空分装置中空压机控制系统的原理与应用

本文详细介绍了空压机的基本控制、不同工况的控制过程、防喘振控制及对电机的控制,进行了控制模式的探讨,分析了空压机运行调试过程中的问题。     

利用H∞范数的最优异步自学习控制频域方法

利用H∞范数对异步自学习控制系统进行了深入的研究.基于用H∞语言重新改写的问 题提法,给出了四个引理与一个定理的严格证明,得到了渐近稳定时控制器存在的一个充分条 件,并给出了计算实例.     

基于内模控制的液位控制系统的研究

内模控制以其设计简单、跟踪调节性能好、鲁棒性强、能消除不可测干扰的影响等特点而得到了广泛的应用、但是,将内模控制应用于实际场合时,必须考虑到执行机构动作范围的限制本提出了一种处理控制器受限问题的方法,实验表明,获得了良好的控制效果。     

基于PLC的绕线系统控制方法研究

针对传统绕线机存在张力控制稳定性差问题,对收线主轴运用了矢量控制,并详细地分析了设定的速度值和给定变频器频率之间的关系.利用配重模块,将压力值转换为位移量,在稳定运行的基础上,结合实时反馈位移量,利用PID和bang-bang结合的方式设计了放线跟随控制系统,并对调试过程中PID的校准方法进行了分析.经实验验证,结果表明,随着系统运行中放线线轮和收线线轮直径比的不断变化,PID与bang-bang结合的控制方法的控制效果优于仅PID控制时的控制效果.     

基于自适应广义预测控制的船舶航向保持

提出了基于自适应广义预测控制的船舶航向保持算法,将控制算法应用到船舶航向控制系统,仿真结果证实了算法的有效性.     

位置PID控制在望远镜控制系统中的应用

本文分析了位置控制在望远镜控制系统中的应用。并详细介绍了位置PID控制在望远镜控制系统中的设计方法与步骤。实验表明采用位置PID控制方法可以改善望远镜的控制性能,达到更高的控制精度。     

永磁同步电机优化控制系统建模与仿真

为了降低永磁同步电机本身的非线性及参数的不确定性对整个伺服控制系统的干扰,优化永磁同步电机控制系统.实现综合在线预测转速、反馈输出转速和参考转速求取下一时刻最优的控制电流增量,通过实时监控转速变化消除系统的不确定扰动.用电机的AR模型为内部模型,对传统模型中的系数进行优化,并且只选择未来某一时刻的预测值来计算最优控制律,简化了计算过程从而克服了传统模型算法控制需要选择较长的采样周期才能计算出合适的控制量,能很好地满足动态较快的电动机实时性要求.对系统的稳定性和鲁棒性进行了仿真和实验分析,表明在模型失配时系统稳定,并且增强了鲁棒性.     

鲁棒低增益变结构模型参考自适应控制

就对象含未建模动态且其建模部分相对阶大于1时的变结构模型参考自适应控制 (VS-MRAC)系统,提出了一种鲁棒低增益变结构控制方案,特点是系统变结构律在低增益状态 下仍可保证所有辅助误差均在有限时间内收敛到零,从而提高了系统跟踪精度.此外,还研究了 平均值滤波器对系统稳定性的影响.     

铝材冷轧机自动厚度优化控制系统

针对铝材冷轧机板厚控制系统是一个具有非线性、延时、多变量耦合的复杂的实时系统,给出了一种适用于不同类型轧机的自动厚度优化控制系统。该系统采用各种厚度控制系统(AGC)控制、优化控制和自适应控制方法,对轧机系统进行优化精确控制,既保证了产品的质量,又可提高轧机的生产效率。试验证明,该优化控制系统实时性强,控制响应时间小于100ms,铝带出口厚度偏差可控制在2%以内,保证了产品质量;同时提高轧机的平均运行速度10%以上,提高了轧机的生产效率。     

网络拥塞控制算法综述

随着计算机网络的持续快速发展,各种网络应用需求不断涌现,造成网络数据流量的激增。网络拥塞问题变得越来越严重,网络拥塞控制也一直是网络研究的最关键热点问题之一。在本文中,作者着重阐述了TCP拥塞控制和IP拥塞控制中的典型算法以及目前一些较有影响的拥塞控制算法,并指出了这些算法的优缺点。最后分析了当前拥塞控制算法设计过程中存在的不足,并给出了一个有意义的研究方向。     

A comprehensive survey and future trend of simulation study on FMS scheduling

Since the late 1970s when the first collection of papers on scheduling of flexible manufacturing systems (FMSs) has been published, it has been one of the most popular topics for researchers. A number of approaches have been delivered to schedule FMSs including simulation techniques and analytical methods, whereas the former is the most widely used tool for modeling FMSs. The objective of this paper is to review scheduling study on FMSs and analyse future trend that employed simulation techniques as the analyzing tool. Scheduling methodologies are categorized into, namely traditional simulation techniques with single criterion scheduling approaches, traditional simulation techniques with multi-criteria scheduling approaches, and artificial intelligence (AI) approaches in FMSs. It is concluded that AI approaches will be dominating in future study.     

