Reduction of distortion and improvement of efficiency for gridding of scattered gravity and magnetic data

This paper presents a reasonable gridding-parameters extraction method for setting the optimal interpolation nodes in the gridding of scattered observed data. The method can extract optimized gridding parameters based on the distribution of features in raw data. Modeling analysis proves that distortion caused by gridding can be greatly reduced when using such parameters. We also present some improved technical measures that use human- machine interaction and multi-thread parallel technology to solve inadequacies in traditional gridding software. On the basis of these methods, we have developed software that can be used to grid scattered data using a graphic interface. Finally, a comparison of different gridding parameters on field magnetic data from Ji Lin Province, North China demonstrates the superiority of the proposed method in eliminating the distortions and enhancing gridding efficiency.

Gridding cropland data reconstruction over the agricultural region of China in 1820

Recent studies have demonstrated the importance of LUCC change with climate and ecosystem simulation, but the result could only be determined precisely if a high-resolution underlying land cover map is used. While the efforts based satellites have provided a good baseline for present land cover, what the next advancement in the research about LUCC change required is the development of reconstruction of historical LUCC change especially spatially-explicit historical dataset. Being different from other similar studies, this study is based on the analysis of historical land use patterns in the traditional cultivated region of China. Taking no account of the less important factors, altitude, slope and population patterns are selected as the major drivers of reclamation in ancient China, and used to design the HCGM （Historical Cropland Gridding Model, at a 60 km×60 km resolution）, which is an empirical model for allocating the historical cropland inventory data spatially to grid cells in each political unit. Then we use this model to reconstruct cropland distribution of the study area in 1820, and verify the result by prefectural cropland data of 1820, which is from the historical documents. The statistical analyzing result shows that the model can simulate the patterns of the cropland distribution in the historical period in the traditional cultivated region efficiently.

ARL中Gridding算法的并行化实现

针对海量天文数据实时性处理效率低的问题,通过对SKA图像采集及成像ARL算法库中耗时较长的Gridding算法进行耗时分析,找出了该算法中调用频率高且运行时间长的两个函数convolutional-grid和convolutional-degrid,利用GPU的多线程并行化处理降低两个函数的循环迭代,实现了Gridding算法在GPU和CPU上的协同运行.验证实验结果表明,在相同的数据量下,改进后的Gridding算法运行时间大大缩短,特别是在处理海量数据时,有效提高了ARL的整体运行效率.

Sensitivity Encoding Reconstruction for MRI with Gridding Algorithm

The Sensitivity Encoding (SENSE) parallel reconstruction scheme for magnetic resonance imaging (MRI) is studied and implemented with gridding algorithm in this paper. In this paper, the sensitivity map profile, field map information and the spiral k-space data collected from an array of receiver coils are used to reconstruct un-aliased images from under-sampled data. The gridding algorithm is implemented with SENSE due to its ability in evaluating forward and adjoins operators with non-Cartesian sampled data. This paper also analyzes the performance of SENSE with real data set and identifies the computational issues that need to be improved for further research.

An improved non-uniform fast Fourier transform method for radio imaging of coronal mass ejections

Radioheliographs can obtain solar images at high temporal and spatial resolution,with a high dynamic range.These are among the most important instruments for studying solar radio bursts,understanding solar eruption events,and conducting space weather forecasting.This study aims to explore the effective use of radioheliographs for solar observations,specifically for imaging coronal mass ejections(CME),to track their evolution and provide space weather warnings.We have developed an imaging simulation program based on the principle of aperture synthesis imaging,covering the entire data processing flow from antenna configuration to dirty map generation.For grid processing,we propose an improved non-uniform fast Fourier transform(NUFFT)method to provide superior image quality.Using simulated imaging of radio coronal mass ejections,we provide practical recommendations for the performance of radioheliographs.This study provides important support for the validation and calibration of radioheliograph data processing,and is expected to profoundly enhance our understanding of solar activities.

基于卷积神经网络的燃气表信息自动识别方法研究

提出了一种基于改进的LeNet-5卷积神经网络的识别方法。通过加装摄像头和通信线路的方式,实时采集图像信息,并对图像进行部分预处理。引入Gabor滤波器、ReLU-Softplus函数、SVM分类器等优化传统LeNet-5模型,并根据图像数据的不均衡性,运用Grid Loss函数优化CNN网络,由此,实现燃气表自动化识别方法的构建。在Caffe深度学习框架下进行实验测评,结果表明该方法整体的识别准确性高达99.60%、整个样本集及单幅字码的训练总时间均优于其他识别方法,且对于不完整表码字的识别准确率也达到了99.21%。

Performance Assessment of a Real PV System Connected to a Low-Voltage Grid

The generation of photovoltaic(PV)solar energy is increasing continuously because it is renewable,unlimited,and clean energy.In the past,generation systems depended on non-renewable sources such as oil,coal,and gas.Therefore,this paper assesses the performance of a 51 kW PV solar power plant connected to a low-voltage grid to feed an administrative building in the 6th of October City,Egypt.The performance analysis of the considered grid-connected PV system is carried out using power system simulator for Engineering(PSS/E)software.Where the PSS/E program,monitors and uses the power analyzer that displays the parameters and measures some parameters such as current,voltage,total power,power factor,frequency,and current and voltage harmonics,the used inverter from the type of grid inverter for the considered system.The results conclude that when the maximum solar radiation is reached,the maximum current can be obtained from the solar panels,thus obtaining the maximum power and power factor.Decreasing total voltage harmonic distortion,a current harmonic distortion within permissible limits using active harmonic distortion because this type is fast in processing up to 300 microseconds.The connection between solar stations and the national grid makes the system more efficient.

