NON-INTERIOR SMOOTHING ALGORITHM FOR FRICTIONAL CONTACT PROBLEMS

A new algorithm for solving the three-dimensional elastic contact problem with friction is presented. The algorithm is a non-interior smoothing algorithm based on an NCP-function. The parametric variational principle and parametric quadratic programming method were applied to the analysis of three-dimensional frictional contact problem. The solution of the contact problem was finally reduced to a linear complementarity problem, which was reformulated as a system of nonsmooth equations via an NCP-function. A smoothing approximation to the nonsmooth equations was given by the aggregate function. A Newton method was used to solve the resulting smoothing nonlinear equations. The algorithm presented is easy to understand and implement. The reliability and efficiency of this algorithm are demonstrated both by the numerical experiments of LCP in mathematical way and the examples of contact problems in mechanics.

An energy spectrum smoothing algorithm based on TCC-DEE

A smoothing algorithm for energy spectrum based on differential nonlinearity(DNL) error elimination with total counts conservation for high-energy particle detector systems is presented. It is physics based and is only determined by the DNL error of analog-to-digital converter device itself. From the experimental results, this algorithm slightly improves both noise performance and energy resolution, while greatly reduces the testing errors by almost a half compared to their original values. In addition, the reduced-x^2 statistic for evaluating the Gaussian fitting goodness is significantly reduced by almost two orders after smoothing. As a typical verification example,this algorithm is successfully applied in the ground calibration of the Low Energy X-ray Instrument onboard the Hard X-ray Modulation Telescope(HXMT-LE) satellite,lending it a powerful, nondestructive and low-cost tool for both calibration and data processing for high-energy particle detector systems.

Smoothing Newton Algorithm for Linear Programming over Symmetric Cones

By using the theory of Euclidean Jordan algebras,based on a new class of smoothing functions,the QiSun-Zhou's smoothing Newton algorithm is extended to solve linear programming over symmetric cones(SCLP).The algorithm is globally convergent under suitable assumptions.

A distributed algorithm for H_infinity fixed_lag smoothing

In this paper, a new approach to H-infinity fixed-lag smoothing is developed by applying the innovation analysis theory. The smoother is derived by resorting to the augmentation state. However, being completely different from the previous work,the augmented state here is considered as just a theoretical mathematical tool for deriving the estimator. A distributed algorithm for the Riccati equation of the augmented system is presented. The calcuhtion of the estimator does not require any augmentation. The comparison of the computation costs between the new approach and previous work is made. The main technique applied in this paper is the re-organized innovation analysis in an indefinite space.

A Control Strategy for Smoothing Active Power Fluctuation of Wind Farm with Flywheel Energy Storage System Based on Improved Wind Power Prediction Algorithm

The fluctuation of active power output of wind farm has many negative impacts on large-scale wind power integration into power grid. In this paper, flywheel energy storage system (FESS) was connected to AC side of the doubly-fed induction generator (DFIG) wind farm to realize smooth control of wind power output. Based on improved wind power prediction algorithm and wind speed-power curve modeling, a new smooth control strategy with the FESS was proposed. The requirement of power system dispatch for wind power prediction and flywheel rotor speed limit were taken into consideration during the process. While smoothing the wind power fluctuation, FESS can track short-term planned output of wind farm. It was demonstrated by quantitative analysis of simulation results that the proposed control strategy can smooth the active power fluctuation of wind farm effectively and thereby improve power quality of the power grid.

Medical Quality-of-Service Optimization in Wireless Telemedicine System Using Optimal Smoothing Algorithm

In this paper we consider the transmission of stored video from a server to a client for medical applications such as, Telemonitoring, to optimize medical quality of service (m-QoS) and to examine how the client buffer space can be used efficiently and effectively towards reducing the rate variability of the compressed variable bit rate (VBR) video. Three basic results are presented. First, we show how to obtain the greatest possible reduction in rate variability when sending stored video to client with a given buffer size. Second, how to reduce high peak data rate of compressed VBR video when a patient is moving/walking very fast in hospital. Third, we evaluate the impact of optimal smoothing algorithm on the network parameters such as, peak-to-mean ratio, standard deviation, delay, jitter, average delay and average jitter to optimize the m-QoS. To resolve these all problems we used optimal smoothing algorithm and show its performance over a set of long MPEG-4 encoded video traces. Simulation results show that m-QoS is optimized by minimizing network metrics.

