B-Spline曲线聚风装置直线翼垂直轴风力机启动性能数值模拟

为了提升直线翼垂直轴风力机的启动性能,基于B-Spline曲线生成方法,提出一种具有流线型轮廓的聚风装置,将其分别安装在风轮顶端和底端,用以提升风轮附近的来流风速,使风轮汲取更多风能,达到更易启动的特点.选取聚风装置的5个结构参数进行设计:聚风装置与风轮间隙距离ΔL、底圆半径R_(1)、顶圆半径R_(2)、入口角度α_(1)和出口角度α_(2).设计方法采用二次旋转正交组合筛选最优模型,通过三维数值模拟研究聚风装置参数对直线翼垂直轴风力机启动性能的影响,获得了最佳外形参数组合.此外,对有无加装聚风装置的直线翼垂直轴风力机进行了静态三维数值模拟.结果表明,通过添加具有外凸流线型轮廓的聚风装置,直线翼垂直轴风力机的启动性能有显著提升.在风速较低的情况下,性能改善更加明显.聚风装置可使直线翼垂直轴风力机的平均启动力矩系数最大增加38.8%,峰值平均启动力矩系数最大可增加31.2%.

Quantitatively characterizing sandy soil structure altered by MICP using multi-level thresholding segmentation algorithm

The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

Construction of a Multi-Level Strategic System for Cultivating Cultural Industry Management Talents in Colleges and Universities

Through SWOT(strengths,weaknesses,opportunities,and threats)and PEST(political,economic,social,and technological)analysis,this study discusses the construction of a multi-level strategic system for the cultivation of cultural industry management talents in colleges and universities.First of all,based on SWOT analysis,it is found that colleges and universities have rich educational resources and policy support,but they face challenges such as insufficient practical teaching and intensified international competition.External opportunities come from the rapid development of the cultivation of cultural industry management talents and policy promotion,while threats come from global market competition and talent flow.Secondly,PEST analysis reveals the key factors in the macro-environment:at the political level,the state vigorously supports the cultivation of cultural industry management talents;at the economic level,the market demand for cultural industries is strong;at the social level,the public cultural consumption is upgraded;at the technological level,digital transformation promotes industry innovation.On this basis,this paper puts forward a multi-level strategic system covering theoretical education,practical skill improvement,interdisciplinary integration,and international vision training.The system aims to solve the problems existing in talent training in colleges and universities and cultivate high-quality cultural industry management talents with theoretical knowledge,practical skills,and global vision,so as to adapt to the increasingly complex and diversified cultural industry management talents market demand and promote the long-term development of the industry.

基于B-spline的路径规划与轨迹跟踪研究

为满足局部路径规划换道路径下的特性和提高智能驾驶车辆在多种变曲率条件下的路径跟踪精度和稳定性,采用贝塞尔曲线、B-spline等路径规划换道条件下的方法,分别在圆形轨迹、8字形轨迹、S形轨迹、正弦曲线形轨迹路径下,基于MATLAB对4类路径跟踪算法进行分析,得出最合理的B-spline路径规划和跟踪算法。结果表明,B-spline算法在换道点和并道点曲率为0,满足换道条件的要求;MPC算法在正弦轨迹下比其他算法平均误差小约48.6%,在圆形轨迹下比其他算法平均误差小约84%,说明该算法在多种轨迹下都具有很好的跟踪性,能在一定程度上保证智能驾驶汽车的安全性和稳定性,为今后智能车辆路径规划和轨迹跟踪控制研究提供参考。

基于二维B-splines方法研究强磁场中类氢离子He+的低能能级

采用改进的二维B-spline方法在柱坐标系下研究了强磁场下类氢离子He^(+)的结构和电子空间几率密度,计算包含三种对称性0^(+)、0^(-)和(^(-)1)^(+)共10个本征态1 0^(+)、2 0^(+)、ν 0^(-)(ν=1~4)和ν(-1)^(+)(ν=1~4)的能级,选定磁感应强度分别为0、0.001、0.003、0.005、0.007、0.010、0.030、0.050、0.070、0.100、0.200、0.300、0.500、0.700和1.000 a.u.。计算结果表明,类氢离子He^(+)的能级在强磁场中发生劈裂,简并度消除,得到了各个能级随磁感应强度变化规律,并且发现随着磁感应强度的增加能级高低会改变,甚至会发生能级翻转现象;定量地研究了类氢离子He^(+)的基态1 0^(+)和激发态3 0^(-)几率密度分布随磁感应强度的变化规律,并与氢原子进行比较。本研究部分计算结果与他人研究结果十分吻合,有助于进一步理解其他复杂原子的电子运动行为。

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

Explicit Isogeometric Topology Optimization Method with Suitably Graded Truncated Hierarchical B-Spline

