Ant colony optimization with dynamic parameter adaptation based on interval type-2 fuzzy logic systems

A dynamic parameter adaptation methodology for Ant Colony Optimization (ACO) based on interval type-2 fuzzy systems is presented in this paper. The idea is to be able to apply this new ACO method with parameter adaptation to a wide variety of problems without the need of finding the best parameters for each particular problem. We developed several fuzzy systems for parameter adaptation and a comparison was made among them to decide on the best design. The use of fuzzy logic is to control the diversity of the solutions, and in this way controlling the exploration and exploitation abilities of ACO. The travelling salesman problem (TSP) and the design of a fuzzy controller for an autonomous mobile robot are the benchmark problems used to test the proposed methodology.     

基于Python的Abaqus二次开发在层压板修理模拟中的应用

为加快复合材料层压板单面贴补参数设计的效率,利用基于Python的Abaqus二次开发技术,开发针对复合材料层压板单面贴补修理的快速建模及后处理程序.结果表明,程序能够通过输入单面贴补设计参数便捷地创建模型,并且计算结束后能通过快速后处理程序自动提取所需的数据和信息.该程序能够针对单面贴补修理母板的长度、宽度、单层厚度、层数和铺层顺序,补片的层数、直径和铺层顺序,以及复合材料单向预浸料和胶膜的性能等参数进行设计分析.用该程序计算得到的易破坏位置和破坏模式与试验结果的误差小于10%,证明该程序可行.     

基于相空间的云计算仿真系统研究与设计

提出并实现了一种基于热力学相空间的云计算集群仿真系统设计方法。将云计算集群节点信息投影到相空间,构建云计算系统的相空间分析模型,将云计算集群节点的参数变化转变为其在相空间投影点的运动,并通过相空间的宏观热力学参数反映云计算集群的整体状态,为云计算核心调度算法的研究和云计算数据中心的运行测试提供了仿真测试平台和衡量标准。实验结果表明,全新构建的云计算仿真系统的仿真实验环境搭建简单,仿真结果能够直观、全面地反映出云计算集群的整体状态和调度算法性能等。     

Stabilizing composite control for a class of linear systems modeled by singularly perturbed Ito differential equations

In this paper, the problem of robust H_∞ control is investigated for sampled-data systems with probabilistic sampling. The parameter uncertainties are time-varying norm-bounded and appear in both the state and input matrices. For the simplicity of technical development, only two different sampling periods are considered whose occurrence probabilities are given constants and satisfy Bernoulli distribution, which can be further extended to the case with multiple stochastic sampling periods. By applying an input delay approach, the probabilistic sampling system is transformed into a continuous time-delay system with stochastic parameters in the system matrices. By linear matrix inequality (LMI) approach, sufficient conditions are obtained, which guarantee the robust mean-square exponential stability of the system with an H_∞ performance. Moreover, an H_∞ controller design procedure is then proposed. An illustrative example is included to demonstrate the effectiveness of the proposed techniques.     

Parameter estimation with scarce measurements

In this paper, the problems of parameter estimation are addressed for systems with scarce measurements. A gradient-based algorithm is derived to estimate the parameters of the input–output representation with scarce measurements, and the convergence properties of the parameter estimation and unavailable output estimation are established using the Kronecker lemma and the deterministic version of the martingale convergence theorem. Finally, an example is provided to demonstrate the effectiveness of the proposed algorithm.     

Redesign of adaptive observers for improved parameter identification in nonlinear systems

We propose a method for redesigning adaptive observers for nonlinear systems. The redesign uses an adaptive law that is based on delayed observers. This increases the computational burden, but gives significantly better parameter identification and robustness properties. In particular, given that a special persistency of excitation condition is satisfied, we prove uniform global asymptotic stability and semi-global exponential stability of the origin of the state and parameter estimation error, and give explicit lower bounds on the convergence rate of both the state and parameter estimation error dynamics. For initial conditions with a known upper bound, we prove tunable exponential convergence rate. To illustrate the use of the proposed method, we apply it to estimate the unmeasured flow rate and the uncertain friction parameters in a model of a managed pressure drilling system. The simulation results clearly show the improved performance of the redesigned adaptive observer compared to a traditional design.     

A modern workflow for force-field development – Bridging quantum mechanics and atomistic computational models

In this article we present our recent efforts in designing a comprehensive consistent scientific workflow, nicknamed Wolf₂ Pack, for force-field optimization in the field of computational chemistry. Atomistic force fields represent a multiscale bridge that connects high-resolution quantum mechanics knowledge to coarser molecular mechanics-based models. Force-field optimization has so far been a time-consuming and error-prone process, and is a topic where the use of a scientific workflow can provide obvious great benefits. As a case study we generate a gas-phase force field for methanol using Wolf₂ Pack, with special attention given toward deriving partial atomic charges.     

135MW机组一次调频功能优化

介绍云浮发电厂#4机组一次调频功能的基本原理。通过试验,发现一次调频功能存在问题,经过分析,对控制逻辑和参数进行修改,解决了存在问题,并使DCS侧的一次调频功能与DEH侧的功能很好地配合,在不影响机组稳定运行下完全有能力参与一次调频。     

Adaptive target recognition

Target recognition is a multilevel process requiring a sequence of algorithms at low, intermediate and high levels. Generally, such systems are open loop with no feedback between levels and assuring their performance at the given probability of correct identification (PCI) and probability of false alarm (Pf) is a key challenge in computer vision and pattern recognition research. In this paper, a robust closed-loop system for recognition of SAR images based on reinforcement learning is presented. The parameters in model-based SAR target recognition are learned. The method meets performance specifications by using PCI and Pf as feedback for the learning system. It has been experimentally validated by learning the parameters of the recognition system for SAR imagery, successfully recognizing articulated targets, targets of different configuration and targets at different depression angles.     

Parameter mapping and data transformation for engineering application integration

Enterprise applications may be classified into management applications used by managerial staffs for making decisions on business operations, and engineering applications used by engineers for solving multidisciplinary design problems. In the literature, enterprise application integration is extensively addressed in the context of management applications, but insufficiently discussed in the engineering disciplines. Practitioners in manufacturing industries have for a long time feel the increasing need of integrating engineering applications, in order to accelerate product development paces and improve design qualities. Integrating engineering applications used in the multiple engineering disciplines has to cope with a number of challenges. This paper focuses on one of the critical issues: parameter mapping and data transformation, which is of pivotal importance to integrating engineering applications. Design parameter mapping provides a consistent approach to data extraction, storage, display and manipulation among different data sources. Data transformation describes the operational logic of parsing input/output files, extracting and transforming data, and maintaining consistency among multiple data sources.