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Development of a Novel Real-Time Weighted Surrogate Model for Wind Turbine-Based on DFIG: Enhancing computational Efficiency in Power Systems With Error Bound

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IEEE ACCESS 2025年 13卷 50000-50016页

作者： Latif, Muhammad Ambreen, Hira Diyin, Tang Hassan, Farrukh Imran, Muhammad Abbasi, Saddam Hussain Imran, Muhammad Natl Univ Sci & Technol Coll Signals Dept Elect Engn Islamabad 44000 Pakistan Beihang Univ Aeronaut & Astronaut Sch Automat Sci & Elect Engn Dept Detect & Automat Engn Beijing 100191 Peoples R China Sunway Univ Fac Engn & Technol Dept Data Sci & Artificial Intelligence Darul Ehsan 47500 Selangor Malaysia Henan Univ Sch Comp & Informat Engn Kaifeng 475004 Henan Peoples R China

In the realm of modern wind turbine engineering, where precision is paramount for stability, control, and observability, this article introduces a groundbreaking method leveraging time-weighted Gramians. The focal point of this work is the reduction of model order in wind turbines featuring a double-fed induction generator with time-varying rotational speeds. Employing sophisticated state-space representations, we establish a comprehensive and systematic framework for analyzing wind turbine performance, ensuring adherence to stringent grid standards. A distinctive aspect of our proposed approach lies in the utilization of time-weighted Gramians and an innovative balanced realization technique to reduce the dimensionality of large state-space models effectively. Stability and reduced approximation errors are guaranteed by creating a lower-order system. A significant improvement is the availability of an a priori formula for error-bound, which allows for more efficient and faster computations. The use of time-weighted Gramians allowed for the application of this groundbreaking technique to time-sensitive systems in the real world, such as wind turbines. The optimization of models utilizing vast simulation data is what makes our methodology better than current methods. Wind turbines that allow for real-time adjustment of rotating speeds are part of this dataset. A lot of consideration is given to stability and error calculation in this study, which makes it innovative. The use of time-weighted Gramians in practical, real-time systems has greatly improved the accuracy and efficiency of modeling approaches.

关键词： Wind turbines Stators Mathematical models Doubly fed induction generators Rotors Power system stability Read only memory Reduced order systems computational modeling computational efficiency Time-weighted Gramians balance algorithm model reduction error-bound induction generator wind turbine

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Mesh-Selecting for computational Efficient PA Behavioral modeling and DPD Linearization

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IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS 2021年第1期31卷 37-40页

作者： Wang, Teng Gilabert, Pere L. Univ Politecn Catalunya UPC Barcelona Tech Dept Signal Theory & Commun Barcelona 08034 Spain

This letter proposes a mesh-selecting (MeS) method for complex-valued signals oriented at significantly reducing the training data required to extract the parameters of mathematical models for characterizing the nonlinear behavior of power amplifiers or digital predistortion linearizers. Experimental results will show the advantages of the proposed MeS method when properly combined with dimensionality reduction techniques. A reduction of the parameters' identification computational complexity by a factor of 65 can be achieved with respect to training with consecutive samples and employing the commonly used QR least-squares solution.

关键词： Mathematical model Histograms computational modeling computational complexity Memory management Radio frequency Predistortion computational complexity digital predistortion (DPD) power amplifier (PA)

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Optimizing Vision Transformers: Unveiling 'Focus and Forget' for Enhanced computational Efficiency

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IEEE ACCESS 2025年 13卷 27908-27927页

作者： Latibari, Banafsheh Saber Homayoun, Houman Sasan, Avesta Univ Calif Davis Elect & Comp Engn Davis CA 95616 USA