The extraction and smoothing algorithms for γ-ray spectrum of a CdZnTe detector system

The extraction algorithms for pulse amplitude and smoothing of energy spectrum have a great influence on energy spectrum of γ-rays during the digital detection and analysis procedure. For a CdZnTe digital γ detector system, different extraction algorithms for pulse amplitude and smoothing of energy spectrum are discussed in this paper. The results show that extraction of pulse amplitude using the first-order derivative method and smoothing of energy spectrum using the wavelet transformation method may obtain energy spectrum with good performance.

Smoothing Newton Algorithm for Nonlinear Complementarity Problem with a PFunction

By using a smoothing function,the P nonlinear complementarity problem(P NCP)can be reformulated as a parameterized smooth equation.A Newton method is proposed to solve this equation.The iteration sequence generated by the proposed algorithm is bounded and this algorithm is proved to be globally convergent under an assumption that the P NCP has a nonempty solution set.This assumption is weaker than the ones used in most existing smoothing algorithms.In particular,the solution obtained by the proposed algorithm is shown to be a maximally complementary solution of the P NCP without any additional assumption.

Smoothing Newton Algorithm for Solving Generalized Complementarity Problem

The generalized complementarity problem includes the well-known nonlinear complementarity problem and linear complementarity problem as special cases.In this paper, based on a class of smoothing functions, a smoothing Newton-type algorithm is proposed for solving the generalized complementarity problem.Under suitable assumptions, the proposed algorithm is well-defined and global convergent.

Smooth constraint inversion technique in genetic algorithms and its application to surface wave study in the Tibetan Plateau

Smooth constraint is important in linear inversion, but it is difficult to apply directly to model parameters in genetic algorithms. If the model parameters are smoothed in iteration, the diversity of models will be greatly suppressed and all the models in population will tend to equal in a few iterations, so the optimal solution meeting requirement can not be obtained. In this paper, an indirect smooth constraint technique is introduced to genetic inversion. In this method, the new models produced in iteration are smoothed, then used as theoretical models in calculation of misfit function, but in process of iteration only the original models are used in order to keep the diversity of models. The technique is effective in inversion of surface wave and receiver function. Using this technique, we invert the phase velocity of Raleigh wave in the Tibetan Plateau, revealing the horizontal variation of S wave velocity structure near the center of the Tibetan Plateau. The results show that the S wave velocity in the north is relatively lower than that in the south. For most paths there is a lower velocity zone with 12-25 km thick at the depth of 15-40 km. The lower velocity zone in upper mantle is located below the depth of 100 km, and the thickness is usually 40-80 km, but for a few paths reach to 100 km thick. Among the area of Ando, Maqi and Ushu stations, there is an obvious lower velocity zone with the lowest velocity of 4.2-4.3 km/s at the depth of 90-230 km. Based on the S wave velocity structures of different paths and former data, we infer that the subduction of the Indian Plate is delimited nearby the Yarlung Zangbo suture zone.

Fast Sparse Multipath Channel Estimation with Smooth L0 Algorithm for Broadband Wireless Communication Systems

Broadband wireless channels are often time dispersive and become strongly frequency selective in delay spread domain. Commonly, these channels are composed of a few dominant coefficients and a large part of coefficients are approximately zero or under noise floor. To exploit sparsity of multi-path channels (MPCs), there are various methods have been proposed. They are, namely, greedy algorithms, iterative algorithms, and convex program. The former two algorithms are easy to be implemented but not stable;on the other hand, the last method is stable but difficult to be implemented as practical channel estimation problems be-cause of computational complexity. In this paper, we introduce a novel channel estimation strategy using smooth L0 (SL0) algorithm which combines stable and low complexity. Computer simulations confirm the effectiveness of the introduced algorithm. We also give various simulations to verify the sensing training signal method.