Fast Algorithm for Maneuvering Target Detection in SAR Imagery Based on Gridding and Fusion of Texture Features

Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms,and introduces the gridding and fusion idea of different texture fea-tures to realize fast target detection.It first grids the original SAR imagery,yielding a set of grids to be classified into clutter grids and target grids,and then calculates the texture features in each grid.By fusing the calculation results,the target grids containing potential maneuvering targets are determined.The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest.The fused texture features,including local statistics features and Gray-Level Co-occurrence Matrix(GLCM),are investigated.The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast de-tection algorithms using real SAR data.The results obtained from the experiments indicate the promising practical application val-ue of our study.

Risk assessment of rockburst using SMOTE oversampling and integration algorithms under GBDT framework

Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.

RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

Global dust density in two-dimensional complex plasma

The driven-dissipative Langevin dynamics simulation is used to produce a two-dimensional(2D) dense cloud, which is composed of charged dust particles trapped in a quadratic potential. A 2D mesh grid is built to analyze the center-to-wall dust density. It is found that the local dust density in the outer region relative to that of the inner region is more nonuniform,being consistent with the feature of quadratic potential. The dependences of the global dust density on equilibrium temperature, particle size, confinement strength, and confinement shape are investigated. It is found that the particle size, the confinement strength, and the confinement shape strongly affect the global dust density, while the equilibrium temperature plays a minor effect on it. In the direction where there is a stronger confinement, the dust density gradient is bigger.

基于STAR-CCM+的船体周围流体流动数值分析及验证结果

In this paper,numerical analyses of fluid flow around the ship hulls such as Series 60,the Kriso Container Ship(KCS),and catamaran advancing in calm water,are presented.A commercial computational fluid dynamic(CFD)code,STAR-CCM+is used to analyze total resistance,sinkage,trim,wave profile,and wave pattern for a range of Froude numbers.The governing RANS equations of fluid flow are discretized using the finite volume method(FVM),and the pressure-velocity coupling equations are solved using the SIMPLE(semi-implicit method for pressure linked equations)algorithm.Volume of fluid(VOF)method is employed to capture the interface between air and water phases.A fine discretization is performed in between these two phases to get a higher mesh resolution.The fluid-structure interaction(FSI)is modeled with the dynamic fluid-body interaction(DFBI)module within the STAR-CCM+.The numerical results are verified using the results available in the literatures.Grid convergence studies are also carried out to determine the dependence of results on the grid quality.In comparison to previous findings,the current CFD analysis shows the satisfactory results.

Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models

Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.

An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.

Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.

A Long-Time-Step-Permitting Tracer Transport Model on the Regular Latitude–Longitude Grid

If an explicit time scheme is used in a numerical model, the size of the integration time step is typically limited by the spatial resolution. This study develops a regular latitude–longitude grid-based global three-dimensional tracer transport model that is computationally stable at large time-step sizes. The tracer model employs a finite-volume flux-form semiLagrangian transport scheme in the horizontal and an adaptively implicit algorithm in the vertical. The horizontal and vertical solvers are coupled via a straightforward operator-splitting technique. Both the finite-volume scheme's onedimensional slope-limiter and the adaptively implicit vertical solver's first-order upwind scheme enforce monotonicity. The tracer model permits a large time-step size and is inherently conservative and monotonic. Idealized advection test cases demonstrate that the three-dimensional transport model performs very well in terms of accuracy, stability, and efficiency. It is possible to use this robust transport model in a global atmospheric dynamical core.

Numerical and experimental investigation into the evolution of the shock wave when a muzzle jet impacts a constrained moving body

The gun-track launch system is a new special launch device that connects the track outside the muzzle.Because it is constrained by the track,the characteristics of development of the muzzle jet differ from those of the traditional muzzle jet.Specifically,it changes from freely developing to doing so in a constrained manner,where this results in an asymmetric direction of flow as well as spatio-temporal coupling-induced interference between various shock waves and the formation of vortices.In this background,the authors of this article formulate and consider the development and characteristics of evolution of the muzzle jet as it impacts a constrained moving body.We designed simulations to test the gun-track launch system,and established a numerical model based on the dynamic grid method to explore the development and characteristics of propagation of disturbances when the muzzle jet impacted a constrained moving body.We also considered models without a constrained track for the sake of comparison.The results showed that the muzzle jet assumed a circumferential asymmetric shape,and tended to develop in the area above the muzzle.Because the test platform was close to the ground,the muzzle jet was subjected to reflections from it that enhanced the development and evolution of various forms of shock waves and vortices in the muzzle jet to exacerbate its rate of distortion and asymmetric characteristics.This in turn led to significant differences in the changes in pressure at symmetric points that would otherwise have been identical.The results of a comparative analysis showed that the constrained track could hinder the influence of reflections from the ground on the muzzle jet to some extent,and could reduce the velocity of the shock waves inducing the motion of the muzzle as well as the Mach number of the moving body.The work here provides a theoretical basis and the requisite technical support for applications of the gun-track launch system.It also sheds light on the technical bottlenecks that need to be considered to recover high-value warheads.

Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart

False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural networkmodels to detect FDIA attacks.However,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection model.To address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation.Furthermore,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal characteristics.Finally,efficient FDIA attack samples can be sequentially generated through interactive adversarial learning.Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.

Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.