FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku prod...FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku product and groundbasedradars(GRs)during 2024.Also,the FY-3G PMR is used as a third-party reference to evaluate the reflectivityconsistency among different GRs.The FY-3G PMR and GRs share similarities in their general distribution,characteristics,and intensity of reflectivity in strong precipitation cloud systems,though the former presents less detailed system structure.Systematic deviations between the FY-3G PMR and GRs and between GRs are comparable,albeit the reflectivity of the FY-3G PMR is generally slightly stronger than that of GRs(especially X-band GRs),with a mean bias ranging from 0.7 to 1.7dB.S-band GRs exhibit the smallest systematic deviation(STD=3.09 dB)from the FY-3G PMR,whereas the X-band GRsshow the largest(STD=3.61 dB),indirectly indicating the highest internal consistency among S-band GRs and the lowestamong X-band GRs.Besides,both S-and C-band GRs display similar deviations when paired with the FY-3G PMR as wellas when paired with their adjacent S/C-band GRs,suggesting good consistency between these two bands.In contrast,XbandGRs exhibit relatively poor consistency with S-band GRs and the FY-3G PMR,showing a deviation ranging from 3.0to 4.6 dB.展开更多
Inertial response control(IRC)makes variable-speed wind turbine generators(WTGs)provide short-term frequency support during contingencies by releasing the kinetic energy stored in wind turbine rotors.When frequency su...Inertial response control(IRC)makes variable-speed wind turbine generators(WTGs)provide short-term frequency support during contingencies by releasing the kinetic energy stored in wind turbine rotors.When frequency support is terminated,the rotor speed should be restored to optimum for maximum power point tracking(MPPT).Existing IRCs utilize rotor speed recovery(RSR)strategies with a consistent power reference function.However,under real turbulent wind with alternate gusts and lulls,the consistent power reference function may fail to restore rotor speed or cause unexpected secondary frequency drop(SFD).In this regard,this paper proposes a novel adaptive RSR strategy that not only restores rotor speed via the aerodynamic power enhanced by wind gusts,but also stabilizes the turbine at wind lulls by tracking a suboptimal power curve.Experiments on a wind power-integrated power system testbed validate the proposed RSR strategy can successfully restore rotor speed while attenuating SFD under turbulent wind.展开更多
The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflect...The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflectivity observed by GPM Ku PR is compared quantitatively against GR networks from CINRAD of China and NEXRAD of the United States,and the volume matching method is used for spatial matching.Additionally,a novel frequency correction method for all phases as well as precipitation types is used to correct the GPM Ku PR radar frequency to the GR frequency.A total of 20 GRs(including 10 from CINRAD and 10 from NEXRAD)are included in this comparative analysis.The results indicate that,compared with CINRAD matched data,NEXRAD exhibits larger biases in reflectivity when compared with the frequency-corrected Ku PR.The root-mean-square difference for CINRAD is calculated at 2.38 d B,whereas for NEXRAD it is 3.23 d B.The mean bias of CINRAD matched data is-0.16 d B,while the mean bias of NEXRAD is-2.10 d B.The mean standard deviation of bias for CINRAD is 2.15 d B,while for NEXRAD it is 2.29 d B.This study effectively assesses weather radar data in both the United States and China,which is crucial for improving the overall consistency of global precipitation estimates.展开更多
We give a new result on the construction of K-frame generators for unitary systems by using the pseudo-inverses of involved operators,which provides an improvement to one known result on this topic.We also introduce t...We give a new result on the construction of K-frame generators for unitary systems by using the pseudo-inverses of involved operators,which provides an improvement to one known result on this topic.We also introduce the concept of K-woven generators for unitary systems,by means of which we investigate the weaving properties of K-frame generators for unitary systems.展开更多
Accurate cloud classification plays a crucial role in aviation safety,climate monitoring,and localized weather forecasting.Current research has been focusing on machine learning techniques,particularly deep learning b...Accurate cloud classification plays a crucial role in aviation safety,climate monitoring,and localized weather forecasting.Current research has been focusing on machine learning techniques,particularly deep learning based model,for the types identification.However,traditional approaches such as convolutional neural networks(CNNs)encounter difficulties in capturing global contextual information.In addition,they are computationally expensive,which restricts their usability in resource-limited environments.To tackle these issues,we present the Cloud Vision Transformer(CloudViT),a lightweight model that integrates CNNs with Transformers.The integration enables an effective balance between local and global feature extraction.To be specific,CloudViT comprises two innovative modules:Feature Extraction(E_Module)and Downsampling(D_Module).These modules are able to significantly reduce the number of model parameters and computational complexity while maintaining translation invariance and enhancing contextual comprehension.Overall,the CloudViT includes 0.93×10^(6)parameters,which decreases more than ten times compared to the SOTA(State-of-the-Art)model CloudNet.Comprehensive evaluations conducted on the HBMCD and SWIMCAT datasets showcase the outstanding performance of CloudViT.It achieves classification accuracies of 98.45%and 100%,respectively.Moreover,the efficiency and scalability of CloudViT make it an ideal candidate for deployment inmobile cloud observation systems,enabling real-time cloud image classification.The proposed hybrid architecture of CloudViT offers a promising approach for advancing ground-based cloud image classification.It holds significant potential for both optimizing performance and facilitating practical deployment scenarios.展开更多
