灰色系统建模理论在机床主轴疲劳寿命设计中的应用研究

运用灰色系统的理论及方法，将疲劳应力与寿命的相互制约关系看成一个灰色系统，将疲劳试验的应力水平均匀分成数级，并找出与之对应的安全寿命的白化值。通过对安全寿命值数列的一次累加，生成一个光滑离散函数。据此建立灰色系统模型，并求解灰色系统的微分方程 。通过残差模型的修正，提高模型精度，再运用灰色系统模型的还原模型，可计算出任一应力水平下的安全寿命值。为机床主轴的安全寿命计算提供了新的途径，具有较大的推广应用价值。     

机床圆柱结合部的动态特性解析方法

在机床图纸设计阶段，预测机床整机的动态特性具有十分重要的意义，进行机床整机动态特性预测的难点在于如何确定结合部的刚度和阻尼参数，本文以机床结构中应用较普遍的圆柱结合部为例，研究了基于结合面基础特性参数的圆柱结合部动态特性解析方法，并编制相应解析软件，给出应用实例，证明本文的方法及软件是正确的。     

机床电力拖动脉冲宽度调制技术的研究

本文重点介绍用于机床电力拖动系统中的电机脉冲调制(PWM)技术，在分析现有脉冲宽度调制技术的基础上，提出一种采用优化技术生成PWM调制波的方法，推导了优化PWM调制波的求解过程，给出了其计算公式。     

有限状态机在数控系统软件中的应用研究

在数控系统软件开发中，控制软件的开发是开发的关键。针对数控系统复杂性和实时性特点，把有限状态机应用在数控系统软件开发中，构造了控制器软件结构，并应用于数控系统的原型开发。     

压铸件全自动生产系统的设计与控制

为了满足国际客户对压铸件批量、品质及压铸工艺统计过程控制的要求,使用工业机器人替代程控压铸机取件、喷淋、吹干机械手;增设剪口模用液压机;机器人控制压铸机、液压机及压铸循环,实现压铸件全自动生产。概述了压铸件全自动生产系统的设计和控制方法,并指出了自动压铸工艺的局限性。     

Advanced experimental and numerical methods for the analysis of the dynamic forming press behavior

Numerical simulations are used to estimate the dynamic behavior of new concepts of forming presses as well as to improve the dynamic properties of existing machines. These simulations are to be parametrized with appropriate values in order to yield most accurate results. In many cases neither the way of building up the simulation model for the specific problem nor an appropriate method for gathering the needed date are given in advance. Regarding these facts this paper intends both to characterize the way of building up complex simulation models by using basic simulation modules and to describe methods for the identification of the needed parameters that work even under conditions that hitherto made parameter identifications impossible or at least very time-consuming. In this context recent developments in simulating press dynamics will also be characterized as new modal analysis methods that are not yet established in the field of forming technique. The research activities were financed by the European Research Association for Sheet Metal Working.     

Adaptive receding horizon control for constrained MIMO systems

An adaptive control algorithm for open-loop stable, constrained, linear, multiple input multiple output systems is presented. The proposed approach can deal with both input and output constraints, as well as measurement noise and output disturbances. The adaptive controller consists of an iterative set membership identification algorithm, that provides a set of candidate plant models at each time step, and a model predictive controller, that enforces input and output constraints for all the plants inside the model set. The algorithm relies only on the solution of standard convex optimization problems that are guaranteed to be recursively feasible. The experimental results obtained by applying the proposed controller to a quad-tank testbed are presented.     

Reactive Search strategies using Reinforcement Learning,local search algorithms and Variable Neighborhood Search

Optimization techniques known as metaheuristics have been applied successfully to solve different problems, in which their development is characterized by the appropriate selection of parameters (values) for its execution. Where the adjustment of a parameter is required, this parameter will be tested until viable results are obtained. Normally, such adjustments are made by the developer deploying the metaheuristic. The quality of the results of a test instance [The term instance is used to refer to the assignment of values to the input variables of a problem.] will not be transferred to the instances that were not tested yet and its feedback may require a slow process of “trial and error” where the algorithm has to be adjusted for a specific application. Within this context of metaheuristics the Reactive Search emerged defending the integration of machine learning within heuristic searches for solving complex optimization problems. Based in the integration that the Reactive Search proposes between machine learning and metaheuristics, emerged the idea of putting Reinforcement Learning, more specifically the Q-learning algorithm with a reactive behavior, to select which local search is the most appropriate in a given time of a search, to succeed another local search that can not improve the current solution in the VNS metaheuristic. In this work we propose a reactive implementation using Reinforcement Learning for the self-tuning of the implemented algorithm, applied to the Symmetric Travelling Salesman Problem.     

Ensemble of multiple instance classifiers for image re-ranking

Text-based image retrieval may perform poorly due to the irrelevant and/or incomplete text surrounding the images in the web pages. In such situations, visual content of the images can be leveraged to improve the image ranking performance. In this paper, we look into this problem of image re-ranking and propose a system that automatically constructs multiple candidate “multi-instance bags (MI-bags)”, which are likely to contain relevant images. These automatically constructed bags are then utilized by ensembles of Multiple Instance Learning (MIL) classifiers and the images are re-ranked according to the final classification responses. Our method is unsupervised in the sense that, the only input to the system is the text query itself, without any user feedback or annotation. The experimental results demonstrate that constructing multiple instance bags based on the retrieval order and utilizing ensembles of MIL classifiers greatly enhance the retrieval performance, achieving on par or better results compared to the state-of-the-art.     

Exploring teachers' perceptions on different CSCL script editing tools

Despite the apparent maturity of the learning design field, and the variety of tooling available to support it, adoption among the teacher community (one of its alleged main targets) is still low. There is a lack of research on teachers' perception and use of different technological learning design tools, as existing evaluations are often restricted to a single tool. In order to explore whether there are common factors hampering teacher adoption, and which tool features might appeal to different teachers, more studies involving multiple authoring tools are needed. This paper provides a first step in this direction, describing a mixed methods study performed around a professional development workshop with 18 university teachers from multiple disciplines. This workshop exposed teachers to two different authoring tools (WebCollage and EDIT2), as they learned to create computer-supported collaborative learning (CSCL) designs and implement them. The findings of our interpretive study (which included questionnaires, observations, or group discussion recordings) support the idea that there is no single tool or set of features that are globally perceived as better, although our evidence also highlights certain factors as important for participant teachers – amongst others, the integration of learning designs with the ICT platforms for enactment, as well as with other tools that they already use in their everyday practice.