Active set truncated-Newton algorithm for simultaneous optimization of distillation column

An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are made for the various large-scale problems of varying size. The comparison results between ASTNA and the subspace limited memory quasi-Newton algorithm and between the modified augmented Lagrange multiplier methods combined with ASTNA and the modified barrier function method show the stability and effectiveness of ASTNA for simultaneous optimization of distillation column.

Active Contours and Mumford-Shah Segmentation Based on Level Sets

This paper is to detect regions (objects) boundaries, also to isolate and extract individual components from a medical image. This can be done using an active contours to detect regions in a given image, based on techniques of curve evolution, Mumford Shah functional for segmentation and level sets. The paper classified the images into different intensity regions based on Markov random field, then detected regions whose boundaries are not necessarily defined by gradient by minimizing an energy of Mumford Shah functional for segmentation which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a mean curvature flow like evolving the active contour, which will stop on the desired boundary. The stopping term does not depend on the gradient of the image, as in the classical active contour and the initial curve of level set can be anywhere in the image, and interior contours are automatically detected. The final image segmentation is one closed boundary per actual region in the image.

An Active-Set Projected Trust Region Algorithm for Box Constrained Optimization Problems

An active-set projected trust region algorithm is proposed for box constrained optimization problems, where the given algorithm is designed by three steps. First, the projected gradient direction which normally has better numerical performance is introduced. Second, the projected trust region direction that often possesses good convergence is defined, where the matrix of trust region subproblem is updated by limited memory strategy. Third, in order to get both good numerical performance and convergence, the authors define the final search which is the convex combination of the projected gradient direction and the projected trust region direction. Under suitable conditions, the global convergence of the given algorithm is established. Numerical results show that the presented method is competitive to other similar methods.

UNILATERAL CONTACT PROBLEMS USING QUASI-ACTIVE SET STRATEGY

The unilateral contact problem can be formulated as a mathematical programming with inequality constraints. To resolve the difficulty in dealing with inequality constraints, a quasi-active set strategy algorithm was presented. At each iteration, it transforms the problem into one without contact in terms of the solution obtained in last iteration and initiates the current iteration using the solution of the transformed problem, and updates a group of contact pairs compared with Lemke algorithm that uqdates only one pair of contact points. The present algorithm greatly enhances the efficiency and numerical examples demonstrate the effectiveness and robustness of the proposed algorithm.

A Dynamic Active-Set Method for Linear Programming

An efficient active-set approach is presented for both nonnegative and general linear programming by adding varying numbers of constraints at each iteration. Computational experiments demonstrate that the proposed approach is significantly faster than previous active-set and standard linear programming algorithms.

The Application of Goal-Setting Theory in the Second Classroom Teaching Activities of College English

Interest is the precondition and motivation for people's accomplishment.However,due to the exam-oriented system,millions of parents and teachers neglect the interests of children,and our children nearly forget their interests or hobbies.Based on different interests of children,the thesis analyzes how to apply the goal-setting theory in the second classroom teaching activities of College English,using the example of Lushan College of Guangxi University of Technology.

Acquisition and Active Navigation of Knowledge Particles throughout Product Variation Design Process

The variation design of complex products has such features as multivariate association, weak theory coupling and implicit knowledge iteration. However, present CAD soft wares are still restricted to making decisions only according to current design status in dynamic navigation which leads to the huge drain of the knowledge hidden in design process. In this paper, a method of acquisition and active navigation of knowledge particles throughout product variation design process is put forward. The multi-objective decision information model of the variation design is established via the definition of condition attribute set and decision attribute set in finite universe. The addition and retrieval of the variation semantics is achieved through bidirectional association between the transplantable structures and variation design semantics. The mapping relationships between the topology lapping geometry elements set and constraint relations set family is built by means of geometry feature analysis. The acquisition of knowledge particles is implemented by attribute reduction based on rough set theory to make multi-objective decision of variation design. The topology lapping status of transplantable substructures is known from DOF reduction. The active navigation of knowledge particles is realized through embedded event-condition-action（ECA） rules. The independent prototype system taking Alan, Charles, Ian＇s system（ACIS） as kernel has been developed to verify the proposed method by applying variation design of complex mechanical products. The test results demonstrate that the navigation decision basis can be successfully extended from static isolated design status to dynamic continuous design process so that it more flexibly adapts to the different designers and various variation design steps. It is of profound significance for enhancing system intelligence as well as improving design quality and efficiency.

Fast Image Segmentation Based on a Two-Stage Geometrical Active Contour

A fast two-stage geometric active contour algorithm for image segmentation is developed. First, the Eikonal equation problem is quickly solved using an improved fast sweeping method, and a criterion of local minimum of area gradient (LMAG) is presented to extract the optimal arrival time. Then, the final time function is passed as an initial state to an area and length minimizing flow model, which adjusts the interface more accurately and prevents it from leaking. For object with complete and salient edge, using the first stage only is able to obtain an ideal result, and this results in a time complexity of O(M), where M is the number of points in each coordinate direction. Both stages are needed for convoluted shapes, but the computation cost can be drastically reduced. Efficiency of the algorithm is verified in segmentation experiments of real images with different feature.

