasets. A Delphi consensus-setting protocol was used to acquire agreement on recommendations from experts with extensive knowledge and experience in conducting studies investigating medication safety in pregnancy. A series of recommendations, along with their scientific justifications and examples of how to calculate and describe exposure and outcome incidences, were critiqued and improved through a series of online Delphi review rounds. Agreement to inclusion scoring was assessed using a five-point Likert scale. Recommendations with a median Likert-scale score of at least 4, where ≥ 80% of the expert panel scored the recommendation at level 4 or higher, was used as the threshold for inclusion. The Delphi consensus methodology produced a set of 30 recommendations spread over five themes. These included descriptions of (1) study sample, (2) medication exposure, (3) maternal outcomes, (4) pregnancy and birth outcomes, and (5) fetal and neonatal outcomes. Of the 30 recommendations, 19 were strongly advised while 11 were included for consideration where their implementation may be beneficial for supplementing data communication. Use of the finalised set of recommendations should be encouraged to help standardise published evidence around medication use in pregnancy.

Постквантовая подпись Меркла на основе модифицированного алгоритма Лампорта

The development of a Merkle post-quantum signature scheme based on a modified Lamport one-time signature algorithm is presented. The Merkle signature algorithm and the Lamport one-time signature algorithm are described. There is also a review of the current literature on the subject of the Merkle signature algorithm. A modified algorithm for Lamport's one-time electronic digital signature is described. The algorithms for key generation, signature generation, and verification of a previously generated signature are described in detail. The paper presents a software implementation of an electronic digital signature system with a graphical interface based on the developed algorithm, which allows key generation, signature generation and verification. A flowchart is provided for each of the main modules of the program, and the graphical interface of the developed software for each module is also demonstrated. The results of testing the modified algorithm are presented and its performance is compared with the standard algorithm. The test results confirm that using the modified algorithm allows faster verification of messages, while the speed of key generation and signature does not increase in comparison with the standard algorithm. The modified algorithm speeds up verification regardless of the message length. The results of the performed research are a modified Lamport one-time signature algorithm, which provides a higher signature verification rate compared to the classical algorithm, and a software tool with a graphical interface for generating and verifying a post-quantum electronic digital signature.

Dynamical metastability and re-entrant localization of trapped active elements with speed and orientation fluctuations

We explore the dynamics of active elements performing persistent random motion with fluctuating active speed and in the presence of translational noise in a d-dimensional harmonic trap, modeling active speed generation through an Ornstein-Uhlenbeck process. Our approach employs an exact analytic method based on the Fokker-Planck equation to compute time-dependent moments of any dynamical variable of interest across arbitrary dimensions. We analyze dynamical crossovers in particle displacement before reaching the steady state, focusing on three key timescales: speed relaxation, persistence, and dynamical relaxation in the trap. Notably, for slow active speed relaxation, we observe an intermediate-time metastable saturation in the mean-squared displacement before reaching the final steady state. The steady-state distributions of particle positions exhibit two types of non-Gaussian departures based on control parameters: bimodal distributions with negative excess kurtosis and heavy-tailed unimodal distributions with positive excess kurtosis. We obtain detailed steady-state phase diagrams using the exact calculation of excess kurtosis, identifying Gaussian and non-Gaussian regions and potential re-entrant transitions. Published by the American Physical Society 2025

Modeling and Evaluation of Forecasting Models for Energy Production in Wind and Photovoltaic Systems