Vision Transformers are renowned for their accuracy in computer vision tasks but are computationally and memory expensive, making them challenging to deploy on resource-constrained edge devices. In our research paper, we introduce a revolutionary approach to designing energy-aware dynamically prunable Vision Transformers for use in edge applications. Our solution denoted as Incremental Resolution Enhancing Transformer (IRET), works by the sequential sampling of the input image. However, in our case, the embedding size of input tokens is considerably smaller than prior-art solutions. This embedding is used in the first few layers of the IRET vision transformer until a reliable attention matrix is formed. Then the attention matrix is used to sample additional information using a learnable 2D lifting scheme only for important tokens and IRET drops the tokens receiving low attention scores. Hence, as the model pays more attention to a subset of tokens for its task, its focus and resolution also increase. This incremental attention-guided sampling of input and dropping of unattended tokens allow IRET to significantly prune its computation tree on demand. By controlling the threshold for dropping unattended tokens and increasing the focus of attended ones, we can train a model that dynamically trades off complexity for accuracy. Moreover, using early exiting our model is capable of doing anytime prediction. This is especially useful for real-word energy-sensitive edge devices, where accuracy and complexity could be dynamically traded based on factors such as battery life, reliability, etc.

关键词： Transformers computational modeling Computer vision Head Accuracy Predictive models Visualization Discrete wavelet transforms computational efficiency Spatial resolution deep learning pruning vision transformer

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Sequential perception of tone and focus in parallel-A computational simulation

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SPEECH COMMUNICATION 2025年 168卷

作者： Chen, Yue Xu, Yi UCL Dept Speech Hearing & Phonet Sci London WC1N 1PF England

Speech is produced continuously over time. So, the information it conveys, including intonational functions, also unfolds over time. But many intonational functions are encoded across whole utterances rather than only within certain words. How can perception process speech signals continuously over time, even for communicative functions that are globally encoded? In this study we used computational simulation to test the idea that even for intonational functions with large temporal scopes, it is possible to process f0 contours syllable-by-syllable, and recognize the functions by continuous estimation of progressive probabilistic inference. We trained SVM and GRU models to simulate the perception of Mandarin tone and sentence focus with either syllable-sized or sentence-sized f0 contours as input. The sentence-wide f0 contours are gated at different syllable locations to test the incrementality of the recognition of tone and intonation. We also tested human listeners' perception of tone and focus with full and fragmented f0 contours from the same dataset to evaluate the validity of the simulated perception. The results showed that the simulated syllable-by-syllable processing of tone and focus generated the closest recognition patterns to human perception. The simulations also show that there is little difference whether tone and focus are recognized separately or as tone-focus combinations, which suggests that despite sharing the same acoustic dimension, the two functions are sufficiently separated from each other in their f0 coding.

关键词： Tone perception Focus perception computational modeling Gate recurrent unit (GRU) Sequential speech processing Co-current processing

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Vehicular-computational Resource Geofencing: Efficient Spatiotemporal Uncertainty Estimation

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IEEE ACCESS 2025年 13卷 43653-43665页

作者： Afifi, Ghada Mokhtar, Bassem UAE Univ Coll Informat Technol Comp & Network Engn Dept Al Ain U Arab Emirates

Future vehicles are envisioned as smart objects equipped with sensors, actuators, storage capacity and computing resources. Intelligent transportation systems (ITS) have the potential to enhance transportation efficiency, road safety, and sustainability. Vehicular cloud computing (VCC) has emerged to facilitate intervehicle communication and data exchange enabling ITS services. However, existing VCC solutions are unable to satisfy the stringent quality of experience (QoE) requirements for running vehicular applications, especially delay- and throughput-sensitive applications, given the high communication delay and latency with the central cloud. To encounter these challenges, vehicular edge computing (VEC) solutions have emerged which rely on infrastructure or roadside units (RSUs) to bring cloud computing services closer to the user. Our objective is to enable the utilization of computational resources embedded in moving service vehicles to further enhance the QoE of vehicular users requesting computational tasks in urban environments. This methodology has the potential to extend the computation coverage to areas with limited RSU infrastructure as well as reduce the computational latency as services are provided closer to the user. To enable this approach, the service vehicles need to develop a spatiotemporal awareness of other computational resources within range and predict their future availability. The objective of this research is to propose an efficient computational resource geofencing solution to maximize the QoE of vehicular users requiring computational tasks. We evaluate the efficiency of the proposed resource geofencing algorithm under different realistic operating conditions. According to our evaluation, the service vehicles estimate the spatiotemporal uncertainty and predict the availability of computational resources with up to 99% confidence.

