利用离散平稳小波变换改进NURBS二次曲面拟合方法

为了提高二次曲面的拟合精度,提出了一种基于离散平稳小波变换的NURBS二次曲面的拟合方法。 首先利用格网化方法得到二次曲面模型点云数据的高程图像及其高程序列,其次对此高程序列进行离散平稳小波变换提取出二次曲面模型表面的特征点,最后利用提取的特征点实现高精度NURBS二次曲面拟合。实验结果表明,该方法与NURBS拟合方法相比,球面和圆锥面拟合结果的均方根误差分别降低了55.79%和50.47%,具有较高的拟合精度。     

基于激光扫描仪的点云配准方法

针对激光扫描仪实际扫描得到的不完整点云配准困难问题,提出了一种基于对应点对的配准方法。通过激光扫描仪进行实验,得到被测工件的实测点云数据,基于Visual Studio软件配置Point Cloud Library环境,对实测模型与理想模型的点云配准进行研究。首先对实测点云数据进行体素滤波以及均匀下采样的预处理;其次通过对应点对的方式进行对齐为后续精细配准提供较好的变换初值,后基于ICP算法实现点云配准精配准;最终以均方根误差作为点云配准精度评价指标对配准结果进行评价。借助CloudCompare软件对配准结果进行直观展示分析可知,在实测工件本身存在不绝对光滑的情况下,配准的均方根误差可控制在0.62 mm,表明该方法对于不完整点云的配准效果较好。     

一种长基线中野值点判定、剔除及修正方法

为准确获取水下目标的位置和速度信息,需要对长基线定位中的野值点进行剔除和修正。提出了改进残差检测法用于对野值点的剔除和修正,以卡尔曼滤波的残差绝对值作为判别标准,对野值点进行判别和剔除,以调整后的卡尔曼滤波估计值作为野值点的修正值,针对滤波模型与实际运动不匹配导致滤波前后数据偏差较大的问题,选择对正常点的数据不做处理。湖上实验结果表明,对存在野值点的定位轨迹,未剔除野值点的定位均方根误差为55.68 m,使用残差检测法处理后的定位均方根误差为8.11 m,使用改进残差检测法处理后的定位均方根误差为2.04 m。改进残差检测法可以对长基线定位轨迹中的野值点进行判定、剔除和修正,减小定位误差,提升长基线系统定位精度。     

基于法向量权重改进的ICP算法

针对三维重建过程中点云配准的精度和速度不理想的问题,提出一种基于法向量权重改进的迭代最近点(ICP)算法。通过将点云的法向量投射到高斯球上,统计不同方向法向量的分布情况,结合物体的几何结构信息赋予相应的权重,利用法向量权重结合点到平面的误差度量方法计算最优刚体变换矩阵。实验结果证明：以球面点云数据为例,与改进前的迭代最近点(ICP)算法相比,在配准速度没有降低的情况下,配准误差减小为原来的30%左右,而且该算法适用于各种点云模型,效果显著。     

Error analysis of high-precision DEM generated from airborne dual-antenna interferometric SAR data

结合机载双天线干涉合成孔径雷达(InSAR)正侧视成像模型,分析了机载双天线InSAR数据处理中存在的影响数字高程模型(DEM)高程精度和点位精度的主要因素,包括平台高度、斜距、干涉相位、基线长度、基线倾角、中心多普勒频率以及载机姿态,着重分析了平台高度、基线长度、基线倾角以及侧视角等因素的变化与DEM误差之间的关系.选用机载双天线InSAR数据进行了干涉实验,并对生成的DEM进行了误差分析,实验分析结果和误差定量分析的结果相一致.对机载双天线InSAR数据处理进行误差分析有利于提高DEM的高程和点位精度,也有利于机载双天线InSAR系统设计及飞行试验设计,因而具有重要实用意义.     