The comprehensive change from known, classical energy production methods to the increased use of renewable energy requires new methods in the field of efficient application and use of renewable energy. The urban energy supply presents complex challenges in improving efficiency; therefore, the prediction of the dynamical availability of energy is required. Several approaches have been explored, including statistical models and machine learning using historical data and numerical weather prediction models using mathematical models of the atmosphere and weather conditions. Accurately forecasting renewable energy production involves analyzing factors such as related weather conditions, conversion systems, and their locations, which influence both energy availability and yield. This study focuses on the short-term forecasting of wind and photovoltaic (PV) energy using historical data and machine learning approaches, aiming for accurate 8 h predictions. The goal is to develop models capable of producing accurate short-term forecasts of energy production from both resources (solar and wind), suitable for later use in a model predictive control scheme where generation and demand, as well as storage, must be considered together. Methods include regression trees, support vector regression, and regression neural networks. The main idea in this work is to use past and future information in the model. Inputs for the PV model are past PV generation and future solar irradiance, while the wind model uses past wind generation and future wind speed data. The performance of the model is evaluated over the entire year. Two scenarios are tested: one with perfect future predictions of wind speed and solar irradiance, and another considered realistic situation where perfect future prediction is not possible, and uncertain prediction is accounted for by incorporating noise models. The results of the second scenario were further improved using the output filtering method. This study shows the advantages and disadvantages of different methods, as well as the accuracy that can be expected in principle. The results show that the regression neural network has the best performance in predicting PV and wind generation compared to other methods, with an RMSE of 0.1809 for PV and 5.3154 for wind, and a Pearson coefficient of 0.9455 for PV and 0.9632 for wind.

Predicting and synthesizing terahertz spoof surface plasmon polariton devices with a convolutional neural network model

With the increasing global attention to deep learning and the advancements made in applying convolutional neural networks in electromagnetics, we have recently witnessed the utilization of deep learning-based networks for predicting the spectrum and electromagnetic properties of structures instead of traditional tools like fully numerical-based methods. In this study, a Convolutional Neural Network (CNN is proposed for predicting spoof surface plasmon polaritons, enabling the examination of the absorption spectrum of metallic multilevel-grating structures (MMGS) and designing various sensor devices and absorbers in the shortest time possible. To expedite the training process of this network, a semi-analytical method of rigorous coupled-wave analysis (RCWA) enhanced with the fast Fourier factorization (FFF) technique has been employed, significantly reducing the data generation time for training. This CNN enables the prediction of existing spoof surface plasmon polaritons (SSPPs) and their intensity within a 110% frequency range. In addition to the speed of dataset generation, the simple framework of this network has also facilitated the training of the network in a short time. By comparing the network in this study with previous works, it is apparent that in addition to structural and geometrical changes in the unit cell, the designer is afforded greater freedom in determining the material of the incident medium and, for the first time, specifying the angle of incidence of the source. Finally, for the validation of the suggested network, the predictive power of the absorption spectrum of various structures is compared with traditional methods. Three examples are provided for inversely designing several sensor devices and absorbers in the terahertz band using the proposed CNN and the genetic optimization algorithm.

Face Image Generation for Anime Characters based on Generative Adversarial Network

With the increasing demand for digital art, animation, and games, facial generation for anime characters has attracted growing research interest in recent years, which aims to build models to automatically generate unique and high-quality character images. Thanks to the rapid advancement of deep learning techniques, particularly generative adversarial networks, GAN-based image generation methods have continuously achieved breakthroughs in generation effectiveness and speed. Focusing on generating realistic anime face images, this paper proposes an anime character face image generation model based on GANs, which integrates Bath Normalization and Dropout to maintain strong stability and avoid overfitting. Comprehensive experiments show the efficacy of the proposed method, which can achieve high diversity in facial features and styles while maintaining visual coherence

3DSMILES-GPT: 3D molecular pocket-based generation with token-only large language model.

The generation of three-dimensional (3D) molecules based on target structures represents a cutting-edge challenge in drug discovery. Many existing approaches often produce molecules with invalid configurations, unphysical conformations, suboptimal drug-like qualities, limited synthesizability, and require extensive generation times. To address these challenges, we present 3DSMILES-GPT, a fully language-model-driven framework for 3D molecular generation that utilizes tokens exclusively. We treat both two-dimensional (2D) and 3D molecular representations as linguistic expressions, combining them through full-dimensional representations and pre-training the model on a vast dataset encompassing tens of millions of drug-like molecules. This token-only approach enables the model to comprehensively understand the 2D and 3D characteristics of large-scale molecules. Subsequently, we fine-tune the model using pair-wise structural data of protein pockets and molecules, followed by reinforcement learning to further optimize the biophysical and chemical properties of the generated molecules. Experimental results demonstrate that 3DSMILES-GPT generates molecules that comprehensively outperform existing methods in terms of binding affinity, drug-likeness (QED), and synthetic accessibility score (SAS). Notably, it achieves a 33% enhancement in the quantitative estimation of QED, meanwhile the binding affinity estimated by Vina docking maintaining its state-of-the-art performance. The generation speed is remarkably fast, with the average time approximately 0.45 seconds per generation, representing a threefold increase over the fastest existing methods. This innovative 3DSMILES-GPT approach has the potential to positively impact the generation of 3D molecules in drug discovery.

