Hybrid gradient vector fields for path-following guidance

Guidance path-planning and following are two core technologies used for controlling un-manned aerial vehicles(UAVs)in both military and civilian applications.However,only a few approaches treat both the technologies simultaneously.In this study,an innovative hybrid gradient vector fields for path-following guidance(HGVFs-PFG)algorithm is proposed to control fixed-wing UAVs to follow a generated guidance path and oriented target curves in three-dimensional space,which can be any combination of straight lines,arcs,and helixes as motion primitives.The algorithm aids the creation of vector fields(VFs)for these motion primitives as well as the design of an effective switching strategy to ensure that only one VF is activated at any time to ensure that the complex paths are followed completely.The strategies designed in earlier studies have flaws that prevent the UAV from following arcs that make its turning angle too large.The proposed switching strategy solves this problem by introducing the concept of the virtual way-points.Finally,the performance of the HGVFs-PFG algorithm is verified using a reducedorder autopilot and four representative simulation scenarios.The simulation considers the constraints of the aircraft,and its results indicate that the algorithm performs well in following both lateral and longitudinal control,particularly for curved paths.In general,the proposed technical method is practical and competitive.

Active contours with normally generalized gradient vector flow external force

Gradient vector flow （GVF） is an effective external force for active contours, but its iso- tropic nature handicaps its performance. The recently proposed gradient vector flow in the normal direction （NGVF） is anisotropic since it only keeps the diffusion along the normal direction of the isophotes; however, it has difficulties forcing a snake into long, thin boundary indentations. In this paper, a novel external force for active contours called normally generalized gradient vector flow （NGGVF） is proposed, which generalizes the NGVF formulation to include two spatially varying weighting functions. Consequently, the proposed NGGVF snake is anisotropic and would improve ac- tive contour convergence into long, thin boundary indentations while maintaining other desirable properties of the NGVF snake, such as enlarged capture range, initialization insensitivity and good convergence at concavities. The advantages on synthetic and real images are demonstrated.

Automated measurement of three-dimensional cerebral cortical thickness in Alzheimer’s patients using localized gradient vector trajectory in fuzzy membership maps

Our purpose in this study was to develop an automated method for measuring three-dimensional (3D) cerebral cortical thicknesses in patients with Alzheimer’s disease (AD) using magnetic resonance (MR) images. Our proposed method consists of mainly three steps. First, a brain parenchymal region was segmented based on brain model matching. Second, a 3D fuzzy membership map for a cerebral cortical region was created by applying a fuzzy c-means (FCM) clustering algorithm to T1-weighted MR images. Third, cerebral cortical thickness was three- dimensionally measured on each cortical surface voxel by using a localized gradient vector trajectory in a fuzzy membership map. Spherical models with 3 mm artificial cortical regions, which were produced using three noise levels of 2%, 5%, and 10%, were employed to evaluate the proposed method. We also applied the proposed method to T1-weighted images obtained from 20 cases, i.e., 10 clinically diagnosed AD cases and 10 clinically normal (CN) subjects. The thicknesses of the 3 mm artificial cortical regions for spherical models with noise levels of 2%, 5%, and 10% were measured by the proposed method as 2.953 ± 0.342, 2.953 ± 0.342 and 2.952 ± 0.343 mm, respectively. Thus the mean thicknesses for the entire cerebral lobar region were 3.1 ± 0.4 mm for AD patients and 3.3 ± 0.4 mm for CN subjects, respectively (p < 0.05). The proposed method could be feasible for measuring the 3D cerebral cortical thickness on individual cortical surface voxels as an atrophy feature in AD.

Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity

This paper presents a corner-based image alignment algorithm based on the procedures of corner-based template matching and geometric parameter estimation. This algorithm consists of two stages: 1) training phase, and 2) matching phase. In the training phase, a corner detection algorithm is used to extract the corners. These corners are then used to build the pyramid images. In the matching phase, the corners are obtained using the same corner detection algorithm. The similarity measure is then determined by the differences of gradient vector between the corners obtained in the template image and the inspection image, respectively. A parabolic function is further applied to evaluate the geometric relationship between the template and the inspection images. Results show that the corner-based template matching outperforms the original edge-based template matching in efficiency, and both of them are robust against non-liner light changes. The accuracy and precision of the corner-based image alignment are competitive to that of edge-based image alignment under the same environment. In practice, the proposed algorithm demonstrates its precision, efficiency and robustness in image alignment for real world applications.