Annotating korean text documents with linked data resources

Semantic annotation approaches link entities from a knowledge base to mentions of entities in text to provide additional content-related information. Recently increasing use of resources from the Linked Open Data (LOD) Cloud has been made to annotate text documents thanks to the network of machine-understandable, interlinked data. While existing approaches to semantic annotation in the LOD context have been proven to be well performing with the English language, many other languages in general and the Korean language in particular are still underrepresented. We investigate the applicability of existing semantic annotation approaches to the Korean language by adapting two popular approaches in the semantic annotation field and evaluating those approaches on an English-Korean bilingual sense-tagged corpus. Further, general challenges in internationalization of annotation approaches are summarized.     

Context-aware broadcasting approaches in mobile ad hoc networks

The aim of this paper is to compare different context-aware broadcasting approaches in mobile ad hoc networks (MANETs) and to evaluate their respective performances. Message broadcasting is one of the core challenges brought up by distributed systems and has therefore largely been studied in the context of traditional network structures, such as the Internet. With the emergence of MANETs, new broadcasting algorithms especially geared at these networks have been introduced. The goal of these broadcasting algorithms is to ensure that a maximum number of nodes deliver the broadcasted message (reliability), while ensuring that the minimum number of nodes retransmit the broadcasted message (efficiency), in order to save their resources, such as bandwidth or battery. In recent years, as more and more mobile devices have become context-aware, several broadcasting algorithms have been introduced that take advantage of contextual information in order to improve their performance. We distinguish four approaches with respect to context: (1) context-oblivious approaches, (2) network traffic-aware approaches, (3) power-aware approaches, and (4) location-aware approaches. This paper precisely aims at presenting these four different broadcasting approaches and at measuring the performance of algorithms built upon them.     

Hybrid learning models to get the interpretability–accuracy trade-off in fuzzy modeling

One of the problems associated to linguistic fuzzy modeling is its lack of accuracy when modeling some complex systems. To overcome this problem, many different possibilities of improving the accuracy of linguistic fuzzy modeling have been considered in the specialized literature. We will call these approaches as basic refinement approaches. In this work, we present a short study of how these basic approaches can be combined to obtain new hybrid approaches presenting a better trade-off between interpretability and accuracy. As an example of application of these kinds of systems, we analyze seven hybrid approaches to develop accurate and still interpretable fuzzy rule-based systems, which will be tested considering two real-world problems.This work has been supported by the spanish cicyt project tic2002-04036-c05-01 (keel).     

A survey on tree matching and XML retrieval

With the increasing number of available XML documents, numerous approaches for retrieval have been proposed in the literature. They usually use the tree representation of documents and queries to process them, whether in an implicit or explicit way. Although retrieving XML documents can be considered as a tree matching problem between the query tree and the document trees, only a few approaches take advantage of the algorithms and methods proposed by the graph theory. In this paper, we aim at studying the theoretical approaches proposed in the literature for tree matching and at seeing how these approaches have been adapted to XML querying and retrieval, from both an exact and an approximate matching perspective. This study will allow us to highlight theoretical aspects of graph theory that have not been yet explored in XML retrieval.     

Soft computing-based approaches to predict energy consumption and stability margin of six-legged robots moving on gradient terrains

Soft computing-based approaches have been developed to predict specific energy consumption and stability margin of a six-legged robot ascending and descending some gradient terrains. Three different neuro-fuzzy and one neural network-based approaches have been developed. The performances of these approaches are compared among themselves, through computer simulations. Genetic algorithm-tuned multiple adaptive neuro-fuzzy inference system is found to perform better than other three approaches for predicting both the outputs. This could be due to a more exhaustive search carried out by the genetic algorithm in comparison with back-propagation algorithm and the use of two separate adaptive neuro-fuzzy inference systems for two different outputs. A designer may use the developed soft computing-based approaches in order to predict specific energy consumption and stability margin of the robot for a set of input parameters, beforehand.     

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes metaheuristic optimisation algorithms (also known as nature inspired algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these, as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed.     

Software approaches for resilience of high performance computing systems: a survey

With the scaling up of high-performance computing systems in recent years, their reliability has been descending continuously. Therefore, system resilience has been regarded as one of the critical challenges for large-scale HPC systems. Various techniques and systems have been proposed to ensure the correct execution and completion of parallel programs. This paper provides a comprehensive survey of existing software resilience approaches. Firstly, a classification of software resilience approaches is presented; then we introduce major approaches and techniques, including checkpointing, replication, soft error resilience, algorithm-based fault tolerance, fault detection and prediction. In addition, challenges exposed by system-scale and heterogeneous architecture are also discussed.     

Estimation of optimal feet forces and joint torques for on-line control of six-legged robot

The present study aims to estimate optimal feet forces and joint torques necessary for real-time control of a six-legged robot. Two approaches have been developed, such as minimization of norm of feet forces and minimization of norm of joint torques using the least squared method. Results of these two approaches have been compared with each other, and with those of available literature. As both of these approaches are found to be computationally fast, these are suitable for real-time control of the six-legged robot.     

A comparison of approaches to learning task selection in the training of complex cognitive skills

This paper presents a comparison of learning task selection approaches that have been used throughout the last three decades in the training of complex cognitive skills. In general, a development from static part-task selection to dynamic whole-task selection can be noticed. The four approaches of static part-task approaches, static whole-task approaches, dynamic part-task approaches, and dynamic whole-task approaches are identified and compared in terms of their flexibility and adaptability to the needs of the individual trainee during training. The comparison shows that dynamic whole-task approaches are the most flexible and adaptive. For each approach it is discussed to what complex cognitive skills they might be useful training methods.