This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational ...This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational efficiencies,and technological advancements.Linear generators,such as Induction,permanent magnet synchronous,and switched reluctance types,are highlighted for their direct conversion capability,eliminating the need for mechanical gearboxes.Rotary Induction generators,permanent magnet synchronous generators,and doubly-fed Induction generators are evaluated for their established engineering principles and integration with existing grid infrastructure.The paper discusses the historical development,environmental benefits,and ongoing advancements in wave energy technologies,emphasizing the increasing feasibility and scalability of wave energy as a renewable source.Through a comprehensive analysis,this review provides insights into the current state and future prospects of electrical generators in wave energy conversion,underscoring their potential to significantly reduce reliance on fossil fuels and mitigate environmental impacts.展开更多
Space target imaging simulation technology is an important tool for space target detection and identification,with advantages that include high flexibility and low cost.However,existing space target imaging simulation...Space target imaging simulation technology is an important tool for space target detection and identification,with advantages that include high flexibility and low cost.However,existing space target imaging simulation technologies are mostly based on target magnitudes for simulations,making it difficult to meet image simulation requirements for different signal-to-noise ratio(SNR)needs.Therefore,design of a simulation method that generates target image sequences with various SNRs based on the optical detection system parameters will be important for faint space target detection research.Addressing the SNR calculation issue in optical observation systems,this paper proposes a ground-based detection image SNR calculation method using the optical system parameters.This method calculates the SNR of an observed image precisely using radiative transfer theory,the optical system parameters,and the observation environment parameters.An SNR-based target sequence image simulation method for ground-based detection scenarios is proposed.This method calculates the imaging SNR using the optical system parameters and establishes a model for conversion between the target’s apparent magnitude and image grayscale values,thereby enabling generation of target sequence simulation images with corresponding SNRs for different system parameters.Experiments show that the SNR obtained using this calculation method has an average calculation error of<1 dB when compared with the theoretical SNR of the actual optical system.Additionally,the simulation images generated by the imaging simulation method show high consistency with real images,which meets the requirements of faint space target detection algorithm research and provides reliable data support for development of related technologies.展开更多
This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow con...This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow control technique utilizing a tubercle and vortex generator(VG)close to the leading edge was analyzed numerically for a NACA0015 airfoil.In this study,the Shear Stress Transport(SST)turbulence model was employed in the numerical modelling.Numerical modelling was completed using the ANSYS-Fluent 18.2 solver.Analyses were conducted to investigate the flow pattern and understand the underlying LSB control phenomena that enabled the new passive flow control method to provide this significant performance benefit.The findings indicated that the new concept of passive flow control technique suppressed the formation of an LSB at the suction surface of the NACA0015 airfoil,resulting in a higher lift coefficient and improved aerodynamic performance.Improvements in LSB dynamics and aerodynamic performance through the passive flow control method lead to increased energy output and enhanced stability.展开更多
An islanded microgrid exhibits poor transient power sharing between synchronous generators(SGs)and inverterinterfaced distributed generators(IIDGs).This large error of transient power-sharing may result in the overloa...An islanded microgrid exhibits poor transient power sharing between synchronous generators(SGs)and inverterinterfaced distributed generators(IIDGs).This large error of transient power-sharing may result in the overload of generators and a large deviation in frequency.In this paper,the mechanism that leads to poor transient power sharing is revealed.Then,a parameter design and a coordinated control strategy are proposed to improve transient power sharing.A coordinated enhanced power-sharing(EPS)control strategy is proposed for IIDGs,which prevents the overload of IIDGs in grid-forming mode and is compatible with the existing power sharing strategies.By using a hierarchical control structure,accurate transient power sharing is achieved without the knowledge of connecting impedance.The analysis results and the proposed control method are validated by simulation.展开更多