Advection-Enhanced Gradient Vector Flow for Active-Contour Image Segmentation

In this paper,we propose a new gradient vector flow model with advection enhancement,called advection-enhanced gradient vector flow,for calculating the external force employed in the active-contour image segmentation.The proposed model is mainly inspired by the functional derivative of an adaptive total variation regularizer whose minimizer is expected to be able to effectively preserve the desired object boundary.More specifically,by incorporating an additional advection term into the usual gradient vector flow model,the resulting external force can much better help the active contour to recover missing edges,to converge to a narrow and deep concavity,and to preserve weak edges.Numerical experiments are performed to demonstrate the high performance of the newly proposed model.

Centerline Extraction for Image Segmentation Using Gradient and Direction Vector Flow Active Contours

In this paper, we propose a fast centerline extraction method to be used for gradient and direction vector flow of active contours. The gradient and direction vector flow is a recently reported active contour model capable of significantly improving the image segmentation performance especially for complex object shape, by seamlessly integrating gradient vector flow and prior directional information. Since the prior directional information is provided by manual line drawing, it can be inconvenient for inexperienced users who might have difficulty in finding the best place to draw the directional lines to achieve the best segmentation performance. This paper describes a method to overcome this problem by automatically extracting centerlines to guide the users for providing the right directional information. Experimental results on synthetic and real images demonstrate the feasibility of the proposed method.

Conservative Vector Fields and the Intersect Rule

This paper covers the concept of a conservative vector field, and its application in vector physics and Newtonian mechanics. Conservative vector fields are defined as the gradient of a scalar-valued potential function. Gradient fields are irrotational, as in the curl in all conservative vector fields is zero, by Clairaut’s Theorem. Additionally, line integrals in conservative vector fields are path-independent, and line integrals over closed paths are always equal to zero, properties proved by the Gradient Theorem of multivariable calculus. Gradient fields represent conservative forces, and the associated potential function is analogous to potential energy associated with said conservative forces. The Intersect Rule provides a new, unique shortcut for determining if a vector field is conservative and deriving potential functions, by treating the indefinite integral as a set of infinitely many functions which satisfy the integral.

计及采样扰动抑制的电压源逆变器三矢量无模型预测电流控制方法

针对传统电压源逆变器无模型预测电流控制(model-free predictive current control,MFPCC)方法存在电流纹波大、电流梯度更新停滞以及预测性能易受采样扰动影响的问题。该文提出一种计及采样扰动的三矢量MFPCC方法。在一个控制周期应用3个基本矢量,并根据价值函数计算矢量作用时间,降低了输出电流纹波;其次,通过建立不同矢量作用下的电流梯度方程组,实现电流梯度数据的实时更新,消除了停滞现象;再次,分析采样扰动对MFPCC的影响,采用扩张状态观测器估计采样扰动以补偿预测电流控制,抑制其对输出电流的影响。最后,通过仿真和实验,对所提方法的有效性进行了验证。

一种基于指令MKS的自动向量化代价模型

自动向量化代价模型是编译器进行自动向量化优化时的重要组成部分,其作用是评估代码在应用向量化转换后能否获得性能提升。当代价模型不准确时,编译器会应用负收益的向量化转换,从而降低程序的执行效率。针对GCC编译器默认代价模型的不精确问题,以Intel Xeon Silver 4214R CPU为平台,提出了一种基于指令MKS的自动向量化代价模型。该模型充分考虑了指令的机器模式、运算类型以及运算强度等,并使用梯度下降算法自动搜索不同指令类型的近似代价。在SPEC2006以及SPEC2017上进行了单线程测试,实验结果表明,该模型能够减少收益评估错误的情况。与默认代价模型生成的向量程序相比,GCC编译器添加MKS代价模型后,在SPEC2006课题上最高获得了4.72%的提速,在SPEC2017课题上最高获得了7.08%的提速。

