An improved fiber tracking algorithm based on fiber assignment using the continuous tracking algorithm and two-tensor model

This study tested an improved fiber tracking algorithm, which was based on fiber assignment using a continuous tracking algorithm and a two-tensor model. Different models and tracking decisions were used by judging the type of estimation of each voxel. Thismethod should solve the cross-track problem. This study included eight healthy subjects, two axonal injury patients and seven demyelinating disease patients. This new algorithm clearly exhibited a difference in nerve fiber direction between axonal injury and demyelinating disease patients and healthy control subjects. Compared with fiber assignment with a continuous tracking algorithm, our novel method can track more and longer nerve fibers, and also can solve the fiber crossing problem.

Solution and Application about Conversion Tensor of Motion in Dynamic Modeling of 6-HTRT Parallel Robot

Equivalent integrated finite element method is a canonical and efficient modeling method in dynamic analysis of complex mechanism. The key of establishing dynamic equations of spatial mechanism by the method is to confirm Jacobian matrix reflecting relations of all joints,nodes,and generalized coordinates,namely,relations of second-order and corresponding third-order conversion tensors. For complex motion relations of components in a parallel robot,it gives second-order and third-order conversion tensors of dynamic equations for the 6-HTRT parallel robot based on equivalent integrated finite element method. The method is suitable for the typical robots whose positions of work space and sizes of mechanism are different. The solving course of the method is simple and convenient,so the method lays the foundation of dynamic analysis for robots.

High Order Tensor Forms of Growth Curve Models

In this paper, we first study the linear regression model and obtain a norm-minimized estimator of the parameter vector by using the g-inverse and the singular value decomposition of matrix X. We then investigate the growth curve model (GCM) and extend the GCM to a generalized GCM (GGCM) by using high order tensors. The parameter estimations in GGCMs are also achieved in this way.

Tensor-Centric Warfare VI: A Global Warfare Model

We propose a global warfare model that integrates the models of the whole tensor-centric warfare series, represented as a high-dimensional entangled warfare category. Its underpinning metaphysics is “entangled fusion”: this is the macroscopic entanglement concept inspired by high-dimensional (HD) quantum computation (the “quantum brain”), in which any number of entangled wave-functions can be highly correlated, with neuron-like signaling among them. From this entangled perspective, war and battle is seen essentially as a holistic phenomenon: if any one of a set of mutually entangled warring parties is removed from the equation, then the war as it is instantly stops, possibly to be replaced by a new conflict between the remaining parties but distinct from that which it supplants. The formal global warfare framework developed in this paper expresses this fundamental idea of arbitrary many interrelated/entangled conflicts, each of them defined by its own battle-manifold (with warfighting tensor fields acting on it) and occurring (more-or-less) simultaneously on the planet;we call this entangled

Explicit Algebraic Stress Model for Three-Dimensional Turbulent Buoyant Flows Derived Using Tensor Representation

An explicit algebraic stress model (EASM) has been formulated for two-dimensional turbulent buoyant flows using a five-term tensor representation in a prior study. The derivation was based on partitioning the buoyant flux tensor into a two-dimensional and a three-dimensional component. The five-term basis was formed with the two-dimensional component of the buoyant flux tensor. As such, the derived EASM is limited to two-dimensional flows only. In this paper, a more general approach using a seven-term representation without partitioning the buoyant flux tensor is used to derive an EASM valid for two- and three-dimensional turbulent buoyant flows. Consequently, the basis tensors are formed with the fully three-dimensional buoyant flux tensor. The derived EASM has the two-dimensional flow as a special case. The matrices and the representation coefficients are further simplified using a four-term representation. When this four-term representation model is applied to calculate two-dimensional homogeneous buoyant flows, the results are essentially identical with those obtained previously using the two-dimensional component of the buoyant flux tensor. Therefore, the present approach leads to a more general EASM formulation that is equally valid for two- and three-dimensional turbulent buoyant flows.

