Shape disassembly using generating merging probability

Computer integrated manufacturing uses computer technology to integrate a manufacturing system through a man-machine interface that fills the gap between manual operation and machine processes. It is clear that a computer vision-based man-machine interface makes a fully automated system possible. The basic challenge of a vision-based interface is how to extract information from digitized images and convert it to machine-friendly knowledge. To extract information, then, it often end up to the problem of shape decomposition. This paper proposes an new approach in decomposing compound shapes without prior knowledge of the scene. The proposed algorithm exploits the fact that planar shapes can be completely described by contour segments, and can be decomposed at their maximum concavity into simpler objects. To reduce spurious decomposition, the decomposed segments are merged into groups by analyzing and utilizing the merging hypotheses. The algorithm calculates the linking possibility by weighting the angular differentiation between two segments. The techniques are implemented and are applied to other partial shape matching problems for clustering purposes.     

Parameter identification and position control for helical hydraulic rotary actuators based on particle swarm optimization

It is difficult to obtain an accurate mathematical model in electro-hydraulic servo control system, due to the nonlinear factors such as dead zone, saturation, flow coefficient, and friction. Hence, a parameter identification algorithm, combining recursive least squares (RLS) with modified nonlinear particle swarm optimization (NPSO) algorithm, is proposed. On this basis, another improved NPSO algorithm is also put forward, aiming at searching for the optimal proportional–integral (PI) controller gain of the nonlinear hydraulic system while giving comprehensive consideration to the system performance indexes. The system identification experiments and position tracking control are conducted, respectively. As indicated by the comparison with the least squares (LS), RLS, PSO, and RLS–LPSO results, the proposed method shows higher identification and control accuracy.     

基于积分宽度法测量发光二极管结温方法的研究

本文提出了一种基于光谱积分宽度法来测量发光二极管(light emitting diode, LED)结温的新方法,并进行了理论分析和实验研究。本方法主要分为光谱数据采集、定标函数的测定和结温测量三个过程。首先,为了测量成本的降低和精度的提高而采用在正常工作电流下采集LED光谱数据,并通过采用不同温度下的光谱积分宽度与选定的某一基准状态下的值逐差可得到线性度达0.99以上的定标函数,并通过此定标函数可实时测定任意状态下的结温。其次,为了比较本方法测量结温的精确性,分别对单色和白光LED采用本方法和业界主流的正向电压法,通过自行设计的基于积分宽度法结温测量系统和美国Mentor Graphics公司的T3Ster型仪器的测量结果进行比较,两种方法测出的结温最大偏差为2.1℃,在可接受的误差范围内。实验结果表明积分宽度法测结温具有高效便捷且低成本的的特点,具有一定的应用前景。     

Fixed-time distributed optimization for multi-agent systems with external disturbances over directed networks

This article considers the fixed-time distributed optimization problem of multi-agent systems with external disturbances, in which the global optimization objective is a convex combination of local objective functions. To solve this issue, a directed communication network is carefully designed, and an integral sliding mode control protocol is proposed based on the gradient of global objective function first. Moreover, two distributed optimal protocols are designed by using the gradient and the Hessian matrix of local objective function, respectively. By employing Lyapunov stability theory, graph theory, convex analysis, and inequality techniques, we prove that all proposed protocols can make agents achieve consensus and converge accurately to the optimal solution of the considered problem in some fixed-time intervals. Finally, some numerical simulations are given to verify the feasibility of the theoretical results.     

Event-triggered-based integral reinforcement learning output feedback optimal control for partially unknown constrained-input nonlinear systems

In this paper, an adaptive output feedback event-triggered optimal control algorithm is proposed for partially unknown constrained-input continuous-time nonlinear systems. First, a neural network observer is constructed to estimate unmeasurable state. Next, an event-triggered condition is established, and only when the event-triggered condition is violated will the event be triggered and the state be sampled. Then, an event-triggered-based synchronous integral reinforcement learning (ET-SIRL) control algorithm with critic-actor neural networks (NNs) architecture is proposed to solve the event-triggered Hamilton–Jacobi–Bellman equation under the established event-triggered condition. The critic and actor NNs are used to approximate cost function and optimal event-triggered optimal control law, respectively. Meanwhile, the event-triggered-based closed-loop system state and all the neural network weight estimation errors are uniformly ultimately bounded proved by Lyapunov stability theory, and there is no Zeno behavior. Finally, two numerical examples are presented to show the effectiveness of the proposed ET-SIRL control algorithm.     

Finite-time optimal tracking control for a class of nonlinear systems with prescribed performance

In this paper, a finite-time optimal tracking control scheme based on integral reinforcement learning is developed for partially unknown nonlinear systems. In order to realize the prescribed performance, the original system is transformed into an equivalent unconstrained system so as to a composite system is constructed. Subsequently, a modified nonlinear quadratic performance function containing the auxiliary tracking error is designed. Furthermore, the technique of experience replay is used to update the critic neural network, which eliminates the persistent of excitation condition in traditional optimal methods. By combining the prescribed performance control with the finite-time optimization control technique, the tracking error is driven to a desired performance in finite time. Consequently, it has been shown that all signals in the partially unknown nonlinear system are semiglobally practical finite-time stable by stability analysis. Finally, the provided comparative simulation results verify the effectiveness of the developed control scheme.     