关键词： Resource management Quality of experience Servers Spatiotemporal phenomena Optimization Cloud computing Uncertainty Processor scheduling Dedicated short range communication computational modeling Resource allocation resource geofencing spatiotemporal analysis scheduling task offloading uncertainty estimation vehicular edge of things computing

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computational Exploration and Experimental Verification for Designing SF6 Alternatives

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2025年第2期32卷 667-673页

作者： Shimakawa, Hajime Umemoto, Takahiro Kumada, Akiko Sato, Masahiro Univ Tokyo Dept Elect Engn & Informat Syst Bunkyo Ku Tokyo 1138654 Japan Asahi Kasei Corp Informat Initiat Minato Ku Tokyo 1080023 Japan

There have been numerous experimental efforts in developing SF6 alternatives. However, promising candidates have not been identified due to the tradeoff relationship among multiple requirements necessary for eco-friendly insulating gases. This study presents a computational molecular exploration and experimental verification for identifying potential SF6 alternatives. We propose machine learning models based on quantum mechanical insights to realize extrapolative prediction of gas properties. The evaluation results demonstrate the superior reliability of the proposed models compared with the existing ones, while also achieving high-throughput screening. The molecular exploration is systematically conducted to narrow down candidates that pose low global warming potential (GWP) and superior insulation strength under low-temperature and high-pressure conditions. The screening results reveal new candidates with enhanced insulation performance and favorable environmental impact compared with the existing candidates, including C4F7N, C5F10O, and CF3I. Furthermore, we newly measure the breakdown strength of a gas material for which no experimental data are available, and whose structure is extrapolative relative to the training data. The experimental result matches the prediction well, serving as an example that indicates the extrapolative capability of the proposed model. Our methodology and exploration results contribute to a wide range of material design as well as SF6 alternatives.

关键词： Climate change Machine learning Global warming Dielectric breakdown Quantum chemistry Predictive models Insulation testing computational modeling Training data Gas insulation Alternative gas dielectric breakdown strength global warming potential (GWP) machine learning molecular exploration quantum chemical calculation SF6

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computational Electromagnetics Code Validation and the Electromagnetic Code Consortium: Case studies, challenges, lessons learned, and recommendations

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IEEE ANTENNAS AND PROPAGATION MAGAZINE 2025年第2期67卷 10-19页

作者： Dault, Daniel L. Griffiths, Lance US Air Force Res Lab Sensors Directorate Wright Patterson AFB OH 45433 USA

We examine validation of computational electromagnetic (CEM) codes through the lens of the Electromagnetic Code Consortium (EMCC), a group of U.S. government and associated CEM practitioners that was active from the 1980s through the 2010s. Available EMCC validation cases are summarized, and several practical validation challenges, lessons learned, and recommendations are provided. Finally, one of the original EMCC cases, the metallic "business card," is revisited to illustrate some challenges of matching modeled and measured results and their possible resolution. An integrated team-based approach to constructing validation cases and diagnosing discrepancies between modeled and measured data is recommended.

关键词： Antenna measurements Codes computational modeling Government policies computational electromagnetics Antennas and propagation Data models Complexity theory Electromagnetics Lenses

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NeoCoMM: Neocortical neuro-inspired computational model for realistic microscale simulations

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SOFTWAREX 2025年 30卷

作者： Al Harrach, Mariam Yochum, Maxime Wendling, Fabrice Univ Rennes Inserm U1099 F-35000 Rennes France

The ability to simulate a neocortical neural network activity at the cellular level is of great interest in many studies. It allows for the investigation of microscopic mechanisms in both healthy and pathological brains. Microscale models of cortical volumes already exist however they are either too complex to use or too phenomenological to portray accurate results. NeoCoMM(The Neocortical computational Microscale model) is an innovative and realistic microscale software application. It offers a friendly graphical user interface that allows for the simulation of the intracellular (single cell) and extracellular (local field potential) neural activity of a cortical column. This software provides a realistic framework that can portray the neural activity and underlying cellular mechanisms related to different brain pathologies such as epilepsy. NeoCoMM is capable of (1) simulating the cortical tissue of three different species, (2) visualizing individual cell responses to external stimulation, (3) visualizing the corresponding local field potential, (4) studying the impact of the recording electrode features on simulated signals, and (5) testing various physiological and pathological hypotheses. While NeoCoMM was primarily developed for simulating epileptiform activity, it can also generate healthy brain rhythms or be adapted to other brain disorders.