Snowflake Data Warehouse for Large-Scale and Diverse Biological Data Management and Analysis.

With the increasing speed of genomic, transcriptomic, and metagenomic data generation driven by the advancement and widespread adoption of next-generation sequencing technologies, the management and analysis of large-scale, diverse data in the fields of life science and biotechnology have become critical challenges. In this paper, we thoroughly discuss the use of cloud data warehouses to address these challenges. Specifically, we propose a data management and analysis framework using Snowflake, a SaaS-based data platform. We further demonstrate its convenience and effectiveness through concrete examples, such as disease variant analysis and in silico drug discovery. By introducing Snowflake, researchers can efficiently manage and analyze a wide array of biological data, enabling the discovery of new biological insights through integrated analysis. Through these specific methodologies and application examples, we aim to accelerate research progress in the field of bioinformatics.

Application of artificial intelligence based on state grid ESG platform in clean energy scheduling optimization

The randomness and volatility of existing clean energy sources have increased the complexity of grid scheduling. To address this issue, this work proposes an artificial intelligence (AI) empowered method based on the Environmental, Social, and Governance (ESG) big data platform, focusing on multi-objective scheduling optimization for clean energy. This work employs a combination of Particle Swarm Optimization (PSO) and Deep Q-Network (DQN) to enhance grid scheduling efficiency and clean energy utilization. First, the work analyzes the complexity and uncertainty challenges faced in clean energy scheduling within the current power system, highlighting the limitations of traditional methods in handling multi-objective optimization and real-time response. Consequently, the work introduces the ESG big data platform and leverages its abundant data resources and computational power to improve the scheduling decision-making process. Next, PSO is used for initial scheduling optimization and a mathematical model for clean energy scheduling optimization is constructed. To further enhance the dynamic response capability of the scheduling process, this work designs a dual-layer architecture. Among this architecture, DQN is responsible for adjusting the PSO initially optimized scheduling plan based on real-time data during the actual scheduling process, adapting it to the instantaneous change demand and renewable energy output characteristics. Finally, to verify the model’s effectiveness, this work conducts simulation analyses using real data from the state grid ESG big data platform. The results indicate that this method significantly improves clean energy utilization, increasing it from 62.4% (with traditional methods) to 87.7%, while reducing scheduling costs by 22%. With the increase in communication delay, the generation time of scheduling instruction increases significantly. This is because the algorithm needs to wait longer to receive the complete grid status information, which affects the generation speed of scheduling instructions. Moreover, this method demonstrates good adaptability and stability under different load demands and clean energy supply conditions. This work introduces a novel and efficient method for clean energy scheduling optimization by integrating PSO and DQN. It contributes to the overall improvement of clean energy utilization within the State Grid and provides a theoretical and empirical foundation for further research in related fields.