Harvesting energy from humid air to generate electricity represents a promising strategy for sustainable power generation.However,achieving high output and long-term stability in moisture-driven power generators(MPGs)...Harvesting energy from humid air to generate electricity represents a promising strategy for sustainable power generation.However,achieving high output and long-term stability in moisture-driven power generators(MPGs)remains a significant challenge.Here,we develop an efficient MPG by incorporating polymerized ionic liquid(PIL)and MXene through in-situ polymerization of cationic long chains within the MXene layers.This structural design enhances the hydrophilicity and ion dynamics,ensuring stable and sustained electrical output.A single MPG device delivers an open-circuit voltage of 0.65 V and a power density of 14.87 μW·cm^(-2),operating continuously for over 36 h.Surface characterization and quantum chemistry calculations elucidate that the mobile anions within the MPG move directionally under moisture gradients,while polymerized cations remain stationary,driving power generation.The MPG exhibits exceptional long-term stability,retaining about 80%of its initial voltage output after 30 days.Moreover,these MPGs demonstrate scalability for practical applications,capable of efficiently charging capacitors and powering LEDs through simple series-parallel configurations.This work offers a promising strategy to simultaneously enhance the performance and operational stability of MPGs,offering a sustainable solution for the direct conversion of low-grade thermal energy from moisture into clean electricity.展开更多
The petal-shaped distribution network has high power supply reliability.However,the closed-loop operation mode and the access of inverter-interfaced distributed generators(IIDGs)bring great challenges to the protectio...The petal-shaped distribution network has high power supply reliability.However,the closed-loop operation mode and the access of inverter-interfaced distributed generators(IIDGs)bring great challenges to the protection schemes.The current amplitude differential protection is an effective means to solve this problem,but the existing criterions rarely consider both sensitivity to high-resistance faults and low requirements for data synchronization.Therefore,the general variation laws of the amplitude difference between the current steady-state components at both terminals and the phase differences between current fault components at both terminals are revealed.For external faults,the steady-state-component current amplitude difference is around zero and the fault-component current phase difference is around 180◦.For internal faults,either the amplitude difference is large or the phase difference is small.Accordingly,a current differential protection scheme based on the pre-fault and postfault steady-state current is proposed.The amplitude and phase of current at both terminals of the protected line are required in the proposed scheme,which has low requirements for data synchronization.The simulation results show that the proposed protection scheme is not affected by the fault type,position,resistance and capacity of the IIDGs.It can also be applied to radial distribution networks with IIDGs.展开更多
The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adver...The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.展开更多
Photovoltaic power generating is one of the primary methods of utilizing solar energy resources,with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy....Photovoltaic power generating is one of the primary methods of utilizing solar energy resources,with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy.In order to provide reference strategies for pertinent researchers as well as potential implementation,this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches,statistical approaches and optimization techniques for solar power generation and forecasting.Deep learning-related methods,in particular,can theoretically handle arbitrary nonlinear transformations through proper model structural design,such as hidden layer topology optimization and objective function analysis to save information that can increase forecasting accuracy while filtering out irrelevant or less affected data for forecasting.The research’s results indicate that RBFNN-AG performed the best when applying the predetermined number of days,with an NRMSE value of 4.65%.RBFNN-AG performs better than sophisticated models like DenseNet(5.69%),SLFN-ELM(5.95%),and ANN-k-means-linear regression correction(6.11%).Additionally,scenario application and PV system investment techniques are provided to evaluate the current condition of new energy development and market trends both domestically and internationally.展开更多
To address the excessive complexity of monthly scheduling and the impact of uncertain net load on the chargeable energy of storage,a reduced time-period monthly scheduling model for thermal generators and energy stora...To address the excessive complexity of monthly scheduling and the impact of uncertain net load on the chargeable energy of storage,a reduced time-period monthly scheduling model for thermal generators and energy storage,incorporating daily minimum chargeable energy constraints,was developed.Firstly,considering the variations in the frequency of unit start-ups and shutdowns under different levels of net load fluctuation,a method was proposed to reduce decision time periods for unit start-up and shut-down operations.This approach,based on the characteristics of net load fluctuations,minimizes the decision variables of units,thereby simplifying the monthly schedulingmodel.Secondly,the relationship between energy storage charging and discharging power,net load,and the total maximum/minimum output of units was analyzed.Based on this,daily minimum chargeable energy constraints were established to ensure the energy storage system meets charging requirements under extreme net load scenarios.Finally,taking into account the operational costs of thermal generators and energy storage,load loss costs,and operational constraints,the reduced time-period monthly schedulingmodel was constructed.Case studies demonstrate that the proposedmethod effectively generates economical monthly operation plans for thermal generators and energy storage,significantly reduces model solution time,and satisfies the charging requirements of energy storage under extreme net load conditions.展开更多