Motion Geometric Active Contours: Tracking Nonrigid Objects in Clutter Background

MGAC (Motion Geometric Active Contours), a new variational framework of geometric active contours to track multiple nonrigid moving objects in the clutter background in image sequences is presented. This framework, incorporating with the motion edge information, consists of motion detection and tracking stages. At the motion detection stage, the motion edge map provides an approximate edge map of the moving objects. Then, a tracking stage, merely using the static edge information, is considered to improve the motion detection result. Force field regularization method is used to extend the capture range of the edge attraction force field in both stages. Experiments demonstrate that the proposed framework is valid for tracking multiple nonrigid objects in the clutter background.

REGION-BASED ACTIVE CONTOUR DRIVEN BY GLOBAL INTENSITY FITTING ENERGY

In this paper, we present a novel region-based active contour model based on global in-tensity fitting energy in a variational level set framework. Meanwhile, an internal energy term is in-troduced, and it forces the level set function to be close to a signed distance function. Image global information utilized efficiently makes the proposed model insensitive to noise, and the introduced penalty term can avoid the costly re-initialization for the evolving level set function, which not only speeds up the contour evolvement, but also improves accuracy of the final contour. Comparisons with other classical region-based models, such as Chan-Vese model and Region-Scalable Fitting (RSF) model, show the advantages of our model in terms of efficiency and accuracy. Moreover, the model is robust to noise.

Language Environment Setting Through Our Foreign Currency Activities to Improve Chinese-Majoring Learners’ Competency

Language education centers primarily on fostering linguistic skills and language communication skills for students. In the process of teaching Chinese language, it is in the classroom that is the place tofoster student’s linguistic competence and language communication capacity. Therefore, classroom teaching becomes the main place tofoster the language ability and language communication capacity for students while the extracurricular activities are the place for students to apply the knowledge they have learned in the classroom in to the real world. Conducting a survey to investigate extracurricular activities for Chinese language majors at Thai Nguyen University, School of Foreign Languages, the paper proposes solutions to improve the effectiveness of extracurricular activities for students and create excitement for students in learning.

Effect of the Formulation of Sodium Activation Solutions on the Setting Time of Metakaolin Based Geopolymers

Geopolymer materials today represent innovative products,used frequently as a substitute for cementitious traditional materials.They are obtained by the action of an alkaline activation solution(composed of mainly of silicon dioxide(SiO2)and sodium hydroxide(NaOH)and water)on a powder natural or synthetic aluminosilicates.In this work,we seek to highlight the effect of the percentage of sodium dioxide firstly,on the evolution of the viscosity of the alkali-activated solution and secondly,on the evolution of the viscosity of geopolymeric solution.Another aspect of this work is the determination of the effect of this percentage on the kinetics that characterize the start of the percolation phenomenon(transition from the fresh state to the hardened state).At last result concerns the impact of temperature on this transition.This contribution consolidates the control protocols for the formulation of geopolymers and allows the optimization of the processes of their exploitation.

教育数字化战略引领下大学生数字素养培养多组态路径

文章基于活动理论模型,运用fsQCA方法分析教育数字化战略引领下大学生数字素养培养的多组态路径及不同要素之间的联动匹配关系。研究发现,大学生高数字素养培养包括思维主导型、资源任务型、协同驱动型和内涵自主型4条组态路径,不同前因变量之间存在替代关系;数字化思维和评价反馈机制同时存在于4条组态路径中,表明二者是大学生高数字素养不可或缺的前因条件。在此基础上,从学校、教师、学生3个层面提出大学生数字素养培养策略,即落实国家教育数字化战略,优化校园顶层设计;强化教师职业素养,完善人才培养反馈机制;增强数字道德意识,提升学生数字化思维。

硅酸钠改善碱激发材料力学性能与凝结机理分析

为了进一步增强碱激发材料的力学性能,掌握碱激发材料的凝结规律,通过掺入不同种类与掺量硅酸钠进行增硅改性,增强矿渣碱激发材料反应活性,定期观测试件的力学性能并分析凝结规律,同时结合微观分析手段深入剖析硅酸钠的增硅增强机理。结果表明,固态与液态硅酸钠均能够显著提高试件的抗压强度,明显缩短初凝与终凝时间。随固态硅酸钠掺量的提升,试件抗压强度呈先上升后下降趋势,固态硅酸钠在试件内大量结晶是导致抗压强度下降的主要原因。随液态硅酸钠掺量的提升,试件抗压强度呈先显著上升后趋于平稳的趋势,这说明其增强作用具有最大限值,液态硅酸钠的激发效果明显优于固态。

基于改进型SM-ADRC的PMSM控制策略研究

在永磁同步电机(PMSM)控制中,传统的滑模自抗扰控制(SM-ADRC)能够减小外部负载变化和参数变化导致控制系统性能下降所带来的影响,但是还存在控制器参数太多,整定过于复杂化的问题,故提出一种改进型SM-ADRC方法。设计了一种新型变指数趋近律及新型控制函数将其引入扩展状态观测器(ESO)和非线性状态误差反馈控制律(NLSEF),得到新型滑模状态观测器(SM-ESO)和新型滑模状态误差反馈控制律(SM-NLSEF),减少了SM-ESO和SM-NLSEF待整定参数个数,简化了SM-ADRC参数整定。建立Simulink仿真模型和Links-RT半实物仿真实验平台,仿真和实验结果均表明:与传统SM-ADRC相比,改进型SM-ADRC在简化参数整定的同时提高了系统的响应速度和系统的抗扰性能,减小了电流脉动。