PERFORMANCE ANALYSIS FOR ATM SWITCHING OF MIXED CONTINUOUS-BIT-RATE AND BURSTY TRAFFIC WITH SMOOTHING FUNCTION

Traditional packet switching networks have typically employed window-based congestion control schemes in order to regulate traffic flow. In ATM networks, the high speed of the communication links and the varied nature of the carried traffic make such schemes inappropriate. Therefore, simpler and more efficient schemes have to be proposed to improve the congestion control for ATM switching. This paper presents an exact performance analysis of ATM switching whose inputs consist of Continuous-Bit-Rate(CBR) and bursty traffic. The CBR traffic and bursty traffic are described by Bernoulli process and the Interrupted Bernoulli Process(IBP), respectively. Bursty traffic smoothing mechanism is analyzed. With the use of a recursive algorithm, the cell loss probability and the average delay for ATM switching of mixed CBR and bursty traffic are exactly calculated. Traffic smoothing could be implemented at a slower peak rate keeping the average rate constant or decreasing the average bursty length. Both numerical

Greedy Algorithm in m-Term Approximation for Periodic Besov Class with Mixed Smoothness

Nonlinear m-term approximation plays an important role in machine learning, signal processing and statistical estimating. In this paper by means of a nondecreasing dominated function, a greedy adaptive compression numerical algorithm in the best m -term approximation with regard to tensor product wavelet-type basis is pro-posed. The algorithm provides the asymptotically optimal approximation for the class of periodic functions with mixed Besov smoothness in the L q norm. Moreover, it depends only on the expansion of function f by tensor pro-duct wavelet-type basis, but neither on q nor on any special features of f.

Multinucleated giant cells of bladder mucosa are modified telocytes:Diagnostic and immunohistochemistry algorithm and relation to PDL1 expression score

BACKGROUND Multinucleated giant cells(MGCs)in bladder carcinomas are poorly studied.AIM To describe the function,morphogenesis,and origin of mononuclear and MGCs in urothelial carcinoma(UC)of the bladder in Bulgarian and French patients.METHODS Urothelial bladder carcinomas(n=104)from 2016-2020 were analyzed retrospectively using immunohistochemical(IHC)and histochemical stain examination.Giant cells in the bladder stroma were found in 35.6%of cases,more often in highgrades.RESULTS We confirm that MGCs in the mucosa in UC of the bladder were positive for both mesenchymal and myofibroblast markers(vimentin,smooth muscle actin,Desmin,and CD34)and the macrophage marker CD68.Furthermore,IHC studies revealed the following profile of these cells:Positive for p16;negative for epithelial(CK AE1/AE3 and GATA-3),vascular(CD31),neural(PS100 and CKIT),cambial,blastic(CD34-blasts and C-KIT),and immune markers(IG G,immunoglobulin G4,and PD-L1);no proliferative activity,possess no specific immune function,and cannot be used to calculate the Combined Positive Score scale.CONCLUSION In conclusion,the giant stromal cells in non-tumor and tumor bladder can be used as a characteristic and relatively constant,although nonspecific,histological marker for chronic bladder damage,reflecting the chronic irritation or inflammation.Likewise,according to the morphological and IHC of the mono-and multinucleated giant cells in the bladder,they are most likely represent telocytes capable of adapting their morphology to the pathology of the organ.

融合改进A^(*)算法和动态窗口法的自动驾驶路径规划

针对自动驾驶汽车路径规划全局最优、耗时最优和避障的需求,提出一种改进A^(*)算法和动态窗口法的融合算法。A^(*)算法主要从启发函数、权重系数、搜索邻域和搜索策略4个方面进行改进,动态窗口法主要改进评价函数。利用改进后的A^(*)算法和双向A^(*)算法完成栅格地图上的全局路径规划,去除冗余节点并平滑处理优化全局路径,利用融合动态窗口算法进行局部路径规划,完成避障。与传统的A^(*)算法相比,改进的A^(*)算法和双向A^(*)算法搜索全局路径耗时和节点显著减少,优化的A^(*)算法与动态窗口法的融合算法具有更高的效率、更好的路径规划能力和避障能力。

位姿约束下的双向扩展机械臂路径规划方法

针对机械臂路径规划方法存在的规划效率低、连杆通过性差、路径粗糙等问题,以渐进最优快速随机搜索树RRT*为基础,提出一种位姿约束下的双向扩展机械臂路径规划方法(PCO-BT-RRT*)。首先,设计目标偏置引导的双向扩展RRT*算法(BT-RRT*),改进了RRT*算法的初始化过程,将起始点和目标点分别作为两棵随机树的初始节点,并通过目标偏置策略引导其以一定概率相向生长,加快探索未知区域,在保证路径代价较低的同时提升路径生成速度。其次,提出一种位姿约束路径优化策略(PCO),采用机械臂运动学模型和碰撞检测规则共同约束新节点扩展过程,寻找机械臂可达空间内的避障路径;对生成路径剪枝剔冗,缩短可行路径长度,同时对消冗节点以迭代调整的方式进行平滑优化,提高路径生成质量。通过仿真实验分析,验证了所提方法在路径规划问题上的显著性成效;在自主研发的BIM信息融合下建筑砌筑系统进行真机避障测试,验证了该方法的实用性。