To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capabl...To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.展开更多
Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distr...Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distribution of training data,often neglecting the sequential continuity between moves in the game.This oversight can result in unnatural patterns that deviate from real user behavior,thereby reducing the security of the hidden communication.To address this issue,we design a Gomoku agent based on the AlphaZero algorithm.The model engages in self-play to generate a sequence of plausible moves.These moves formthe basis of the stego images.We then apply an attractionmatrix at each step.It guides themove selection so that themoves appearmore natural.Thismethod helps maintain logical flow between moves.It also extends the game length,which increases the embedding capacity.Next,we filter and prioritize the generated moves.The selected moves are embedded into a move pool.Secret message is mapped to thesemoves.It is then embedded step by step as the game progresses.The finalmove sequence constitutes a complete steganographic game record.The receiver can extract the secret message using this record and a predefined mapping rule.Experiments show that our method reaches a maximum embedding capacity of 223 bits per carrier.Detection accuracy is 0.500 under XuNet and 0.498 under YeNet.These results are equal to random guessing,showing strong imperceptibility.The proposed method demonstrates superior concealment,higher embedding capacity,and greater robustness against common image distortions and steganalysis attacks.展开更多
Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation...Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.展开更多
With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE ...With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.展开更多
Synthesizing images or texts automatically becomes a useful research area in the artificial intelligence nowadays.Generative adversarial networks(GANs),proposed by Goodfellow,et al.in 2014,make this task to be done mo...Synthesizing images or texts automatically becomes a useful research area in the artificial intelligence nowadays.Generative adversarial networks(GANs),proposed by Goodfellow,et al.in 2014,make this task to be done more efficiently by using deep neural networks(DNNs).The authors consider generating corresponding images from a single-sentence input text description using a GAN.Specifically,the authors analyze the GAN-CLS algorithm,which is a kind of advanced method of GAN proposed by Reed,et al.in 2016.In this paper the authors show the theoretical problem with this algorithm and correct it by modifying the objective function of the model.Experiments are performed on the Oxford-102 dataset and the CUB dataset to support the theoretical results.Since the proposed modification can be seen as an idea which can be used to improve all such kind of GAN models,the authors try two models,GAN-CLS and AttnGAN_(GPT).As a result,in both of the two models,the proposed modified algorithm is more stable and can generate images which are more plausible than the original algorithm.Also,some of the generated images match the input texts better,and the proposed modified algorithm has better performance on the quantitative indicators including FID and Inception Score.Finally,the authors propose some future application prospect of the modification idea,especially in the area of large language models.展开更多
Modern/distributed electric energy systems,with ever larger penetration of renewable(photovoltaic,wind,wave,and hydro)energy sources and time-variable outputs,are in need of stronger/higher frequency and alternating c...Modern/distributed electric energy systems,with ever larger penetration of renewable(photovoltaic,wind,wave,and hydro)energy sources and time-variable outputs,are in need of stronger/higher frequency and alternating current(AC)(direct current(DC))voltage control.In fact,faster and more stable active and reactive power in the presence of frequency and voltage sags and swells is needed.Power electronics-controlled variable speed generators do not have enough energy storage(inertia)for the scope(static synchronous compensators(STATCOMs)included).This is because power electronics tends to decouple the generator from the power system.While virtual inertia control in doubly fed induction generators(DFIGs)offers a partial solution to these problems,a more robust and comprehensive framework is required for advanced grid support.This is how,by extending the dual-excitation principles,the dualaxis excited electric synchronous generators(DE-SG)provide superior flexibility in two variants summarized here:as a multifunctional DFIG and dual-axis vs.single-axis excited synchronous generator(SG),and as a synchronous condenser(SC),with dual DC and AC excitation(as a no-load DFIG with inertia wheel),where variable speed is used to accelerate/decelerate the SC and thus provide additional assistance in frequency stabilization.These solutions,good for short-time transients,are not meant,however,to replace the large bidirectional energy storage systems(pump-hydro,hydrogen,batteries,etc.)which are crucial for the daily inherent variations of output energy in modern power systems with multiple power sources.The present paper offers a summary of techniques used in the dual-axis excited vs.single-axis excited SGs(SE-SGs),and SCs topologies,modeling,and control for better stability in modern multiple-source energy systems.This survey includes multiple case studies to shed light on prominent methods.展开更多
基金supported by the Innovation and Development Special Project of the China Meteorological Administration(Grant No.CXFZ2024J058)the Guangdong Province Basic and Applied Basic Research Foundation Meteorological Joint Fund Project(Grant No.2024A1515510036)+1 种基金the National Key R&D Program of China(Grant No.2022YFC3004101)the Technical Innovation Team Project of Guangzhou Meteorological Satellite Ground Station(Grant No.CXTD202401).