Performance Validation of Control Algorithm Considering Independent Generator Torque Control in PCS

This study designed and validated a power control algorithm tailored to the unique characteristics of a 100 kW wind turbine equipped with an independent generator torque control system within the Power Conversion System (PCS). Unlike conventional power control methods based on the Programmable Logic Controller (PLC), the independent generator torque control in the PCS estimates the generator speed based on the voltage signal and performs the generator torque control without any interference from the PLC. The conventional power control algorithm in the PLC was modified so that the PLC could perform the power control by the blade pitch control only. Furthermore, a mode transition algorithm was designed to improve the shortcomings of the mode switch previously used for transitioning between the torque and pitch control modes. To verify the performance, a simulation environment similar to actual control conditions was established using a commercial analysis program, and dynamic simulations were conducted. Additionally, for the experimental validation, the proposed control algorithm was applied to a scaled wind turbine, and wind tunnel tests were performed. The results of the simulations and wind tunnel tests confirmed the operational performance of the PCS’s torque control and the improved control transition logic. The proposed control algorithm could be specifically applied to medium-sized wind turbines employing a sensorless generator speed estimation method and independent generator torque control in the PCS.

Multiple model predictive control for offshore wind turbines operating in the full‐load range

AbstractThis paper presents a multiple model predictive control (MMPC) strategy based on an individual blade pitch (IBP) mechanism to control floating offshore wind turbines (FOWTs) when operating in the full‐load range (above the rated wind speed). The strategy utilizes local model‐based predictive controllers (MPCs) to minimize generator output power fluctuations, generator speed variations, and alleviate tower mechanical loads, particularly fore‐aft and side‐to‐side shear forces. To ensure smooth transitions between local controllers, a soft‐switching technique is employed. The IBP actuation and generator torque control fix generator power and speed at the rated values, mitigating platform motions induced by wind and waves. The proposed control strategy is applied to a 5‐MW baseline wind turbine with a spar‐buoy platform developed by the National Renewable Energy Laboratory. Furthermore, a comparative analysis is conducted by designing an MMPC based on the collective blade pitch (CBP) mechanism. Through comprehensive simulation and analysis, the effectiveness of the IBP‐based MMPC strategy is demonstrated.

Pitch control for floating offshore wind turbines via model-dependent extended state observer-based active disturbance rejection control

Various types of disturbances bring significant challenges to the stable operation of floating offshore wind turbines. This paper formulates the pitch control of floating offshore wind turbines using a novel model-dependent extended state observer-based active disturbance rejection control, which can effectively estimate and compensate for total disturbances. Firstly, a Markov model is proposed to describe wind and wave combined external disturbances and the aerodynamic characteristics of floating offshore wind turbines. Then, an active disturbance rejection control is developed based on the constructed model-dependent extended state observer. The approach provided in this paper can adjust the pitch angle to ensure that the generator speed output tracks the desired reference value. Based on this, the multi verse optimization algorithm is introduced to optimize the parameters of the controller. Finally, the superiorities of the results presented in this paper are illustrated by a 5 MW floating offshore wind turbine model. Simulation results demonstrate that the model-dependent extended state observer-based active disturbance rejection control strategy can significantly adjust the pitch angle to guarantee the stable operation of floating offshore wind turbines under wind and wave combined external disturbances.

Wind Power Generation Data Forecasting

Wind power generation is highly variable due to fluctuating weather conditions, necessitating accurate forecasting to ensure grid stability and efficient energy management. This study explores the application of the Random Forest algorithm, a robust ensemble learning method, for forecasting wind power generation. Utilizing the algorithm the model is trained using wind speed and power generation data. to predict future power output. The algorithm's ability to handle non-linear relationships and interactions among multiple variables enhances its predictive accuracy. Results indicate that the Random Forest model outperforms traditional forecasting methods, providing more reliable and precise predictions, which are crucial for optimizing wind energy integration into the power grid

Power control of an autonomous wind energy conversion system based on a permanent magnet synchronous generator with integrated pumping storage