基于加速无约束张量隐因子分解模型的Web服务Qo S估计

针对基于张量非负隐因子分解模型的Web服务QoS估计方法过于依赖非负初始随机数据以及特意设计的非负训练方法,导致模型的兼容性和扩展性不高的问题,提出了加速无约束张量隐因子分解模型。其主要思想包括三部分:将非负性约束从决策参数转移到输出的隐因子,并通过单元素映射函数连接它们;运用结合动量方法的随机梯度下降算法,有效提高模型的收敛速度与估计精度;给出加速无约束张量隐因子分解模型的详细算法和结果分析。在实际工业应用中的2个动态QoS数据集上的实证研究表明,与最先进的QoS估计模型相比,所提模型具有较高的计算效率和估计精度。

Extended intrinsic mean spin tensor for turbulence modelling in non-inertial frame of reference

We investigate the role of extended intrinsic mean spin tensor introduced in this work for turbulence modelling in a non-inertial frame of reference. It is described by the Euclidean group of transformations and, in particular, its significance and importance in the approach of the algebraic Reynolds stress modelling, such as in a nonlinear K-ε model. To this end and for illustration of the effect of extended intrinsic spin tensor on turbulence modelling, we examine several recently developed nonlinear K-ε models and compare their performance in predicting the homogeneous turbulent shear flow in a rotating frame of reference with LES data. Our results and analysis indicate that, only if the deficiencies of these models and the like be well understood and properly corrected, may in the near future, more sophisticated nonlinear K-ε models be developed to better predict complex turbulent flows in a non-inertial frame of reference.

Diffusion tensor imaging fiber tracking with reliable tracking orientation and flexible step size

We propose a method of reliable tracking orientation and flexible step size fiber tracking. A new directional strategy was defined to select one optimal tracking orientation from each directional set, which was based on the single-tensor model and the two-tensor model. The directional set of planar voxels contained three tracking directions： two from the two-tensor model and one from the single- tensor model. The directional set of linear voxels contained only one principal vector. In addition, a flexible step size, rather than fixable step sizes, was implemented to improve the accuracy of fiber tracking. We used two sets of human data to assess the performance of our method; one was from a healthy volunteer and the other from a patient with low-grade glioma. Results verified that our method was superior to the single-tensor Fiber Assignment by Continuous Tracking and the two-tensor eXtended Streamline Tractography for showing detailed images of fiber bundles.

虚拟手术中基于Tensor-Mass的变形仿真技术

详细论述并实现了虚拟手术中软组织变形仿真技术。虚拟软组织由四面体网格构成,生物力学模型使用Tensor-Mass模型。推导了一种半隐式数值积分算法用以求解软组织变形的动力学方程。该方法比传统的显式积分算法更稳定,并可将积分时间步长提高一个数量级。使用了基于拓扑距离的方法进一步减少数值积分算法的计算量。最后给出使用肝脏模型进行变形仿真的结果。

基于低秩分解和向量量化的深度网络压缩方法

随着人工智能的发展,深度神经网络成为多种模式识别任务中必不可少的工具,由于深度卷积神经网络(CNN)参数量巨大、计算复杂度高,将它部署到计算资源和存储空间受限的边缘计算设备上成为一项挑战。因此,深度网络压缩成为近年来的研究热点。低秩分解与向量量化是深度网络压缩中重要的两个研究分支,其核心思想都是通过找到原网络结构的一种紧凑型表达,从而降低网络参数的冗余程度。通过建立联合压缩框架,提出一种基于低秩分解和向量量化的深度网络压缩方法——可量化的张量分解(QTD)。该方法能够在网络低秩结构的基础上实现进一步的量化,从而得到更大的压缩比。在CIFAR-10数据集上对经典ResNet和该方法进行验证的实验结果表明,QTD能够在准确率仅损失1.71个百分点的情况下,将网络参数量压缩至原来的1%。而在大型数据集ImageNet上把所提方法与基于量化的方法PQF(Permute,Quantize,and Fine-tune)、基于低秩分解的方法TDNR(Tucker Decomposition with Nonlinear Response)和基于剪枝的方法CLIP-Q(Compression Learning by In-parallel Pruning-Quantization)进行比较与分析的实验结果表明,QTD能够在相同压缩范围下实现更好的分类准确率。