结合SVM与XGBoost的链式多路径覆盖测试用例生成

机器学习方法可很好地与软件测试相结合,增强测试效果,但少有学者将其运用于测试数据生成方面.为进一步提高测试数据生成效率,提出一种结合SVM(support vector machine)和XGBoost(extreme gradient boosting)的链式模型,并基于此模型借助遗传算法实现多路径测试数据生成.首先,利用一定样本训练若干个用于预测路径节点状态的子模型(SVM和XGBoost),通过子模型的预测精度值筛选最优子模型,并根据路径节点顺序将其依次链接,形成一个链式模型C-SVMXGBoost(chained SVM and XGBoost).在利用遗传算法生成测试用例时,使用训练好的链式模型代替插桩法获取测试数据覆盖路径(预测路径),寻找预测路径与目标路径相似的路径集,对存在相似路径集的预测路径进行插桩验证,获取精确路径,计算适应度值.在交叉变异过程中引入样本集中路径层级深度较大的优秀测试用例进行重用,生成覆盖目标路径的测试数据.最后,保留进化生成中产生的适应度较高的个体,更新链式模型C-SVMXGBoost,进一步提高测试效率.实验表明,C-SVMXGBoost较其他各对比链式模型更适合解决路径预测问题,可提高测试效率.并且通过与已有经典方法相比,所提方法在覆盖率上提高可达15%,平均进化代数也有所降低,在较大规模程序上其降低百分比可达65%.

应用机器学习算法模型预测兴安落叶松地上生物量

为了准确预测兴安落叶松地上生物量,以小兴安岭201株兴安落叶松地上生物量作为研究对象,以胸径(D)和树高(H)为变量,构建随机森林(RF)、人工神经网络(ANN)、支持向量回归(SVR)和梯度提升回归树(GBRT)等4种机器学习模型,并将机器学习算法的预测结果与传统二元生物量模型的预测结果进行对比分析。结果表明:对比传统生物量模型,4种机器学习算法的拟合效果与检验精度均有了大幅度提高。模型拟合精度由高到低的顺序为随机森林、梯度提升回归树、人工神经网络、支持向量回归、传统生物量模型;RF模型在各模型中的拟合精度最高,相对于传统生物量模型,RF模型的确定系数(R~2)提升了3.72%,均方根误差(R_(MSE))降低了44.47%,平均绝对误差(M_(AE))降低了42.81%,相对误差绝对值(M_(PB))降低了42.80%,赤池信息准则值降低了18.17%。模型检验精度由高到低的顺序为随机森林、人工神经网络、梯度提升回归树、支持向量回归、传统生物量模型;RF模型在各模型中的预测精度最高,与传统生物量模型相比,RF模型的确定系数(R~2)提升了1.08%,均方根误差(R_(MSE))降低了10.95%,平均绝对误差(M_(AE))降低了10.34%,相对误差绝对值(M_(PB))降低了10.34%,赤池信息准则值降低了5.20%。因此,相对于传统生物量模型,4种机器学习算法模型均可以提高兴安落叶松地上生物量的预测精度,RF模型的预测精度最高。

GRIN(Gradient Index)介质中的Maxwell方程组与光线光学

利用坡印廷矢量(Poynting vector)的方向就是光线轨迹曲线的切线方向,推出程函方程(Eikonal equation)的矢量式.经分析发现此式包含了光的粒子性与光的波动性因素,光线的传播规律还受介质折射率函数的制约.再由程函方程进一步推得光线方程,并给出了应用实例.

基于轨道结构和电磁特性的电磁发射器电感梯度分析

根据Kerrisk推导得出的矩形轨道电感梯度计算数学模型,构建出轨道高度h、轨道宽度W和轨道间距s与电感梯度L'之间的四维函数变化关系,从而直观地表明了电感梯度随轨道结构参数变化的趋势。为了了解电感梯度的电磁特性变化机理,通过电磁物理量磁失势A推导得出轨道截面平均磁失势A与电感梯度之间的关系式,从而简化了电感梯度的电磁理论分析。物理概念清晰,易于理解与指导工程实践。同时又分别对不同轨道结构参数、不同轨道截面形式和交变电磁环境下,影响电感梯度的主要因素进行电磁特性变化理论分析与研究,从而了解了这些因素对电感梯度的影响。

三芯电缆中间接头压接缺陷多物理场及温度梯度场分析

为解决传统电缆中间接头压接缺陷识别温度敏感性差、环境影响因素多、设备检测精度低等问题,提出了一种基于温度梯度矢量分析的电缆中间接头缺陷识别方法。通过对10 kV三芯电缆开展多场耦合计算,采用矢量分析方法获得了电缆中间接头的温度梯度场和热流密度分布矢量图,形成了电缆中间接头温度梯度场分布规律。结果表明:电缆中间接头表面温度梯度主要受x方向(电缆径向)温度梯度的影响;正常接头表面温度变化率不超过16.6%,其表面温度梯度变化率达49.8%;缺陷接头表面温度变化率不超过21.8%,其表面温度梯度变化率达57.8%,温度梯度场相较温度场有更高的检测灵敏度。开展电缆中间接头温度梯度场分析研究,对电缆接头致热型缺陷运维效率提升有积极意义。

Two Methods to Solve the Ionospheric Electron Concentration Horizontal Gradient at Chongqing

The electron concentration horizontal gradient vector of the ionosphere and its south-north and east-west components over Chongqing station are analyzed and calculated, using the first approximation, time correlation and space correlation and another approach introduced. And then, the validity of the two methods is analyzed and compared.