文摘FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku product and groundbasedradars(GRs)during 2024.Also,the FY-3G PMR is used as a third-party reference to evaluate the reflectivityconsistency among different GRs.The FY-3G PMR and GRs share similarities in their general distribution,characteristics,and intensity of reflectivity in strong precipitation cloud systems,though the former presents less detailed system structure.Systematic deviations between the FY-3G PMR and GRs and between GRs are comparable,albeit the reflectivity of the FY-3G PMR is generally slightly stronger than that of GRs(especially X-band GRs),with a mean bias ranging from 0.7 to 1.7dB.S-band GRs exhibit the smallest systematic deviation(STD=3.09 dB)from the FY-3G PMR,whereas the X-band GRsshow the largest(STD=3.61 dB),indirectly indicating the highest internal consistency among S-band GRs and the lowestamong X-band GRs.Besides,both S-and C-band GRs display similar deviations when paired with the FY-3G PMR as wellas when paired with their adjacent S/C-band GRs,suggesting good consistency between these two bands.In contrast,XbandGRs exhibit relatively poor consistency with S-band GRs and the FY-3G PMR,showing a deviation ranging from 3.0to 4.6 dB.
基金supported by National Natural Science Foundation of China(51977111)the Six Talent Peaks High-level Talent Project in Jiangsu Province(XNY-025)the Special Fund of Jiangsu Province for Transformation of Scientific and Technological Achievements(BA2019045).
文摘Inertial response control(IRC)makes variable-speed wind turbine generators(WTGs)provide short-term frequency support during contingencies by releasing the kinetic energy stored in wind turbine rotors.When frequency support is terminated,the rotor speed should be restored to optimum for maximum power point tracking(MPPT).Existing IRCs utilize rotor speed recovery(RSR)strategies with a consistent power reference function.However,under real turbulent wind with alternate gusts and lulls,the consistent power reference function may fail to restore rotor speed or cause unexpected secondary frequency drop(SFD).In this regard,this paper proposes a novel adaptive RSR strategy that not only restores rotor speed via the aerodynamic power enhanced by wind gusts,but also stabilizes the turbine at wind lulls by tracking a suboptimal power curve.Experiments on a wind power-integrated power system testbed validate the proposed RSR strategy can successfully restore rotor speed while attenuating SFD under turbulent wind.
基金funded by the National Key Research and Development Program of China(Grant No.2023YFB3907500)the National Natural Science Foundation(Grant No.42330602)the“Fengyun Satellite Remote Sensing Product Validation and Verification”Youth Innovation Team of the China Meteorological Administration(Grant No.CMA2023QN12)。
文摘The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflectivity observed by GPM Ku PR is compared quantitatively against GR networks from CINRAD of China and NEXRAD of the United States,and the volume matching method is used for spatial matching.Additionally,a novel frequency correction method for all phases as well as precipitation types is used to correct the GPM Ku PR radar frequency to the GR frequency.A total of 20 GRs(including 10 from CINRAD and 10 from NEXRAD)are included in this comparative analysis.The results indicate that,compared with CINRAD matched data,NEXRAD exhibits larger biases in reflectivity when compared with the frequency-corrected Ku PR.The root-mean-square difference for CINRAD is calculated at 2.38 d B,whereas for NEXRAD it is 3.23 d B.The mean bias of CINRAD matched data is-0.16 d B,while the mean bias of NEXRAD is-2.10 d B.The mean standard deviation of bias for CINRAD is 2.15 d B,while for NEXRAD it is 2.29 d B.This study effectively assesses weather radar data in both the United States and China,which is crucial for improving the overall consistency of global precipitation estimates.
基金Supported by NSFC(Nos.12361028,11761057)Science Foundation of Jiangxi Education Department(Nos.GJJ202302,GJJ202303,GJJ202319).
文摘We give a new result on the construction of K-frame generators for unitary systems by using the pseudo-inverses of involved operators,which provides an improvement to one known result on this topic.We also introduce the concept of K-woven generators for unitary systems,by means of which we investigate the weaving properties of K-frame generators for unitary systems.
基金funded by Innovation and Development Special Project of China Meteorological Administration(CXFZ2022J038,CXFZ2024J035)Sichuan Science and Technology Program(No.2023YFQ0072)+1 种基金Key Laboratory of Smart Earth(No.KF2023YB03-07)Automatic Software Generation and Intelligent Service Key Laboratory of Sichuan Province(CUIT-SAG202210).
文摘Accurate cloud classification plays a crucial role in aviation safety,climate monitoring,and localized weather forecasting.Current research has been focusing on machine learning techniques,particularly deep learning based model,for the types identification.However,traditional approaches such as convolutional neural networks(CNNs)encounter difficulties in capturing global contextual information.In addition,they are computationally expensive,which restricts their usability in resource-limited environments.To tackle these issues,we present the Cloud Vision Transformer(CloudViT),a lightweight model that integrates CNNs with Transformers.The integration enables an effective balance between local and global feature extraction.To be specific,CloudViT comprises two innovative modules:Feature Extraction(E_Module)and Downsampling(D_Module).These modules are able to significantly reduce the number of model parameters and computational complexity while maintaining translation invariance and enhancing contextual comprehension.Overall,the CloudViT includes 0.93×10^(6)parameters,which decreases more than ten times compared to the SOTA(State-of-the-Art)model CloudNet.Comprehensive evaluations conducted on the HBMCD and SWIMCAT datasets showcase the outstanding performance of CloudViT.It achieves classification accuracies of 98.45%and 100%,respectively.Moreover,the efficiency and scalability of CloudViT make it an ideal candidate for deployment inmobile cloud observation systems,enabling real-time cloud image classification.The proposed hybrid architecture of CloudViT offers a promising approach for advancing ground-based cloud image classification.It holds significant potential for both optimizing performance and facilitating practical deployment scenarios.