无人机遥感在开发建设项目水土保持监测数据获取中的应用

为了解决大面积的开发建设项目水土保持监测效率和精度问题,该文以陕西省宝鸡市为研究对象,基于无人机遥感技术对辖区内的开发建设项目进行监测,并采用机载三维激光雷达扫描技术进行平行试验,研究其监测数据获取方法及监测效果。结果表明,不同高程范围的三维数字高程模型数据中误差呈现不同程度的波动,随着高程差的不断减少,其中误差也不断减少;不同高程差的三维数字高程模型数据中误差的集中度存在显著不同;无人机遥感技术的测绘成果精度较高,其平面误差控制在0.0084 m～0.1357 m,远小于0.250 m的规范控制要求。     

二次插值鲸鱼优化算法在圆柱度误差评定中的应用

为了进一步提高圆柱度误差评定的精度和计算收敛速度,以国际标准中的最小区域原则为准,建立并求解圆柱度误差的测量模型,在标准鲸鱼优化算法的基础上,对其进行改进,设计一种二次插值鲸鱼优化算法,最后,通过圆柱度测量数据,对算法进行仿真和测试,证明算法在圆柱度误差的评定精度和收敛速度上有了进一步的提高。     

基于三维激光扫描的煤堆建模系统设计与实现

在国内外煤炭码头中,一般采用散料堆场的形式储存货物,通过堆料机将煤炭卸到堆场上,再通过取料机将煤炭取料装船。煤堆的三维建模对于实现全自动堆取料和煤堆的体积测量都起到关键的作用,由于煤炭在装卸过程中灰尘较大,且存在各种恶劣天气的影响,所以在堆取料机作业过程中实现实时煤堆建模存在很大的难度。设计一套智能化的煤堆建模系统,主要原理为通过三维激光扫描仪将煤堆的三维点云信息实时的采集到服务器上,经过滤波、规格化、数学拟合等算法处理,将点云信息转化为标准的三维煤堆模型,并自动计算煤堆的体积,为全自动堆取料作业提供数据基础。     

Delaunay-固定距离滑动邻域Kriging算法

地质统计学中的Kriging 算法是利用空间变异结构进行插值预报的算法,作为一种区域性算法,邻近点的选择是Kriging 算法实际应用中无法回避的重要问题。文章结合温度场计算的实际应用详细分析了Kriging 邻近点选择中需要考虑的原则,并采用Delaunay 三角划分搜索和固定距离搜索相结合的邻近点搜索策略,提出一种利用变程的Delaunay-固定距离滑动邻域算法。通过温度场数据的计算结果证明新算法在精确度上优于普通固定半径的滑动邻域Kriging 算法。     

基于地形数据的三维模型的建立

数字高程模型不仅作为一种基础数据,更作为一种应用方法,在地学领域发挥越来越重要的作用。内容包括:首先介绍DEM的含义,其次叙述DEM的数据的获取,然后又对数字高程模型表面建模进行说明,再次详细的描述DEM三维可视化方法和步骤,最后对DEM精度的主要影响因素及其质量控制进行概述。     

基于密度聚类和圆外切线斜率拟合的埋地排水管道声呐点云去噪技术

针对环扫声呐扫描结果受到排水管道内混合水声的影响问题,提出一种声呐扫描点云数据去噪的方法:在环扫声呐扫描得到的三维点云数据基础上,通过密度聚类优化算法,去除点云数据中的噪声,然后采用类圆外切线斜率拟合的方式,识别出管道内壁界限和淤泥淤积线,最终得出包含排水管道内壁界限和淤积线特征的模型。为验证有效性,以武汉市某排水管道为例进行分析,基于现场采集的98万个点云坐标进行数据去噪和特征提取,结果表明:采用密度聚类优化算法进行点云数据初筛后,通过圆外切线斜率拟合算法能有效识别出排水管道内壁界限和淤积线,拟合半径均方误差0.0071m,相较于单一密度聚类算法拟合精度更高,去噪效果更好。     