心电向量图参数与儿童肺动脉压相关性及对肺动脉高压的预测效能研究

目的 探讨心电向量图参数与儿童肺动脉压相关性及对肺动脉高压(pulmonary hypertension, PH)的预测效能。方法 选取2020年2月-2024年2月收治的124例疑似PH患儿,根据临床最终诊断结果分为PH组60例和无PH组64例,入院后均行心电图检查,同步记录常规心电图、心电向量图,比较2组基线资料及平均肺动脉压(mPAP)及心电图向量参数(QRS最大向量振幅、心室复极梯度方位角、QRS最大向量仰角、心室复极梯度仰角、QRS最大向量方位角、心室复极梯度振幅、QRS-T比值、QRS-T夹角),分析心电图向量参数与mPAP相关性及对PH的影响,受试者工作特征(ROC)曲线分析心电图向量参数对PH的预测效能。结果 PH组窦性心律患儿占比低于无PH组,房性心动过速、右心室肥大、房室传导阻滞患儿占比及mPAP高于无PH组(P<0.01)。PH组心室复极梯度方位角、QRS最大向量仰角、心室复极梯度仰角、QRS最大向量方位角、QRS-T夹角高于无PH组,心室复极梯度振幅低于无PH组(P<0.01)。心室复极梯度方位角、QRS最大向量仰角、心室复极梯度仰角、QRS最大向量方位角、QRS-T夹角与mPAP呈正相关(r=0.697、0.766、0.836、0.623、0.793,P<0.001、<0.001、<0.001、<0.001、<0.001),心室复极梯度振幅与mPAP呈负相关(r=-0.828,P<0.001)。Logistic回归分析显示,心室复极梯度方位角、QRS最大向量仰角、心室复极梯度仰角、QRS最大向量方位角、心室复极梯度振幅、QRS-T夹角对PH有影响(P<0.01)。ROC曲线分析显示,心室复极梯度方位角、QRS最大向量仰角、心室复极梯度仰角、QRS最大向量方位角、心室复极梯度振幅、QRS-T夹角联合预测PH的AUC为0.924,敏感度为78.33%,特异度为92.19%。结论 心电向量图参数与肺动脉压力具有显著相关性,临床可通过心电向量图参数预测评估PH,以针对性制订干预方案,改善预后。

导数/梯度的几何意义

利用法向量给出导数/梯度的几何意义,不仅具有直观性,而且可以直接推广到高维情形,为梯度下降法等进一步应用提供了几何解释.

基于机器学习的KRAS抑制剂活性预测模型研究

Kirsten大鼠肉瘤病毒癌基因同系物(Kirsten rat sarcoma viral oncogene homolog,KRAS)基因是最常见的突变癌基因之一,发现KRAS抑制剂对存在该基因突变的癌症患者具有潜在的治疗作用。本研究将机器学习应用于KRAS抑制剂的定量构效关系(quantitative structure-activity relationship,QSAR)模型,从ChEMBL、BindingDB、PubChem 3个数据库中收集了1857条KRAS小分子抑制剂的IC50和SMILES(simplified molecular input line entry system),采用3种不同的特征筛选方式结合随机森林、支持向量机、极端梯度提升机3种机器学习模型,构建了9个不同的分类器。结果表明,SVM模型结合互信息筛选显示出最佳性能:AUC_(test)=0.912,ACC_(test)=0.859,F1_(test)=0.890,并且在外部验证集上也表现出良好的预测性能(AUC_(Ext)=0.944,Recall_(Ext)=0.856,FPR_(Ext)=0.111)。该研究为使用人工智能方法在天然产物数据库中进行KRAS抑制剂筛选提供了新的技术路线。

随机梯度下降优化的量子多分类支持向量机

为改善大规模数据在经典机器学习多分类任务中的计算负担,本文提出了一种基于随机梯度下降优化的量子多分类支持向量机(SGD-MQSVM)算法。通过采用量子随机梯度下降法获得训练参数,并采用全对多分类支持向量机的量子方法进行多分类。算法的时间复杂性可将单次迭代的时间复杂度从经典多项式级降低到对数级。