应用于PLC控制程序的Petri网执行模型

在现有的工业控制系统中经常使用PLC实现顺序控制、定时等功能。控制程序经常需要执行同步操作且输入开关量的触发时机具有不确定性,因此无法充分描述真实的执行过程。为此提出下述解决方法:首先在控制程序的编译阶段,使用Petri网建立网模型以表示程序执行逻辑,其次在控制程序的执行阶段,根据程序真实执行状况动态运行上述网模型,当程序运行结束时网模型停止执行,由此判断程序中存在的逻辑错误。     

一种基于BPEL的通用安全控制模块设计方法

随着面向服务架构(SOA)的提出,软件系统的整合与开发速度将越来越快,但软件系统的安全机制却是每次开发时都需要重新建立,并且随着软件系统日益增多,安全机制的复杂程度将不断提升。提出一种基于商业流程执行语言(BPEL)的通用安全控制模块设计方法,以减少相应的权限开发与管理工作,同时还以制造业企业资源计划系统（ERP）与电子商务系统账户整合为例说明权限模块的运行机制。     

The control methods of Knee-Ankle-Toe Active Transfemoral Prosthesis in stance phase

In this paper, a Knee-Ankle-Toe Active Transfemoral Prosthesis (KATATP) is designed for improving the walking performance of the amputees. The main contents are as follows: First, the mechanical model of the prosthesis with three joints is established to help analyze the kinematics and dynamics characteristics of the joints; second, the Drive Torque Calculation cell is proposed to calculate the torque required for the movement of prosthesis joints; third, the motor, which provides the desired torque, drives the prosthesis; finally, experimental results indicate that the KATATP can help the amputee obtain more natural and symmetrical walking gaits. Moreover, the plantar pressure information of the amputated side is close to that of the sound side.     

引入通信与探索的多智能体强化学习QMIX算法

非平稳性问题是多智能体环境中深度学习面临的主要挑战之一,它打破了大多数单智能体强化学习算法都遵循的马尔可夫假设,使每个智能体在学习过程中都有可能会陷入由其他智能体所创建的环境而导致无终止的循环。为解决上述问题,研究了中心式训练分布式执行（CTDE）架构在强化学习中的实现方法,并分别从智能体间通信和智能体探索这两个角度入手,采用通过方差控制的强化学习算法（VBC）并引入好奇心机制来改进QMIX算法。通过星际争霸Ⅱ学习环境（SC2LE）中的微操场景对所提算法加以验证。实验结果表明,与QMIX算法相比,所提算法的性能有所提升,并且能够得到收敛速度更快的训练模型。     

Invariant-Driven Strategies for Maude

We propose generic invariant-driven strategies that control the execution of systems by guaranteeing that the given invariants are satisfied. Our strategies are generic in the sense that they are parameterized by the system whose execution they control, by the logic in which the invariants are expressed, and by the invariants themselves. We illustrate the use of the strategies in the case of invariants expressed in propositional logic. However, the good properties of Maude as a logical and semantic framework, in which many different logics and formalisms can be expressed and executed allow us to use other logics as parameter of our strategies.     

OpenCL的动态执行模式在静态编译支持下的实现

OpenCL的动态执行模式要求底层平台支持device文件的动态生成、编译和加载运行。对于不具备这些特性的平台,必须从软件层面考虑支持方法。通过采用函数更名技术解决同名函数正确识别问题,基于动态执行流的predo策略可以在静态编译环境下实现OpenCL的动态执行模式。     

Execution levels for aspect-oriented programming: Design,semantics, implementations and applications

In aspect-oriented programming (AOP) languages, advice evaluation is usually considered as part of the base program evaluation. This is also the case for certain pointcuts, such as if pointcuts in AspectJ, or simply all pointcuts in higher-order aspect languages like AspectScheme. While viewing aspects as part of base level computation clearly distinguishes AOP from reflection, it also comes at a price: because aspects observe base level computation, evaluating pointcuts and advice at the base level can trigger infinite regression. To avoid these pitfalls, aspect languages propose ad-hoc mechanisms, which increase the complexity for programmers while being insufficient in many cases. After shedding light on the many facets of the issue, this paper proposes to clarify the situation by introducing levels of execution in the programming language, thereby allowing aspects to observe and run at specific, possibly different, levels. We adopt a defensive default that avoids infinite regression, and gives advanced programmers the means to override this default using level-shifting operators. We then study execution levels both in practice and in theory. First, we study the relevance of the issues addressed by execution levels in existing aspect-oriented programs. We then formalize the semantics of execution levels and prove that the default semantics is indeed free of a certain form of infinite regression, which we call aspect loops. Finally, we report on existing implementations of execution levels for aspect-oriented extensions of Scheme, JavaScript and Java, discussing their implementation techniques and current applications.     

决策树模型预测Spark SQL作业执行时间的方法

Spark SQL在超大规模集群和数据集上存在易用性问题,如Catalyst最优执行计划的选择,Shuffle Partition的配置对性能有较大的影响,数据倾斜往往导致集群性能变差。为了在作业执行之前准确预测执行时间,更加充分地使用运行时数据,选择最优执行计划,提出通过决策树及其组合算法的回归模型预测作业执行时间的方法。采用交叉验证方法优化模型超参数,通过剪枝和组合算法优化过度拟合问题,选择相关指标评估机器学习模型预测的准确性。实验表明,梯度提升树回归模型预测作业执行时间的R 2超过0.8,且能够满足在线预测的实时性要求,模型评估指标达到预期效果,相对于线性回归模型的评估指标具有一定的优势。     

赋时着色Petri网在建模仿真中的应用

赋时着色Petri网(TCPN)在着色Petri网(CPN)基础上引入时间概念,更适于复杂系统建模仿真.其库所赋时实施规则同CPN,变迁赋时实施规则将令牌移入变迁的输出库所.基于TCPN优先权服务排队系统,用CPN的元语言实现优先权排队,由于时间引入,可真实模拟顾客到达时间和服务台服务时间.