Wind energy plays a crucial role as a renewable source for electricity generation, especially in remote or isolated regions without access to the main power grid. The intermittent characteristics of wind energy make it essential to incorporate energy storage solutions to guarantee a consistent power supply. This study introduces the design, modeling, and control mechanisms of a self-sufficient wind energy conversion system (WECS) that utilizes a Permanent magnet synchronous generator (PMSG) in conjunction with a Water pumping storage station (WPS). The system employs Optimal torque control (OTC) to maximize power extraction from the wind turbine, achieving a peak power coefficient (Cp) of 0.43. A vector control strategy is applied to the PMSG, maintaining the DC bus voltage at a regulated 465 V for stable system operation. The integrated WPS operates in both motor and generator modes, depending on the excess or shortfall of generated wind energy relative to load demand. In generator mode, the WPS supplements power when wind speeds are insufficient, while in motor mode, it stores excess energy by pumping water to an upper reservoir. Simulation results, conducted in MATLAB/Simulink, show that the system efficiently tracks maximum power points and regulates key parameters. For instance, the PMSG successfully maintains the reference quadrature current, achieving optimal torque and power output. The system’s response under varying wind speeds, with an average wind speed of 8 m/s, demonstrates that the generator speed closely follows turbine speed without a gearbox, leading to efficient power conversion. The results confirm the flexibility and robustness of the control strategies, ensuring continuous power delivery to the load. This makes the system a feasible solution for isolated, off-grid applications, contributing to advancements in renewable energy technologies and autonomous power generation systems.

Comparative Analysis of Elliptic Curve-Based Cryptographic Approaches for Internet of Things Security

The rapid expansion of the Internet of Things (IoT) introduces significant security challenges, given the resource-constrained nature of most IoT devices. To address these challenges, elliptic curve cryptography (ECC) has emerged as a promising solution due to its ability to deliver high levels of security with shorter key lengths, making it highly efficient for devices with limited computational power and memory. This paper focuses on a comparative analysis of three widely used ECC-based cryptographic algorithms: Elliptic Curve Digital Signature Algorithm (ECDSA), Elliptic Curve Integrated Encryption Scheme (ECIES), and Elliptic Curve Diffie-Hellman (ECDH). Through a detailed evaluation of performance metrics such as key generation speed, execution time, and overall efficiency, the study identifies the strengths and limitations of each algorithm in securing IoT environments. The results reveal that ECDH excels in public key generation speed, making it suitable for applications requiring frequent key exchanges. ECDSA demonstrates the fastest overall execution time, providing an efficient option for digital signatures and authentication. Conversely, ECIES, while slower, offers robust encryption capabilities ideal for scenarios demanding enhanced confidentiality. This comparative study highlights the importance of aligning algorithm selection with specific IoT application requirements, balancing factors like security, performance, resource constraints, and operational complexity. The findings underscore the suitability of ECC-based algorithms in addressing the unique challenges of IoT security.

Magnetic Skyrmion and Unconventional Computing

As data generation and processing speeds have dramatically increased in the 21st century, conventional computing architecture is encountering its limitation. One promising candidate to address these issues is unconventional computing, which offers more efficient data processing and reduced energy consumption. This article focuses on magnetic skyrmions in the field of spintronics, a key platform for next-generation computing, and explores how they can be applied towards unconventional computing.

Fast delivery of heralded atom-photon quantum correlation over 12 km fiber through multiplexing enhancement

Distributing quantum entanglement between distant parties is a significant but difficult task in quantum information science, as it can enable numerous applications but suffers from exponential decay in the quantum channel. Quantum repeaters are one of the most promising approaches towards this goal. In a quantum repeater protocol, it is essential that the entanglement generation speed within each elementary link is faster than the memory decoherence rate, and this stringent requirement has not been implemented over a fiber of metropolitan scale so far. As a step towards this challenging goal, in this work we experimentally realize multiplexing-enhanced generation of heralded atom-photon quantum correlation over a 12 km fiber. We successively generate 280 pairs of atom-photon quantum correlations with a train of photonic time-bin pulses filling the long fiber, and read out the excited memory modes on demand with either fixed or variable storage time after successful heralding. With the multiplexing enhancement, the heralding rate of atom-photon correlation can reach 1.95 kHz, and the ratio between the quantum correlation generation rate to memory decoherence rate can be improved to 0.46 for a fiber length of 12 km. This work therefore constitutes an important step towards the realization of a large-scale quantum repeater network.