融合加权SVD改进算法的工业机器人运动学标定

针对环境或人为因素引入的测量粗差对测量坐标系和机器人基坐标系的转换存在较大影响的问题,对奇异值分解(SVD)算法进行了改进,并将其应用于机器人运动学标定中。以ABB-IRB2600型机器人为研究对象,建立修正型D-H(MD-H)运动学模型和误差模型;通过激光跟踪仪测量得到机器人末端靶球位置坐标,在SVD算法中,根据补偿前位置误差大小对测量数据重新分配权重,转换测量坐标系和机器人基坐标系;使用Levenberg-Marquart(L-M)算法进行了误差参数辨识,并在Matlab中对机器人25个运动学参数进行了仿真补偿。仿真和实验结果表明,加权SVD算法稳定性更优,能够减小测量粗差影响,经标定后机器人的平均绝对误差降低了65.10%,均方根误差降低了65.85%,其绝对定位精度得到了明显提高。     

未知输入干扰下异构多传感器分布式两级信息滤波与偏差联合估计

针对目标跟踪过程中受未知输入影响的多传感器网络,提出一种局部单传感器抗干扰信息滤波算法并根据此算法实现分布式一致性多传感器融合滤波估计实现目标的精确跟踪。首先,建立包含未知输入的系统模型;其次,消除未知输入影响并设计局部单传感器两级信息滤波算法实现状态和广义偏差的同时估计;最后,根据提出的单传感器两级信息滤波算法进行分布式加权数据融合。仿真结果表明,该方法及其融合算法的系统偏差、状态估计误差和均方根误差均明显降低,目标跟踪精度有所提高,并且具有较低的运算量和较高的一致性。     

Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error?=?3.362 and root mean square error?=?0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.     

低信噪比环境下的语音识别方法研究

单通道语音信号在信噪比较大的环境下经过增强后再识别,能表现出较高的识别率。但是在低信噪比环境下,增强后语音信号的识别率急剧下降。针对此种情况,提出了一种用在识别系统前端的语音增强算法,该增强算法将采集到的带噪语音信号先使用对数最小均方误差(Logarithmic Minimum Mean Square Error,Log MMSE)提高其信噪比,然后再利用改进的维纳滤波去除噪声残留并提升语音可懂度,最后用梅尔频率倒谱系数(Mel-Frequency Cepstral Coefficients,MFCC)和隐马尔科夫模型(Hidden Markov Model,HMM)对增强后的语音信号做特征提取并识别。实验分析结果表明,该方法能有效地抑制背景噪声并减少噪声残留,显著提升低信噪比环境下语音识别的准确性。     

基于拉格朗日乘子法的空间圆弧拟合优化方法

针对传统的空间圆弧拟合方法鲁棒性低、拟合精度不高等问题,提出了一种鲁棒性较强的空间圆弧拟合优化方法。首先,以拉格朗日乘子法为基础,基于平面条件约束建立目标函数,从而得出空间圆弧拟合方程;其次,采用RANSAC（random sample consensus,随机抽样一致）算法剔除错误跟踪点,将RANSAC算法的高稳定性应用到空间圆弧拟合的点云优化中,进而提高拟合精度。最后,通过实验分析验证了所提空间圆弧拟合优化方法的可行性,并与传统拟合方法进行比较,分析所提方法的拟合精度。实验结果表明：普通圆弧点云拟合的相对精度在0.003左右,复杂圆弧点云拟合的相对精度在0.01左右;相较于传统拟合方法,所提方法有效解决了拟合精度低及鲁棒性差等问题。研究结果表明提出的空间圆弧拟合优化方法一方面可运用拉格朗日乘子法增强鲁棒性,另一方面可通过采用RANSAC方法剔除错误点以提高拟合精度,具有广泛的工程实际应用价值。     

Adaptive Error Curve Learning Ensemble Model for Improving Energy Consumption Forecasting

Despite the advancement within the last decades in the field of smart grids, energy consumption forecasting utilizing the metrological features is still challenging. This paper proposes a genetic algorithm-based adaptive error curve learning ensemble (GA-ECLE) model. The proposed technique copes with the stochastic variations of improving energy consumption forecasting using a machine learning-based ensembled approach. A modified ensemble model based on a utilizing error of model as a feature is used to improve the forecast accuracy. This approach combines three models, namely CatBoost (CB), Gradient Boost (GB), and Multilayer Perceptron (MLP). The ensembled CB-GB-MLP model’s inner mechanism consists of generating a meta-data from Gradient Boosting and CatBoost models to compute the final predictions using the Multilayer Perceptron network. A genetic algorithm is used to obtain the optimal features to be used for the model. To prove the proposed model’s effectiveness, we have used a four-phase technique using Jeju island’s real energy consumption data. In the first phase, we have obtained the results by applying the CB-GB-MLP model. In the second phase, we have utilized a GA-ensembled model with optimal features. The third phase is for the comparison of the energy forecasting result with the proposed ECL-based model. The fourth stage is the final stage, where we have applied the GA-ECLE model. We obtained a mean absolute error of 3.05, and a root mean square error of 5.05. Extensive experimental results are provided, demonstrating the superiority of the proposed GA-ECLE model over traditional ensemble models.     