基于传感器的人体活动识别与应用

近年来,人体活动识别技术得到了迅速发展,并在安全防御、医疗健康、智能交互、体育运动等领域得到广泛应用。本文结合国内外的相关文献,围绕人体活动识别,对知识图谱、人体活动数据集及人体活动识别方法及应用进行综述。首先,指出了不同数据采集方式的人体活动识别数据集的特点。其次,梳理了各种不断提高模型的准确度和性能的人体活动识别具体方法。再次,指出了知识图谱在人体识别活动中发挥的揭示实体间的关系,为人体活动解析提供隐含知识等重要作用。最后,面对人体活动识别的实际应用,对当前研究存在的问题和发展趋势进行了总结和展望。本研究为人体活动识别研究的技术发展和实际应用提供了方向指导和技术建议。

考虑宏观结构性能的多材料微结构拓扑优化设计

多孔微结构材料以其质量轻、比刚度/比强度高、抗冲击等优异力学性能,被广泛应用于航空航天等工程领域。相比单一材料设计,多材料设计可获得力学性能更优的微结构拓扑构型。拓扑优化是实现多材料布局设计的有效方法之一。通过构建基于差集的多材料水平集描述模型和交替活动相算法,提出了基于参数化水平集的多材料微结构拓扑优化设计方法。首先,构建了基于差集的多材料水平集描述模型,利用N个水平集函数实现N+1相材料拓扑的精确描述。然后,引入交替活动相算法,将原始N+1相多材料优化问题分解为N(N+1)/2个双相材料优化子问题,以减少设计变量与约束条件,提高计算效率。最后,采用均匀化方法计算多材料微结构的等效弹性张量,以宏观结构柔度最小化为优化目标,各相材料用量为约束条件,构建多材料微结构拓扑优化模型,并利用优化准则法实现上述优化模型的高效求解。数值算例结果表明,所提方法可有效实现多材料微结构的拓扑优化设计,所设计微结构均具有光滑的结构边界和清晰的材料界面。

基于可穿戴设备的开放集动作识别技术研究

随着智能手表、手环等可穿戴设备的普及,将其用于人体行为识别领域并从中解码出人类行为活动,对于健康监测、日常行为分析、智能家居等应用具有重要意义。然而,传统的动作识别算法存在特征提取困难、识别准确率较低等问题,并且均基于封闭集假设,即所有的训练数据和测试数据均来自同一个标签空间,而现实世界中大多都是开放集(Open-Set)场景,在测试阶段可能会将未知标签样本送入模型,从而导致分类错误。文中针对人体动作识别问题,提出了多通道自适应卷积网络(Multi-channel Adaptive Convolutional Network,MCACN),针对传统CNN网络特征提取仅局限于一个小范围内的问题,自适应卷积模块能够使用不同大小的卷积核提取不同时间跨度的特征,并自动计算权重求和。此外MCACN的多通道结构使各传感器数据得以分头进行处理,获得能够区分相近动作的特征细节。最后,设计了基于标签的多元变分自编码器,提出了用于开放集识别的模型MCACN-VAE。该模型能够通过计算重建误差来识别未知类,聚焦于已知类别动作,提高了模型的健壮性。实验结果表明,在封闭集实验中,MCACN模型能够有效地对动作进行识别,对7种日常动作的识别准确率均达到了91%以上,总体准确率达到了95%。在开放集实验中,MCACN-VAE在不同开放度下对于已知类别的总体识别准确率均达到了89%以上,对于未知动作片段的识别准确率也保持在75%以上,证明了所提模型能够有效拒绝未知类,识别已知类。

电容电压平衡的四电平有源中性点钳位逆变器的改进模型预测电流控制

多电平有源中点钳位(ANPC)逆变器具有功率器件电压应力小、电流谐波低和器件损耗可调控等优点,被广泛应用于工业驱动中。针对四电平ANPC(4L-ANPC)逆变器的控制与调制研究相对较少,现有的调制方案实现复杂,中点电压(NPV)控制难度较大。因此,该文针对三相4L-ANPC逆变器,提出一种改进的有限集模型预测电流控制(FS-MPCC)算法,避免了复杂的调制过程。首先,使用仅包含电流误差的代价函数对位于六边形顶点的6个大电压矢量进行寻优;其次,对参与第一步寻优的大矢量划分扇区,使用包含电流误差和电容电压误差的代价函数对最优大矢量所在扇区的所有电压矢量滚动寻优,得到目标电压矢量并作用于逆变器;最后,仿真和实验结果表明,与传统的FS-MPCC相比,所提方法在降低系统计算负担的同时,还具有较好的控制性能和电容电压平衡能力。