Model Predictive Control for Cascaded H-Bridge PV Inverter with Capacitor Voltage Balance

We designed an improved direct-current capacitor voltage balancing control model predictive control(MPC)for single-phase cascaded H-bridge multilevel photovoltaic(PV)inverters.Compared with conventional voltage balanc-ing control methods,the method proposed could make the PV strings of each submodule operate at their maximum power point by independent capacitor voltage control.Besides,the predicted and reference value of the grid-connected current was obtained according to the maximum power output of the maximum power point tracking.A cost function was con-structed to achieve the high-precision grid-connected control of the CHB inverter.Finally,the effectiveness of the proposed control method was verified through a semi-physical simulation platform with three submodules.

A Cascading Fault Path Prediction Method for Integrated Energy Distribution Networks Based on the Improved OPA Model under Typhoon Disasters

In recent times,the impact of typhoon disasters on integrated energy active distribution networks(IEADNs)has received increasing attention,particularly,in terms of effective cascading fault path prediction and enhanced fault recovery performance.In this study,we propose a modified ORNL-PSerc-Alaska(OPA)model based on optimal power flow(OPF)calculation to forecast IEADN cascading fault paths.We first established the topology and operational model of the IEADNs,and the typical fault scenario was chosen according to the component fault probability and information entropy.The modified OPA model consisted of two layers:An upper-layer model to determine the cascading fault location and a lower-layer model to calculate the OPF by using Yalmip and CPLEX and provide the data to update the upper-layer model.The approach was validated via the modified IEEE 33-node distribution system and two real IEADNs.Simulation results showed that the fault trend forecasted by the novel OPA model corresponded well with the development and movement of the typhoon above the IEADN.The proposed model also increased the load recovery rate by>24%compared to the traditional OPA model.

基于Open CASCADE的拱桥建模研究

几何引擎是三维BIM建模软件中的核心组件,对软件的性能、准确性、互操作性和用户体验等方面有着重要影响。商业软件的几何引擎虽然经过了多年的发展和优化具有较好的性能和稳定性,但常受限于软件厂商提供的API接口和商业软件所需要的系统环境,无法对底层数据进行访问或修改,灵活性较差,且不具备完全自主商业版权。基于开源几何引擎的应用在很多领域已有相关的研究成果,但在桥梁工程领域较少,基于开源几何引擎Open CASCADE对桥梁工程领域的模型构建技术进行探索,实现自主可控的桥梁工程三维建模平台,以拱桥为研究对象,结合几何引擎的模型构建技术,实现拱桥模型的创建。探索开源几何引擎在桥梁工程领域的建模实现能力,为实现更多桥型的三维建模以及基于模型更进一步的衍生应用积累经验,并为打造自主可控的桥梁工程建模软件提供参考。

基于Cascade R-CNN的玉米幼苗检测

准确识别玉米幼苗是实现自动化精准除草、间苗、补种等苗期作业的重要前提。为此,针对自然环境下农业机器人对玉米幼苗的检测问题,结合深度残差网络强大的特征提取能力和级联网络连接多个检测器不断优化预测结果的特点,对Cascade R-CNN模型进行改进,使之适用于自然环境下玉米幼苗的检测。模型使用残差网络ResNet-50与特征金字塔网络FPN作为特征提取器提取玉米幼苗图像的特征图,利用区域建议网络生成目标候选框,通过感兴趣区域池化将不同大小的特征图转换为统一尺寸的输出;最后,分类回归模块根据特征图对目标进行分类,并使用边框回归修正候选框的位置和大小,从而完成玉米幼苗目标检测。同时,以3~5叶期玉米幼苗为研究对象,采集其田间图像并制作数据集,用所制作的数据集对Cascade R-CNN模型进行训练,选取AlexNet、VGG16、ResNet18、ResNet50与ResNet50+FPN分别作为特征提取网络进行对比试验,确定所提出的ResNet50+FPN为最优特征提取网络,平均精度均值(mAP)为91.76%,平均检测时间为6.5ms。选取双阶段目标检测模型Faster R-CNN、R-FCN、CoupleNet与以ResNet50+FPN为特征提取网络的Cascade R-CNN进行对比实验,结果表明:Cascade R-CNN模型检测效果最佳、速度最快,且能对自然环境下的玉米幼苗进行有效检测,可为玉米苗期自动化精准作业提供技术支持。

Effects of Deposition Models on Deposition and Performance Deterioration in Axial Compressor Cascade

A new particle deposition model, namely partial deposition model, is developed in order to improve the accuracy of prediction to particle deposition. Concepts of critical velocity and critical angle are proposed and used to determine whether particles are deposited or not. The comparison of numerical results calculated by partial deposition model and existing deposition model shows that the deposition distribution obtained by partial deposition model is more reasonable. Based on the predicted deposition results, the change of total pressure loss coefficient with operating time and the distribution of pressure coefficients on blade surface after 500 hours are predicted by using partial deposition model.