A Novel Twist Deformation Model of Soft Tissue in Surgery Simulation

Real-time performance and accuracy are two most challenging requirements in virtual surgery training. These difficulties limit the promotion of advanced models in virtual surgery, including many geometric and physical models. This paper proposes a physical model of virtual soft tissue, which is a twist model based on the Kriging interpolation and membrane analogy. The proposed model can quickly locate spatial position through Kriging interpolation method and accurately compute the force change on the soft tissue through membrane analogy method. The virtual surgery simulation system is built with a PHANTOM OMNI haptic interaction device to simulate the torsion of virtual stomach and arm, and further verifies the real-time performance and simulation accuracy of the proposed model. The experimental results show that the proposed soft tissue model has high speed and accuracy, realistic deformation, and reliable haptic feedback.     

An Improved BPNN Prediction Method Based on Multi-Strategy Sparrow Search Algorithm

Data prediction can improve the science of decision-making by making predictions about what happens in daily life based on natural law trends. Back propagation (BP) neural network is a widely used prediction method. To reduce its probability of falling into local optimum and improve the prediction accuracy, we propose an improved BP neural network prediction method based on a multi-strategy sparrow search algorithm (MSSA). The weights and thresholds of the BP neural network are optimized using the sparrow search algorithm (SSA). Three strategies are designed to improve the SSA to enhance its optimization-seeking ability, leading to the MSSA-BP prediction model. The MSSA algorithm was tested with nine different types of benchmark functions to verify the optimization performance of the algorithm. Two different datasets were selected for comparison experiments on three groups of models. Under the same conditions, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the prediction results of MSSA-BP were significantly reduced, and the convergence speed was significantly improved. MSSA-BP can effectively improve the prediction accuracy and has certain application value.     

A DEM-based residual kriging model for estimating groundwater levels within a large-scale domain: a study for the Fuyang River Basin

It is a basis of the effective groundwater resources management to understand the movements and changes of groundwater, wherein the information about spatial distribution of groundwater levels is indispensable. Geostatistical methods like kriging have been widely used to estimate groundwater levels based on observation wells. The errors are inevitably introduced through the interpolation process; hence, how to increase the accuracy, which is based on limited well data, has become an urgent issue for estimating groundwater levels, especially for a large area. This study developed an integrated DEM-based residual kriging model for estimating groundwater levels within a large-scale domain. The model can yield more physically plausible estimates of groundwater levels in a large-scale domain than those currently in use by effectively utilizing well data and considering the influences of terrain morphology on the groundwater flow. The model was then applied to the Fuyang River Basin, a 5,000 km² investigating area, in the North China for estimating the regional groundwater levels and flow. The Kolmogorov–Smirnov test was employed to prove that the DEM information could markedly facilitate the residuals to approach a normal distribution, which insures satisfied estimate accuracy. For demonstrating the advantages of the proposed DEM-based trend surface, three types of trend surface using both simple and quadratic equations were developed to estimate groundwater levels. Based on the verification points, the mean error, the mean absolute error, and the square root of the quadratic multiply error, for each trend-surface equation were compared. The DEM-based trend surface equations were discovered with the highest accuracy. The results indicated that quadratic equation could more effectively present the trend surface than simple one with a higher correlation coefficient. However, for a large-scale estimation domain with limited well data, the simple equation for DEM-based trend surface showed more feasible with better accuracy than the quadratic one. Further research on improving trend-surface simulation to more effectively reflect system complexities would be desired.