非零和微分博弈系统的事件触发最优跟踪控制（英文）

近年来,对于具有未知动态的非零和微分博弈系统的跟踪问题,已经得到了讨论,然而这些方法是时间触发的,在传输带宽和计算资源有限的环境下并不适用.针对具有未知动态的连续时间非线性非零和微分博弈系统,本文提出了一种基于积分强化学习的事件触发自适应动态规划方法.该策略受梯度下降法和经验重放技术的启发,利用历史和当前数据更新神经网络权值.该方法提高了神经网络权值的收敛速度,消除了一般文献设计中常用的初始容许控制假设.同时,该算法提出了一种易于在线检查的持续激励条件(通常称为PE),避免了传统的不容易检查的持续激励条件.基于李亚普诺夫理论,证明了跟踪误差和评价神经网络估计误差的一致最终有界性.最后,通过一个数值仿真实例验证了该方法的可行性.     

基于神威·太湖之光的非结构网格计算加速算法

在国产异构众核平台神威·太湖之光上的非结构网格计算具有稀疏存储、离散访存、数据依赖等特点,严重制约了众核处理器的性能发挥。为解决稀疏存储和离散访存问题,提出一种N阶对角染色算法,以有效平衡主从核计算并利用从核将全局访存转化为LDM访问。针对数据依赖造成的计算竞争问题,采用自适应和无依赖的任务划分方法,避免并行计算时的数据冲突。为对处理器架构和非结构网格计算进行优化,采用主核与从核异步并行的方式,差异化使用主从核以充分利用硬件资源,同时,取消处理器提供的寄存器通信机制,降低从核阵列的同步开销同时便于扩展到新一代神威平台。此外,使用计算访存异步重叠技术来充分隐藏访存延迟。利用SpMV、Integration、calcLudsFcc算子进行实验,结果表明,相比主核实现,组合加速算法在不同算例规模下平均取得了10倍的加速效果,加速比最高可达24倍,N阶对角染色算法相比非染色分块算法取得了超过5.8倍的性能加速,有效提升了数据局部性和计算并行度。该算法对有依赖关系的计算冲突算子同样具有良好的加速性能,验证了自适应和无依赖任务划分方法的有效性。     

An n‐material thresholding method for improving integerness of solutions in topology optimization

It is common in solving topology optimization problems to replace an integer‐valued characteristic function design field with the material volume fraction field, a real‐valued approximation of the design field that permits ‘fictitious’ mixtures of materials during intermediate iterations in the optimization process. This is reasonable so long as one can interpolate properties for such materials and so long as the final design is integer valued. For this purpose, we present a method for smoothly thresholding the volume fractions of an arbitrary number of material phases which specify the design. This method is trivial for two‐material design problems, for example, the canonical topology design problem of specifying the presence or absence of a single material within a domain, but it becomes more complex when three or more materials are used, as often occurs in material design problems. We take advantage of the similarity in properties between the volume fractions and the barycentric coordinates on a simplex to derive a thresholding, method which is applicable to an arbitrary number of materials. As we show in a sensitivity analysis, this method has smooth derivatives, allowing it to be used in gradient‐based optimization algorithms. We present results, which show synergistic effects when used with Solid Isotropic Material with Penalty and Rational Approximation of Material Properties material interpolation functions, popular methods of ensuring integerness of solutions. Copyright © 2016 John Wiley & Sons, Ltd.     

Drug self-assembly: A phenomenon at the nanometer scale with major impact in the structure–biological properties relationship and the treatment of disease

Under water-rich conditions, small amphiphilic and hydrophobic drug molecules self-assemble into supramolecular nanostructures. Thus, substantial modifications in their interaction with cellular structures and the ability to reach intracellular targets could happen. Additionally, drug aggregates could be more toxic than the non-aggregated counterparts, or vice versa. Moreover, since self-aggregation reduces the number of effective “monomeric” molecules that interact with the target, the drug potency could be underestimated. In other cases, the activity could be ascribed to the non-aggregated molecule while it stems from its aggregates. Thus, drug self-assembly could mislead from drug throughput screening assays to advanced preclinical and clinical trials. Finally, aggregates could serve as crystallization nuclei. The impact that this phenomenon has on the biological performance of active compounds, the inconsistent and often controversial nature of the published data and the need for recommendations/guidelines as preamble of more harmonized research protocols to characterize drug self-aggregation were main motivations for this review. First, the key molecular and environmental parameters governing drug self-aggregation, the main drug families for which this phenomenon and the methods used for its characterization are described. Then, promising nanotechnology platforms investigated to prevent/control it towards a more efficient drug development process are briefly discussed.