Multivariable identification of membrane fouling based on compacted cascade neural network

The membrane fouling phenomenon,reflected with various fouling characterization in the membrane bioreactor(MBR)process,is so complicated to distinguish.This paper proposes a multivariable identification model(MIM)based on a compacted cascade neural network to identify membrane fouling accurately.Firstly,a multivariable model is proposed to calculate multiple indicators of membrane fouling using a cascade neural network,which could avoid the interference of the overlap inputs.Secondly,an unsupervised pretraining algorithm was developed with periodic information of membrane fouling to obtain the compact structure of MIM.Thirdly,a hierarchical learning algorithm was proposed to update the parameters of MIM for improving the identification accuracy online.Finally,the proposed model was tested in real plants to evaluate its efficiency and effectiveness.Experimental results have verified the benefits of the proposed method.

Convective Storm VIL and Lightning Nowcasting Using Satellite and Weather Radar Measurements Based on Multi-Task Learning Models

Convective storms and lightning are among the most important weather phenomena that are challenging to forecast.In this study,a novel multi-task learning(MTL)encoder-decoder U-net neural network was developed to forecast convective storms and lightning with lead times for up to 90 min,using GOES-16 geostationary satellite infrared brightness temperatures(IRBTs),lightning flashes from Geostationary Lightning Mapper(GLM),and vertically integrated liquid(VIL)from Next Generation Weather Radar(NEXRAD).To cope with the heavily skewed distribution of lightning data,a spatiotemporal exponent-weighted loss function and log-transformed lightning normalization approach were developed.The effects of MTL,single-task learning(STL),and IRBTs as auxiliary input features on convection and lightning nowcasting were investigated.The results showed that normalizing the heavily skew-distributed lightning data along with a log-transformation dramatically outperforms the min-max normalization method for nowcasting an intense lightning event.The MTL model significantly outperformed the STL model for both lightning nowcasting and VIL nowcasting,particularly for intense lightning events.The MTL also helped delay the lightning forecast performance decay with the lead times.Furthermore,incorporating satellite IRBTs as auxiliary input features substantially improved lightning nowcasting,but produced little difference in VIL forecasting.Finally,the MTL model performed better for forecasting both lightning and the VIL of organized convective storms than for isolated cells.

A Multi-Task Motion Generation Model that Fuses a Discriminator and a Generator

The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spatial-temporal structures,and the deep learning model can fully describe the potential semantic structure of human motion.To improve the authenticity of the generated human motion sequences,we propose a multi-task motion generation model that consists of a discriminator and a generator.The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17 crucial human joints in three-freedom degrees.And target motion sequences are created with these styles by the generator.Unlike traditional related works,our model can handle multiple tasks,such as identifying styles and generating data.In addition,by extracting 17 crucial joints from 29 human joints,our model avoids data redundancy and improves the accuracy of model recognition.The experimental results show that the discriminator of the model can effectively recognize diversified movements,and the generated data can correctly fit the actual data.The combination of discriminator and generator solves the problem of low reuse rate of motion data,and the generated motion sequences are more suitable for actual movement.

A Study on Small Pest Detection Based on a CascadeR-CNN-Swin Model

This study aims to detect and prevent greening disease in citrus trees using a deep neural network.The process of collecting data on citrus greening disease is very difficult because the vector pests are too small.In this paper,since the amount of data collected for deep learning is insufficient,we intend to use the efficient feature extraction function of the neural network based on the Transformer algorithm.We want to use the Cascade Region-based Convolutional Neural Networks(Cascade R-CNN)Swin model,which is a mixture of the transformer model and Cascade R-CNN model to detect greening disease occurring in citrus.In this paper,we try to improve model safety by establishing a linear relationship between samples using Mixup and Cutmix algorithms,which are image processing-based data augmentation techniques.In addition,by using the ImageNet dataset,transfer learning,and stochastic weight averaging(SWA)methods,more accuracy can be obtained.This study compared the Faster Region-based Convolutional Neural Networks Residual Network101(Faster R-CNN ResNet101)model,Cascade Regionbased Convolutional Neural Networks Residual Network101(Cascade RCNN-ResNet101)model,and Cascade R-CNN Swin Model.As a result,the Faster R-CNN ResNet101 model came out as Average Precision(AP)(Intersection over Union(IoU)=0.5):88.2%,AP(IoU=0.75):62.8%,Recall:68.2%,and the Cascade R-CNN ResNet101 model was AP(IoU=0.5):91.5%,AP(IoU=0.75):67.2%,Recall:73.1%.Alternatively,the Cascade R-CNN Swin Model showed AP(IoU=0.5):94.9%,AP(IoU=0.75):79.8%and Recall:76.5%.Thus,the Cascade R-CNN Swin Model showed the best results for detecting citrus greening disease.