文摘This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational efficiencies,and technological advancements.Linear generators,such as Induction,permanent magnet synchronous,and switched reluctance types,are highlighted for their direct conversion capability,eliminating the need for mechanical gearboxes.Rotary Induction generators,permanent magnet synchronous generators,and doubly-fed Induction generators are evaluated for their established engineering principles and integration with existing grid infrastructure.The paper discusses the historical development,environmental benefits,and ongoing advancements in wave energy technologies,emphasizing the increasing feasibility and scalability of wave energy as a renewable source.Through a comprehensive analysis,this review provides insights into the current state and future prospects of electrical generators in wave energy conversion,underscoring their potential to significantly reduce reliance on fossil fuels and mitigate environmental impacts.
基金supported by Open Fund of National Key Laboratory of Deep Space Exploration(NKDSEL2024014)by Civil Aerospace Pre-research Project of State Administration of Science,Technology and Industry for National Defence,PRC(D040103).
文摘Space target imaging simulation technology is an important tool for space target detection and identification,with advantages that include high flexibility and low cost.However,existing space target imaging simulation technologies are mostly based on target magnitudes for simulations,making it difficult to meet image simulation requirements for different signal-to-noise ratio(SNR)needs.Therefore,design of a simulation method that generates target image sequences with various SNRs based on the optical detection system parameters will be important for faint space target detection research.Addressing the SNR calculation issue in optical observation systems,this paper proposes a ground-based detection image SNR calculation method using the optical system parameters.This method calculates the SNR of an observed image precisely using radiative transfer theory,the optical system parameters,and the observation environment parameters.An SNR-based target sequence image simulation method for ground-based detection scenarios is proposed.This method calculates the imaging SNR using the optical system parameters and establishes a model for conversion between the target’s apparent magnitude and image grayscale values,thereby enabling generation of target sequence simulation images with corresponding SNRs for different system parameters.Experiments show that the SNR obtained using this calculation method has an average calculation error of<1 dB when compared with the theoretical SNR of the actual optical system.Additionally,the simulation images generated by the imaging simulation method show high consistency with real images,which meets the requirements of faint space target detection algorithm research and provides reliable data support for development of related technologies.
基金the Scientific Research Projects Unit of Erciyes University under contract no:FDS-2022-11532 and FOA-2025-14773.
文摘This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow control technique utilizing a tubercle and vortex generator(VG)close to the leading edge was analyzed numerically for a NACA0015 airfoil.In this study,the Shear Stress Transport(SST)turbulence model was employed in the numerical modelling.Numerical modelling was completed using the ANSYS-Fluent 18.2 solver.Analyses were conducted to investigate the flow pattern and understand the underlying LSB control phenomena that enabled the new passive flow control method to provide this significant performance benefit.The findings indicated that the new concept of passive flow control technique suppressed the formation of an LSB at the suction surface of the NACA0015 airfoil,resulting in a higher lift coefficient and improved aerodynamic performance.Improvements in LSB dynamics and aerodynamic performance through the passive flow control method lead to increased energy output and enhanced stability.
基金supported in part by National Natural Science Foundation of China under Grant 52125705in part by National Natural Science Foundation of China under Grant 52107194.
文摘An islanded microgrid exhibits poor transient power sharing between synchronous generators(SGs)and inverterinterfaced distributed generators(IIDGs).This large error of transient power-sharing may result in the overload of generators and a large deviation in frequency.In this paper,the mechanism that leads to poor transient power sharing is revealed.Then,a parameter design and a coordinated control strategy are proposed to improve transient power sharing.A coordinated enhanced power-sharing(EPS)control strategy is proposed for IIDGs,which prevents the overload of IIDGs in grid-forming mode and is compatible with the existing power sharing strategies.By using a hierarchical control structure,accurate transient power sharing is achieved without the knowledge of connecting impedance.The analysis results and the proposed control method are validated by simulation.
基金the National Natural Science Foundation of China(22278401 and 92163209)the ANSO Collaborative Research Program(ANSO-CR-KP-2022-12)+2 种基金Beijing Natural Science Foundation(2252011 and JQ22004)Beijing Nova Program(20230484478)for financial supportsupported by Public Computing Cloud,Renmin University of China.