知识图谱补全方法研究综述

知识图谱是用来描述世界中存在的各种实体和概念以及他们之间的关系的一种语义网络,近年来被广泛应用于智能问答、智能推荐和信息检索等领域。目前,大多数知识图谱都具有不完整性,因此,知识图谱补全成为一项重要的任务。根据模型构造方法的不同,将知识图谱补全模型分为传统知识图谱补全模型、基于神经网络的知识图谱补全模型和基于元学习的知识图谱补全模型三类,对这三种知识图谱补全模型的分类情况进行介绍;总结知识图谱补全方法所使用的数据集和评价指标,并从各个模型优点和不足等方面对各类模型进行详细的对比分析。最后,对知识图谱补全进行归纳与总结,并展望未来的研究方向。

基于VAR-LRTC-TNN的交通流量数据补全框架模型

从各类传感系统收集到的交通流数据往往会因探测器或通信故障等缘故出现数据连续性的缺失,故准确补全缺失的交通流数据对制定合理的交通管理策略至关重要。鉴于交通流数据具有低秩的特性,通过低秩张量补全模型可较好地刻画出交通流数据的全局一致性,但却无法很好地捕捉数据的局部变化趋势,一定程度上影响了效果。基于此,提出了将VAR模型和基于残差序列的LRTC-TNN模型相结合的交通流补全框架模型;采用VAR模型对缺失数据进行粗略估计,移除平均趋势,利用LRTC-TNN模型对残差时间序列进行补全,再将平均趋势还原,从而完成对交通流量数据的高精度补全;该方法不仅保留了交通流数据的全局结构,还考虑了数据局部变化的特征。研究结果表明:与基于原始交通流量数据的填充方法相比,该模型框架对单传感器和多传感器数据的连续性缺失均具有更高的补全精度。

一种多点拟合的恒定立体角纤维重建模型

基于弥散磁共振成像DTI的纤维追踪技术是非侵入性活体脑神经研究的关键技术.恒定立体角重建模型CSA是基于DTI发展而来的一种纤维重建模型,能够根据采样球壳上的数据对弥散方向分布函数进行线性径向投影计算,从而进行纤维重建.目前,恒定立体角纤维重建模型存在鲁棒性较差,重建纤维过于杂乱以及弥散方向分布偏差的问题.针对上述问题,本文提出MCSA(Multipoint Constant Solid Angle)模型,首先引入可以使弥散方向分布函数更加准确的最小二乘法,接着通过自适应高斯函数引入多点拟合弥散信息提高模型鲁棒性和抗噪性.最后,本文分别使用Fibercup、ISMRM2015年模拟数据以及Stanford HARDI真实影像数据对传统CSA模型以及本文提出MCSA模型进行对比分析,结果表明,利用本文提出MCSA模型重建的纤维更加符合客观规律,并且在一定程度上减少了假阳性纤维的生成.

HOSVD-based LPV modeling and mixed robust H_2/H_∞ control design for air-breathing hypersonic vehicle

This paper focuses on synthesizing a mixed robust H_2/H_∞ linear parameter varying（LPV） controller for the longitudinal motion of an air-breathing hypersonic vehicle via a high order singular value decomposition（HOSVD） approach.The design of hypersonic flight control systems is highly challenging due to the enormous complexity of the vehicle dynamics and the presence of significant uncertainties.Motivated by recent results on both LPV control and tensor-product（TP） model transformation approach,the velocity and altitude tracking control problems for the air-breathing hypersonic vehicle is reduced to that of a state feedback stabilizing controller design for a polytopic LPV system with guaranteed performances.The controller implementation is converted into a convex optimization problem with parameterdependent linear matrix inequalities（LMIs） constraints,which is intuitively tractable using LMI control toolbox.Finally,numerical simulation results demonstrate the effectiveness of the proposed approach.