This work puts forward an explicit isogeometric topology optimization(ITO)method using moving morphable components(MMC),which takes the suitably graded truncated hierarchical B-Spline based isogeometric analysis as the solver of physical unknown(SGTHB-ITO-MMC).By applying properly basis graded constraints to the hierarchical mesh of truncated hierarchical B-splines(THB),the convergence and robustness of the SGTHB-ITOMMC are simultaneously improved and the tiny holes occurred in optimized structure are eliminated,due to the improved accuracy around the explicit structural boundaries.Moreover,an efficient computational method is developed for the topological description functions(TDF)ofMMC under the admissible hierarchicalmesh,which consists of reducing the dimensionality strategy for design space and the locally computing strategy for hierarchical mesh.We apply the above SGTHB-ITO-MMC with improved efficiency to a series of 2D and 3Dcompliance design problems.The numerical results show that the proposed SGTHB-ITO-MMC method outperforms the traditional THB-ITO-MMCmethod in terms of convergence rate and efficiency.Therefore,the proposed SGTHB-ITO-MMC is an effective way of solving topology optimization(TO)problems.

B-Spline-Based Curve Fitting to Cam Pitch Curve Using Reinforcement Learning

Directly applying the B-spline interpolation function to process plate cams in a computer numerical control(CNC)system may produce verbose tool-path codes and unsmooth trajectories.This paper is devoted to addressing the problem of B-splinefitting for cam pitch curves.Considering that the B-spline curve needs to meet the motion law of the follower to approximate the pitch curve,we use the radial error to quantify the effects of thefitting B-spline curve and the pitch curve.The problem thus boils down to solving a difficult global optimization problem tofind the numbers and positions of the control points or data points of the B-spline curve such that the cumulative radial error between thefitting curve and the original curve is minimized,and this problem is attempted in this paper with a double deep Q-network(DDQN)reinforcement learning(RL)algorithm with data points traceability.Specifically,the RL envir-onment,actions set and current states set are designed to facilitate the search of the data points,along with the design of the reward function and the initialization of the neural network.The experimental results show that when the angle division value of the actions set isfixed,the proposed algorithm can maximize the number of data points of the B-spline curve,and accurately place these data points to the right positions,with the minimum average of radial errors.Our work establishes the theoretical foundation for studying splinefitting using the RL method.

基于B-Spline的位移监测数据动态优化拟合

为更好地进行工程位移的监测,对基坑施工过程钢支撑水平位移激光监测数据特征、误差类型及其成因进行了分析,研究了B-Spline在海量监测数据去噪中的适用性,并通过引入待估参数的B样条基函数修正,建立最小二乘控制下位移-时间关系的高精度拟合优化。结果表明:待估参数ζ_(i)的修正重构,把监测数据序列转化为参数估计问题,使位移时变关系的最优化拟合成为可能;移动时间坐标方法,可实现等时距监测数据的动态处理,优化计算时间;B-Spline融合最小二乘法,在最小方差控制下实现监测数据-时间关系最优拟合,形成光滑、连续的高精度拟合曲线,有利于提高监测反馈控制的精度和可靠性。实例应用验证了成果的可靠性和适用性。

FPGA based hardware platform for trapped-ion-based multi-level quantum systems

We report a design and implementation of a field-programmable-gate-arrays(FPGA)based hardware platform,which is used to realize control and signal readout of trapped-ion-based multi-level quantum systems.This platform integrates a four-channel 2.8 Gsps@14 bits arbitrary waveform generator,a 16-channel 1 Gsps@14 bits direct-digital-synthesisbased radio-frequency generator,a 16-channel 8 ns resolution pulse generator,a 10-channel 16 bits digital-to-analogconverter module,and a 2-channel proportion integration differentiation controller.The hardware platform can be applied in the trapped-ion-based multi-level quantum systems,enabling quantum control of multi-level quantum system and highdimensional quantum simulation.The platform is scalable and more channels for control and signal readout can be implemented by utilizing more parallel duplications of the hardware.The hardware platform also has a bright future to be applied in scaled trapped-ion-based quantum systems.

BITS: an efficient transport solver based on a collocation method with B-spline basis

A B-spline Interpolation Transport Solver(BITS) based on a collocation method is developed. It solves transport equations as a generalized interpolation problem, taking the first-order accuracy in time and the second-order accuracy in space along with a predictor–corrector or under-relaxation iteration method. Numerical tests show that BITS can solve one-dimensional transport equations for tokamak plasma more accurately without additional computation cost, compared to the finite difference method transport solver which is widely used in existing tokamak transport codes.