DISOPE distributed model predictive control of cascade systems with network communication

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

基于Open CASCADE的弯管参数提取与建模系统研发

为解决自动化弯管CAD/CAM系统中弯管数字化模型到加工坐标参数之间的转换问题,提出了基于Open CASCADE的弯管参数提取与参数化建模的方法。通过开源三维几何引擎Open CASCADE读取弯管数字化模型并提取弯管的几何参数信息,然后通过弯管几何参数信息到加工坐标参数的转换方法获得弯管加工坐标参数。根据弯管加工原理通过弯管加工坐标参数使用Open CASCADE来建立弯管数字化模型。根据以上参数提取和参数化建模方法,开发了弯管参数提取与参数化建模软件系统,实现了弯管数字化模型与加工坐标参数的相互转换。该系统能应用于弯管的设计与制造过程,为实现自动化弯管CAD/CAM系统奠定了基础。

Numerical investigation of hydro-morphodynamic characteristics of a cascading failure of landslide dams

A cascading failure of landslide dams caused by strong earthquakes or torrential rains in mountainous river valleys can pose great threats to people’s lives,properties,and infrastructures.In this study,based on the three-dimensional Reynoldsaveraged Navier-Stokes equations(RANS),the renormalization group(RNG)k-εturbulence model,suspended and bed load transport equations,and the instability discriminant formula of dam breach side slope,and the explicit finite volume method(FVM),a detailed numerical simulation model for calculating the hydro-morphodynamic characteristics of cascading dam breach process has been developed.The developed numerical model can simulate the breach hydrograph and the dam breach morphology evolution during the cascading failure process of landslide dams.A model test of the breaches of two cascading landslide dams has been used as the validation case.The comparison of the calculated and measured results indicates that the breach hydrograph and the breach morphology evolution process of the upstream and downstream dams are generally consistent with each other,and the relative errors of the key breaching parameters,i.e.,the peak breach flow and the time to peak of each dam,are less than±5%.Further,the comparison of the breach hydrographs of the upstream and downstream dams shows that there is an amplification effect of the breach flood on the cascading landslide dam failures.Three key parameters,i.e.,the distance between the upstream and the downstream dams,the river channel slope,and the downstream dam height,have been used to study the flood amplification effect.The parameter sensitivity analyses show that the peak breach flow at the downstream dam decreases with increasing distance between the upstream and the downstream dams,and the downstream dam height.Further,the peak breach flow at the downstream dam first increases and then decreases with steepening of the river channel slope.When the flood caused by the upstream dam failure flows to the downstream dam,it can produce a surge wave that overtops and erodes the dam crest,resulting in a lowering of the dam crest elevation.This has an impact on the failure occurrence time and the peak breach flow of the downstream dam.The influence of the surge wave on the downstream dam failure process is related to the volume of water that overtops the dam crest and the erosion characteristics of dam material.Moreover,the cascading failure case of the Xiaogangjian and Lower Xiaogangjian landslide dams has also been used as the representative case for validating the model.In comparisons of the calculated and measured breach hydrographs and final breach morphologies,the relative errors of the key dam breaching parameters are all within±10%,which verify the rationality of the model is applicable to real-world cases.Overall,the numerical model developed in this study can provide important technical support for the risk assessment and emergency treatment of failures of cascading landslide dams.

Numerical Physical Mechanism and Model of Turbulent Cascades in a Barotropic Atmosphere

In a barotropic atmosphere, new Reynolds mean momentum equations including turbulent viscosity, dispersion, and instability are used not only to derive the KdV-Burgers-Kuramoto equation but also to analyze the physical mechanism of the cascades of energy and enstrophy. It shows that it is the effects of dispersion and instability that result in the inverse cascade. Then based on the conservation laws of the energy and enstrophy, a cascade model is put forward and the processes of the cascades are described.