基于二次转角约束的改进RRT路径规划算法研究

针对快速随机搜索树(RRT)算法存在节点扩展冗余、生成路径不满足车辆转角条件等问题,提出一种改进的二次转角约束RRT算法。首先,在传统RRT算法基础上对采样空间进行裁剪,引入目标导向策略减少采样时间;然后采用车辆膨胀处理和直线方法检测障碍物,并引入第一次转角约束得到粗解路径;接着对粗解路径建立二次转角约束并进行优化处理,获取优化路径后拟合,并进行仿真验证。结果表明,相比于引入目标导向策略的RRT算法,所提出的算法路径最大曲率降低了34.33%,平均曲率降低47.36%,扩展节点数降低47.62%,路径距离降低7.76%,规划时间缩短14.98%。

光滑粒子法中的摩擦接触算法及其在含界面土体变形问题中的应用

光滑粒子流体动力学(smoothed particle hydrodynamics,简称SPH)方法作为拉格朗日型的无网格粒子类方法,在固体极大变形问题的数值求解分析中具有显著优势。针对包含界面接触的固体大变形问题的SPH模拟,基于一种能改善边界精度的光滑粒子插值格式——有限粒子法(finiteparticlemethod,简称FPM),提出了一种新的点对体积的离散(point-to-volume discretization,简称PTVD)界面接触算法。该算法实际是将界面接触力等价转换为接触点附近两组粒子间的外部相互作用力。具体是根据接触界面特点将接触面两侧物体离散后的粒子划分为主体粒子和从属粒子,对于每个接触点附近的从属粒子,根据其影响域所包含的主体粒子情况确定该粒子与接触面的相对关系计算法向接触力,根据其影响域内两类粒子的相对切向速度和界面摩擦系数计算切向接触力。PTVD接触算法可避免界面粒子识别及精确模拟等相关的复杂接触力计算,充分体现了光滑粒子法的非局部特点。在利用经典界面接触和摩擦算例进行验证的基础上,将PTVD算法应用于颗粒土拟静力坍塌和弹体侵彻软土等涉及接触界面的大变形土力学问题SPH数值分析,结果表明PTVD算法在摩擦接触问题的SPH数值分析中具有有效性和广泛适用性。

应用于无人机全局航迹规划的改进双向RRTs算法

针对无人机复杂环境下的全局航迹规划问题,将人工势场法与双向RRTs(Rapidly-exploring random trees)算法结合,提出一种改进双向RRTs算法。首先,目标偏置策略引导采样点以一定概率顺着目标点生成,同时随机树新节点受到障碍物斥力和目标点引力的合力影响有效避开障碍物生长,提高航迹搜寻效率,其次对随机树的节点扩展考虑了无人机飞行性能约束条件,最后采用3阶贝塞尔函数进一步航迹优化。仿真结果表明:二维和三维复杂环境中改进双向RRTs算法相比传统RRT、双向RRTs算法航迹搜索耗时减少了71.3%、24.7%和41.0%、18.6%,验证了改进算法全局搜索能力的快速性和有效性,能很好的应用于无人机离线全局航迹规划场合。

基于改进蚁群算法的机器人全局路径规划

针对传统蚁群算法存在初始信息素缺乏、收敛速度慢以及无法有效躲避障碍物等问题,文中提出了一种基于改进蚁群算法的全局路径规划。引入正态分布函数改进传统启发函数,提高了算法效率,缩短了算法收敛所需时间。自适应调整信息素挥发系数,限定信息素范围,避免过早收敛。对算法路径平滑处理,缩短路径长度,从而实现机器人的全局路径规划。仿真结果表明,在20×20环境下,文中算法平均迭代次数比传统蚁群算法减少了28代,收敛速度更快。平均拐点减少了33.3%,使路径更为平滑,克服了初始信息素缺乏,加快了收敛速度,减少了拐点数量,能够有效躲避环境中的障碍物,证明了该算法的可行性。