Neighborhood Effects and Political Trust: A Multi-level Analysis of Chinese Rural-to-Urban Migrants’ Trust in County Government

Massive rural-to-urban migration in China is consequential for political trust: rural-to-urban migrants have been found to hold lower levels of trust in local government than their rural peers who choose to stay in the countryside (mean 4.92 and 6.34 out of 10, respectively, p < 0.001). This article explores why migrants have a certain level of political trust in their county-level government. Using data of rural-to-urban migrants from the China Family Panel Survey, this study performs a hierarchical linear modeling (HLM) to unpack the multi-level explanatory factors of rural-to-urban migrants’ political trust. Findings show that the individual-level socio-economic characteristics and perceptions of government performance (Level-1), the neighborhood-level characteristics-the physical and social status and environment of neighborhoods (Level-2), and the objective macroeconomic performance of county-level government (Level-3), work together to explain migrants’ trust levels. These results suggest that considering the effects of neighborhood-level factors on rural-to-urban migrants’ political trust merits policy and public management attention in rapidly urbanizing countries.

连续短线段拐角加工路径平滑算法

数控机床在处理连续拐角路径时,由于存在大量短线段的转接关系,常导致全局路径不连续和不平滑问题.针对该问题,提出了一种最大曲率和最短路径长度约束的路径拐角优化方法.首先,应用曲线曲率与角度之间的映射关系,以齿轮轮廓轨迹为模型,求得平均转角下的最短路径长度,以及五阶样条拟合曲线的最大曲率;然后,通过约束最大曲率和最短路径长度对离散数据点进行插值;最后,采用德布尔三角递推方法再次进行三阶样条曲线拟合,从而获得约束下的平滑拐角路径.实验表明,所提出的方法精度误差在5 mm以内,最大曲率值降低了20%以上,同时B-Spline曲线满足C2连续性,能够有效提高车床的加工效率.

数控加工中的连续多段直线轨迹B-Spline拟合

为了克服直线和圆弧插补的各种不足,实现曲线插补,针对数控加工中的连续多段小直线刀具轨迹,提出了一种简单快捷的B-Spline曲线(NURBS曲线的特殊形式)拟合算法.该算法以标准的B-Spline最小二乘方法为基础,通过简化后的误差计算模块,可以将拟合后的曲线和原有路径的偏离控制在要求的范围内,并提出了可以实现快速连许多段小直线刀具轨迹拟合的搜索方法-最大拟合区间的稳步前进方法.通过实例计算分析,证明了给出的拟合方法可靠高效.

B-Spline高阶元方法在三维水弹性力学中的应用

本文简要叙述了Ｂ－Ｓｐｌｉｎｅ样条函数的基本性质；将二元高阶Ｂ－Ｓｐｌｉｎｅ函数与Gａｌｅｒｋｉｎ方法相结合，形成了任意光滑空间曲面的二元高阶 Ｂ－ Ｓｐｌｉｎｅ函数簇解析表达方法； 讨论了 Ｂ－ Ｓｐｌｉｎｅ函数在三维水弹性力学边界积分方程中应用的一些数值计算问题。

基于PIA的B-Spline曲面实时交互修改方法

交互修改是几何设计中一种常用的曲线曲面编辑手段,NURBS曲线曲面是CAD系统中曲线曲面的标准表示形式.现有的B-spline曲线曲面交互修改技术往往需要求解一个带约束的能量优化问题,当曲线曲面的控制顶点较多时,这个优化问题的求解过程较慢,难以满足交互操作的实时要求.为此,基于B-spline曲线曲面的局部迭代逼近(PIA)性质提出一种实时的B-spline曲面交互编辑方法.给定一张B-spline曲面和空间待插值目标点集,首先确定距待插值目标点位置最近的曲面上的点以及最近的控制顶点,构造对应于这2个点的主差向量,并将它们扩散到其他控制顶点;然后通过平均操作获得对应于每个控制顶点的差向量;最后通过PIA迭代生成新曲面.该迭代过程的极限曲面就是插值于给定目标点集的曲面.由于PIA迭代仅需调整若干控制顶点,不需求解约束优化问题,使得其在交互修改具有大规模控制网格的B-spline曲面时,在速度方面具有较大优势;同时,文中方法生成的曲面质量与采用能量优化方法得到的曲面质量相比差别不大.

基于B-spline矢量图形数字水印方法

提出了一种针对矢量图形的数字水印算法,用于矢量图形的版权保护。算法利用了B-spline良好的持续逼近曲线形状的特点,将水印嵌入到B-spline的控制点坐标中。经实验证明,该算法对于通常的图形几何变换,如平移、旋转、缩放以及局部修改攻击,均有令人满意的鲁棒性。

一种光顺B-Spline曲线的实用方法

曲线曲面的光顺处理一直是CAGD中研究的热点问题之一,如何快速、简便、正确地光顺曲线曲面,是光顺处理问题研究的核心。文章针对B-spline曲线提出了一种光顺方法,并描述了其基本原理和光顺准则。实例表明,文章所提出的光顺方法快速、简便,是一种较为实用的光顺方法。同时,该方法也可扩展到对曲面的光顺处理。