An equivalent circuit model for terahertz quantum cascade lasers:Modeling and experiments

Terahertz quantum cascade lasers（THz QCLs） emitted at 4.4 THz are fabricated and characterized. An equivalent circuit model is established based on the five-level rate equations to describe their characteristics. In order to illustrate the capability of the model, the steady and dynamic performances of the fabricated THz QCLs are simulated by the model.Compared to the sophisticated numerical methods, the presented model has advantages of fast calculation and good compatibility with circuit simulation for system-level designs and optimizations. The validity of the model is verified by the experimental and numerical results.

A CASCADED MODEL OF NEURAL NETWORK FOR PATTERN RECOGNITION

A cascaded model of neural network and its learning algorithm suitable for opticalimplementation are proposed.Computer simulations have shown that this model may successfullybe applied to an error-tolerance pattern recognitions of multiple 3-D targets with arbitrary spatialorientations.

Estimation of Reliability for Stress-Strength Cascade Model

The study endeavors to provide statistical inference for a (1 + 1) cascade system for exponential distribution under joint effect of stress-strength attenuation factors. Estimators of reliability function are obtained using Maximum Likelihood Estimator (MLE) and Uniformly Minimum Variance Unbiased Estimator (UMVUE) of the parameters. Asymptotic distribution of the parameters is also obtained. Comparison between estimators is made using data obtained through simulation experiment.

Influence Maximization for Cascade Model with Diffusion Decay in Social Networks

Maximizing the spread of influence is to select a set of seeds with specified size to maximize the spread of influence under a certain diffusion model in a social network. In the actual spread process, the activated probability of node increases with its newly increasing activated neighbors, which also decreases with time. In this paper, we focus on the problem that selects k seeds based on the cascade model with diffusion decay to maximize the spread of influence in social networks. First, we extend the independent cascade model to incorporate the diffusion decay factor, called as the cascade model with diffusion decay and abbreviated as CMDD. Then, we discuss the objective function of maximizing the spread of influence under the CMDD, which is NP-hard. We further prove the monotonicity and submodularity of this objective function. Finally, we use the greedy algorithm to approximate the optimal result with the ration of 1 ? 1/e.

Cascade插值方法在GRAPES模式中的应用

基于半拉格朗日(semi-Lagrangian)方案的数值天气预报模式,求解半拉格朗日轨迹上游点变量,通常采用传统直线逐点拉格朗日多项式插值,由已知模式格点(欧拉网格点)的数值插值获得。对于三维空间上游点的插值,N阶精度需要O(N3)运算量。N增大,运算量将大幅增加,特别耗费计算机机时,而采用Cascade插值法(降阶插值法)则只需要O(N)运算量。它的显著特点是:用曲线代替直线,通过一系列中间过渡网格点,在曲线上用一维拉格朗日插值,使得相邻拉格朗日格点或中间过渡点的插值不再是孤立的,而且可以重复使用某些中间结果,达到减少运算量的目的。将这种方法合理应用于GRAPES模式,并根据模式的特点,对Cascade插值过程中独立变量的距离分段计算,从而有利于实现并行计算。计算结果表明Cascade插值法与传统直线逐点插值法相比,计算效率平均提高约30%,同时不降低精度。

基于Open CASCADE的机电产品三维结构的集成与准确建模

现有的计算机辅助设计工具虽然在机械、电子各自领域已经十分的成熟,但在机电集成设计和信息交互方面还不是十分有效,特别是在机电产品三维结构的集成建模上存在很大的局限性.为了寻求一种将机电模型准确造型的方法,基于Open CASCADE(OCC)三维造型内核开发了一个机电集成设计平台,在机械设计方面通过对产品数据交换标准(STEP)文件的读写与机械计算机辅助设计(MCAD)配合支持产品机械结构的造型设计;在电气设计方面实现中间数据格式(IDF)中性文件的读写与电子计算机辅助设计(ECAD)配合支持产品电气结构的设计及三维模型的自动生成;通过系统自带的三维电子元件数据库搜寻元件准确的三维模型替换IDF文件中描述的简单元件模型,最终实现机电产品三维结构的集成与准确建模,该模型有效提高了机电产品的设计效率、在后续产品的仿真分析验证及制造所需数据的准确性.模型可通过输出接口以STEP文件格式导出,供其他分析软件使用.

基于Open CASCADE平台的CAD系统开发研究

探讨了开源几何内核库Open CASCADE的特点,着重介绍了基础类、造型数据类、造型算法类、可视化类等主要功能模块,给出了几何模型构建、几何运算、可视化操作等功能开发的基本过程,研究了基于Open CASCADE平台的CAD系统开发的一般方法。使用该方法开发了一款小型三维CAD系统,能够实现几何建模、几何运算和数据转换等功能。