文摘Harvesting energy from humid air to generate electricity represents a promising strategy for sustainable power generation.However,achieving high output and long-term stability in moisture-driven power generators(MPGs)remains a significant challenge.Here,we develop an efficient MPG by incorporating polymerized ionic liquid(PIL)and MXene through in-situ polymerization of cationic long chains within the MXene layers.This structural design enhances the hydrophilicity and ion dynamics,ensuring stable and sustained electrical output.A single MPG device delivers an open-circuit voltage of 0.65 V and a power density of 14.87 μW·cm^(-2),operating continuously for over 36 h.Surface characterization and quantum chemistry calculations elucidate that the mobile anions within the MPG move directionally under moisture gradients,while polymerized cations remain stationary,driving power generation.The MPG exhibits exceptional long-term stability,retaining about 80%of its initial voltage output after 30 days.Moreover,these MPGs demonstrate scalability for practical applications,capable of efficiently charging capacitors and powering LEDs through simple series-parallel configurations.This work offers a promising strategy to simultaneously enhance the performance and operational stability of MPGs,offering a sustainable solution for the direct conversion of low-grade thermal energy from moisture into clean electricity.
基金supported in part by Science and Technology Project of State Grid Corporation of China:Research on Key Protection Technologies for New-type Urban Distribution Network with Controllable Sources and Loads.
文摘The petal-shaped distribution network has high power supply reliability.However,the closed-loop operation mode and the access of inverter-interfaced distributed generators(IIDGs)bring great challenges to the protection schemes.The current amplitude differential protection is an effective means to solve this problem,but the existing criterions rarely consider both sensitivity to high-resistance faults and low requirements for data synchronization.Therefore,the general variation laws of the amplitude difference between the current steady-state components at both terminals and the phase differences between current fault components at both terminals are revealed.For external faults,the steady-state-component current amplitude difference is around zero and the fault-component current phase difference is around 180◦.For internal faults,either the amplitude difference is large or the phase difference is small.Accordingly,a current differential protection scheme based on the pre-fault and postfault steady-state current is proposed.The amplitude and phase of current at both terminals of the protected line are required in the proposed scheme,which has low requirements for data synchronization.The simulation results show that the proposed protection scheme is not affected by the fault type,position,resistance and capacity of the IIDGs.It can also be applied to radial distribution networks with IIDGs.
文摘The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.
基金supported by the National Natural Science Foundation of China(NSFC)(Nos.61902158,61806087).
文摘Photovoltaic power generating is one of the primary methods of utilizing solar energy resources,with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy.In order to provide reference strategies for pertinent researchers as well as potential implementation,this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches,statistical approaches and optimization techniques for solar power generation and forecasting.Deep learning-related methods,in particular,can theoretically handle arbitrary nonlinear transformations through proper model structural design,such as hidden layer topology optimization and objective function analysis to save information that can increase forecasting accuracy while filtering out irrelevant or less affected data for forecasting.The research’s results indicate that RBFNN-AG performed the best when applying the predetermined number of days,with an NRMSE value of 4.65%.RBFNN-AG performs better than sophisticated models like DenseNet(5.69%),SLFN-ELM(5.95%),and ANN-k-means-linear regression correction(6.11%).Additionally,scenario application and PV system investment techniques are provided to evaluate the current condition of new energy development and market trends both domestically and internationally.
基金This study was supported by State Grid Corporation headquarters technology project(4000-202399368A-2-2-ZB).
文摘To address the excessive complexity of monthly scheduling and the impact of uncertain net load on the chargeable energy of storage,a reduced time-period monthly scheduling model for thermal generators and energy storage,incorporating daily minimum chargeable energy constraints,was developed.Firstly,considering the variations in the frequency of unit start-ups and shutdowns under different levels of net load fluctuation,a method was proposed to reduce decision time periods for unit start-up and shut-down operations.This approach,based on the characteristics of net load fluctuations,minimizes the decision variables of units,thereby simplifying the monthly schedulingmodel.Secondly,the relationship between energy storage charging and discharging power,net load,and the total maximum/minimum output of units was analyzed.Based on this,daily minimum chargeable energy constraints were established to ensure the energy storage system meets charging requirements under extreme net load scenarios.Finally,taking into account the operational costs of thermal generators and energy storage,load loss costs,and operational constraints,the reduced time-period monthly schedulingmodel was constructed.Case studies demonstrate that the proposedmethod effectively generates economical monthly operation plans for thermal generators and energy storage,significantly reduces model solution time,and satisfies the charging requirements of energy storage under extreme net load conditions.
基金funded by the Science and Technology Projects of State Grid Corporation of China(Project No.J2024136).
文摘To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.