THEORETICAL MODEL OF EFFECTIVE STRESS COEFFICIENT FOR ROCK/SOIL-LIKE POROUS MATERIALS

Physical mechanisms and influencing factors on the effective stress coefficient for rock/soil-like porous materials are investigated, based on which equivalent connectivity index is proposed. The equivalent connectivity index, relying on the meso-scale structure of porous material and the property of liquid, denotes the connectivity of pores in Representative Element Area （REA）. If the conductivity of the porous material is anisotropic, the equivalent connectivity index is a second order tensor. Based on the basic theories of continuous mechanics and tensor analysis, relationship between area porosity and volumetric porosity of porous materials is deduced. Then a generalized expression, describing the relation between effective stress coefficient tensor and equivalent connectivity tensor of pores, is proposed, and the expression can be applied to isotropic media and also to anisotropic materials. Furthermore, evolution of porosity and equivalent connectivity index of the pore are studied in the strain space, and the method to determine the corresponding functions in expressions above is proposed using genetic algorithm and genetic programming. Two applications show that the results obtained by the method in this paper perfectly agree with the test data. This paper provides an important theoretical support to the coupled hydro-mechanical research.

基于张量链的电网大数据多模态预测方法

为了优化大数据预测系统的准确率和运算耗时,在张量理论的基础上,提出了一种适用于电网领域的多模态预测方法。通过综合运用张量和马尔科夫理论,设计了一种具有较强适应性的多元多阶马尔科夫模型,以及无假设前提的马尔科夫转移方法。在此基础上,基于张量链理论的短期预测和长期预测算法,提出了具有较低计算复杂度的大数据多模态预测方法。相关仿真验证结果表明,与经典马尔科夫预测方法相比,基于张量链的多模态预测方法具有更高的预测准确率与更少的运算耗时。

Contour tracking using weighted structure tensor based variational level set

A novel contour tracking method using weighted structure tensor based variational level set is proposed in this paper.The image is first converted to weighted structure tensor field by extracting apositive definite symmetric covariance matrix for each pixel.Then,a level set method is employed to represent object contour implicitly which separates the image domain into two areas each modeled by tensor field based Gaussian mixture model separately.By solving agradient flow equation of energy functional with respect to the level set,the object contour will converge to its real profile in the newly arrived frame.Experimental results on several video sequences demonstrate the better performance of our method than the other two contour tracking algorithms.

Tensor-Centric Warfare I: Tensor Lanchester Equations

We propose the basis for a rigorous approach to modeling combat, specifically under conditions of complexity and uncertainty. The proposed basis is a tensorial generalization of earlier Lanchester-type equations, inspired by the contemporary debate in defence and military circles around how to best utilize information and communications systems in military operations, including the distributed C4ISR system (Command, Control, Communications, Computing, Intelligence, Surveillance and Reconnaissance). Despite attracting considerable interest and spawning several efforts to develop sound theoretical frameworks for informing force design decision-making, the development of good frameworks for analytically modeling combat remains anything but decided. Using a simple combat scenario, we first develop a tensor generalization of the Lanchester square law, and then extend it to also include the Lanchester linear law, which represents the effect of suppressive fire. We also add on-off control inputs, and discuss the results of a simple simulation of the final model using our small scenario.

Two-dimensional regularized inversion of AMT data based on rotation invariant of Central impedance tensor

Considering the uncertainty of the electrical axis for two-dimensional audo-magnetotelluric(AMT) data processing, an AMT inversion method with the Central impedance tensor was presented. First, we present a calculation expression of the Central impedance tensor in AMT, which can be considered as the arithmetic mean of TE-polarization mode and TM-polarization mode in the twodimensional geo-electrical model. Second, a least-squares iterative inversion algorithm is established, based on a smoothnessconstrained model, and an improved L-curve method is adopted to determine the best regularization parameters. We then test the above inversion method with synthetic data and field data. The test results show that this two-dimensional AMT inversion scheme for the responses of Central impedance is effective and can reconstruct reasonable two-dimensional subsurface resistivity structures. We conclude that the Central impedance tensor is a useful tool for two-dimensional inversion of AMT data.

Phase transitions of the five-state clock model on the square lattice

Using the tensor renormalization group method based on the higher-order singular value decomposition, we have studied the phase transitions of the five-state clock model on the square lattice. The temperature dependence of the specific heat indicates the system has two phase transitions, as verified clearly by the correlation function at three representative tem- peratures. By calculating the magnetic susceptibility, we obtained only the upper critical temperature as To2 = 0.9565（7）. Investigating the fixed-point tensor, we precisely locate the transition temperatures at Tcl = 0.9029（1） and Tc2 = 0.9520（1）, consistent well with the Monte Carlo and the density matrix renormalization group results.