关键词： Cortical network computational modeling Microscale neural model Interictal events simulation Epilepsy Local field potential

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computational modeling of Lava Domes using Particle Dynamics to investigate the effect of conduit flow mechanics on flow patterns

Computational Modeling of Lava Domes using Particle Dynamics...

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作者： Husain, Taha Murtuza PennState University Libraries

学位级别：Doctor of Philosophy

Large (1 – 4 x 106 m3) to major (> 4 x 106 m3) dome collapses for andesitic lava domes such as Soufriere Hills Volcano, Montserrat are observed for elevated magma discharge rates (6 – 13 m3/s). The gas rich magma pulses lead to pressure build up in the lava dome that result in structural failure of the over steepened canyon-like walls which may lead to rockfall or pyroclastic flow. This indicates that dome collapse at Soufriere Hills Volcano, Montserrat is intimately related to magma extrusion rate. Variation in magma extrusion rate for open-system magma chambers is observed to follow alternating periods of high and low activity. Periodic behavior of magma exhibits a rich diversity in the nature of its eruptive history due to variation in magma chamber size, total crystal content, linear crystal growth rate and magma replenishment rate. Distinguished patterns of growth were observed at different magma flow rates ranging from endogenous to exogenous dome growth for magma with varying *** the key parameters that control the transition in flow pattern of the magma during its lava dome building eruption is the main focus of this work. This dissertation examines the mechanical effects on the morphology of the evolving lava dome on the extrusion of magma from a central vent using a 2D particle dynamics model. The particle dynamics model is coupled with a conduit flow model that incorporates the kinetics of crystallization and rheological stiffening to investigate important mechanisms during lava dome building *** I of this dissertation explores lava dome growth and failure mechanics using a two-dimensional particle-dynamics model. The model follows the evolution of fractured lava, with solidification driven by degassing induced crystallization of magma. The particle-dynamics model emulates the natural development of dome growth and rearrangement of the lava dome which is difficult in mesh-based analyses due to mesh entanglement effects. The

关键词： computational modeling lava domes

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An Adaptive LSTM-PR Hybrid Health Status Prognostics Strategy With Balance Between Accuracy and computational Burden

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Dong, Run Liu, Wenjie Cheng, Boyuan Li, Weilin Northwestern Polytech Univ Sch Automat Xian 710060 Peoples R China Minist Ind & Informat Technol Key Lab Aircraft Elect Prop Technol Xian 710129 Peoples R China

Aviation contactors are widely utilized in the aircraft electrical power distribution systems (AEPDS), which are the key equipment for the reliability and security of electrical power distribution. The accurate and rapid estimation and prognostics of the health status (HS) for aviation contactors can greatly improve the operational stability of AEPDS and ensure flight safety. Nevertheless, the limited computational resources on board restrict the application of complex methods, which have heavy computational burdens. To obtain the balance between the prognostic accuracy and computational burden of HS prognostics for aviation contactors, an adaptive LSTM-PR hybrid prognostics strategy is proposed. The proposed adaptive hybrid strategy combines the advantages of the high accuracy of the long short-term memory (LSTM) model and the light computational burden of the polynomial regression (PR) method, respectively. Moreover, a novel integrated assessment indicator (IAI) is constructed to comprehensively assess the prognostic performance. Based on the degradation dataset of aviation contactors, the proposed prognostics strategy is validated. The validation results demonstrate that the proposed strategy has a remarkable superiority in terms of integrated prognostic performance. Compared with typical baseline methods, which contain the convolutional neural networks (CNNs) and back propagation neural network (BPNN) methods, the proposed strategy enhances the integrated prognostic performance by 91.06% and 74.65%, relatively.

关键词： Accuracy Contactors Long short term memory Degradation computational modeling Aircraft Atmospheric modeling Integrated circuit modeling Feature extraction Estimation computational burden contactor health status (HS) long short-term memory (LSTM) polynomial regression (PR) prognostics and health management (PHM)

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