基金funded by theWuxi“Taihu Light”Science and Technology Key Project(Basic Research)(K20241046)the National Natural Science Foundation of China(Grant Nos.62102189,62122032,42305158)+1 种基金the Open Project of the National Engineering Research Center for Sensor Networks(2024YJZXKFKT02)Wuxi University Research Start-up Fund for High-Level Talents(No.2022r043).
文摘Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distribution of training data,often neglecting the sequential continuity between moves in the game.This oversight can result in unnatural patterns that deviate from real user behavior,thereby reducing the security of the hidden communication.To address this issue,we design a Gomoku agent based on the AlphaZero algorithm.The model engages in self-play to generate a sequence of plausible moves.These moves formthe basis of the stego images.We then apply an attractionmatrix at each step.It guides themove selection so that themoves appearmore natural.Thismethod helps maintain logical flow between moves.It also extends the game length,which increases the embedding capacity.Next,we filter and prioritize the generated moves.The selected moves are embedded into a move pool.Secret message is mapped to thesemoves.It is then embedded step by step as the game progresses.The finalmove sequence constitutes a complete steganographic game record.The receiver can extract the secret message using this record and a predefined mapping rule.Experiments show that our method reaches a maximum embedding capacity of 223 bits per carrier.Detection accuracy is 0.500 under XuNet and 0.498 under YeNet.These results are equal to random guessing,showing strong imperceptibility.The proposed method demonstrates superior concealment,higher embedding capacity,and greater robustness against common image distortions and steganalysis attacks.
基金supported by Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/540/46.
文摘Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.
基金supported by the Shanghai Municipal Education Research Project“Exploring the Practical Application of Generative Artificial Intelligence in Cultivating Innovative Thinking and Capabilities of Interdisciplinary Application Technology Talents‘Practice Path’”(C2025299)the university-level postgraduate course project“Software Process Management”(PX-2025251502)of Shanghai Sanda Universitythe key course project at the university level of Shanghai Sanda University,“Introduction to Software Engineering”(PX-5241216).
文摘With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.
基金supported by the National Natural Science Foundation of China under Grant No.12288201。
文摘Synthesizing images or texts automatically becomes a useful research area in the artificial intelligence nowadays.Generative adversarial networks(GANs),proposed by Goodfellow,et al.in 2014,make this task to be done more efficiently by using deep neural networks(DNNs).The authors consider generating corresponding images from a single-sentence input text description using a GAN.Specifically,the authors analyze the GAN-CLS algorithm,which is a kind of advanced method of GAN proposed by Reed,et al.in 2016.In this paper the authors show the theoretical problem with this algorithm and correct it by modifying the objective function of the model.Experiments are performed on the Oxford-102 dataset and the CUB dataset to support the theoretical results.Since the proposed modification can be seen as an idea which can be used to improve all such kind of GAN models,the authors try two models,GAN-CLS and AttnGAN_(GPT).As a result,in both of the two models,the proposed modified algorithm is more stable and can generate images which are more plausible than the original algorithm.Also,some of the generated images match the input texts better,and the proposed modified algorithm has better performance on the quantitative indicators including FID and Inception Score.Finally,the authors propose some future application prospect of the modification idea,especially in the area of large language models.
文摘Modern/distributed electric energy systems,with ever larger penetration of renewable(photovoltaic,wind,wave,and hydro)energy sources and time-variable outputs,are in need of stronger/higher frequency and alternating current(AC)(direct current(DC))voltage control.In fact,faster and more stable active and reactive power in the presence of frequency and voltage sags and swells is needed.Power electronics-controlled variable speed generators do not have enough energy storage(inertia)for the scope(static synchronous compensators(STATCOMs)included).This is because power electronics tends to decouple the generator from the power system.While virtual inertia control in doubly fed induction generators(DFIGs)offers a partial solution to these problems,a more robust and comprehensive framework is required for advanced grid support.This is how,by extending the dual-excitation principles,the dualaxis excited electric synchronous generators(DE-SG)provide superior flexibility in two variants summarized here:as a multifunctional DFIG and dual-axis vs.single-axis excited synchronous generator(SG),and as a synchronous condenser(SC),with dual DC and AC excitation(as a no-load DFIG with inertia wheel),where variable speed is used to accelerate/decelerate the SC and thus provide additional assistance in frequency stabilization.These solutions,good for short-time transients,are not meant,however,to replace the large bidirectional energy storage systems(pump-hydro,hydrogen,batteries,etc.)which are crucial for the daily inherent variations of output energy in modern power systems with multiple power sources.The present paper offers a summary of techniques used in the dual-axis excited vs.single-axis excited SGs(SE-SGs),and SCs topologies,modeling,and control for better stability in modern multiple-source energy systems.This survey includes multiple case studies to shed light on prominent methods.