Enhancing the Digital Inheritance and Development of Chinese Intangible Cultural Heritage Paper-Cutting Through Stable Diffusion LoRA Models

With the advent of artificial intelligence digitization, intangible cultural heritage faces challenges in preservation and transmission. Utilizing modern technology to achieve digital protection and dissemination has become a crucial issue today. This study enhances the digital inheritance and development of Chinese intangible cultural heritage paper-cutting art through generative AI technologies, specifically Diffusion and LoRA models. The Analytic Hierarchy Process (AHP) was employed to categorize the cultural value of paper-cutting, selecting four core elements: “Spring Festival”, “Chinese Zodiac”, “Women”, and “Birds and Flowers”. Based on these, eight LoRA models were developed to generate paper-cutting-style patterns (using the FLUX.1-dev and Stable Diffusion 1.5 models). In the user satisfaction assessment, the Importance–Performance Analysis (IPA) method was used to analyze four dimensions of the model experience. The results indicate that the LoRA model excels in generating detailed paper-cutting patterns and accurately reproducing cultural elements, particularly in the generation of complex Chinese character designs. User feedback suggests that the LoRA model effectively enhances the digital representation and dissemination of paper-cutting art, though there is room for improvement in terms of generation speed and ease of operation. This study provides a new technological pathway for the digital preservation of intangible cultural heritage and promotes the modernization of paper-cutting art transmission.

A globally optimized fault diagnosis model based on generative flow model for imbalanced data

Abstract In the actual scenario of fault diagnosis based on deep learning, the diagnosis accuracy is often affected by the lack of fault state data, so the processing of imbalanced data is always a significant challenge. generative adversarial networks (GAN) and denoising diffusion probability models (DDPM) are widely used for data augmentation. However, GAN often shows sensitivity and instability in the training process, and the sample generation speed of DDPM is slow due to the steps requiring multiple iterations–both of which are limiting factors. To solve these problems, we introduce the generative flow network with invertible 1 × 1 convolutions (GLOW) into fault diagnosis. The GLOW model is optimized by maximum likelihood estimation and does not require multiple iterations to generate samples, avoiding the problems faced by GAN and DDPM. In order to generate balanced data explicitly, we propose a condition GLOW (CGLOW) to provide class-balanced samples in real time throughout the framework. On the other hand, using the reversibility of CGLOW, we design an end-to-end fault diagnosis framework that is globally optimized to mitigate the decline in diagnostic accuracy caused by the separation of generation and diagnosis and simplify the steps of fault diagnosis. In addition, to accommodate the non-stationary characteristics of fault signals, we propose a new data transformation method to improve the feature mining ability of the model and the diagnostic accuracy. Finally, we conduct extensive experiments to validate the superiority of the proposed approach. The experimental results demonstrate that our method outperforms existing ones.

Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse Problems.

Diffusion models have emerged as powerful generative techniques for solving inverse problems. Despite their success in a variety of inverse problems in imaging, these models require many steps to converge, leading to slow inference time. Recently, there has been a trend in diffusion models for employing sophisticated noise schedules that involve more frequent iterations of timesteps at lower noise levels, thereby improving image generation and convergence speed. However, application of these ideas for solving inverse problems with diffusion models remain challenging, as these noise schedules do not perform well when using empirical tuning for the forward model log-likelihood term weights. To tackle these challenges, we propose zero-shot approximate posterior sampling (ZAPS) that leverages connections to zero-shot physics-driven deep learning. ZAPS fixes the number of sampling steps, and uses zero-shot training with a physics-guided loss function to learn log-likelihood weights at each irregular timestep. We apply ZAPS to the recently proposed diffusion posterior sampling method as baseline, though ZAPS can also be used with other posterior sampling diffusion models. We further approximate the Hessian of the logarithm of the prior using a diagonalization approach with learnable diagonal entries for computational efficiency. These parameters are optimized over a fixed number of epochs with a given computational budget. Our results for various noisy inverse problems, including Gaussian and motion deblurring, inpainting, and super-resolution show that ZAPS reduces inference time, provides robustness to irregular noise schedules and improves reconstruction quality. Code is available at https://github.com/ualcalar17/ZAPS.