Adaptive fuzzy control for nontriangular form systems with time-varying full-state constraints

This article investigates an adaptive fuzzy tracking control problem for a class of nontriangular form systems with asymmetric time-varying full state constraints. Unknown functions are approximated by the fuzzy logic systems. A domination approach is employed to tackle the nontriangular form structure. Time-varying asymmetric barrier Lyapunov functions (ABLFs) are adopted to ensure full-state constraints satisfaction. Based on the backstepping technique and time-varying ABLFs, an adaptive controller is proposed and guarantees that all the signals in the closed-loop system are ultimately bounded and the time-varying full state constraints are met. Simulation examples are presented to further demonstrate the effectiveness of the proposed approach.     

HFC电缆分配系统的建设与思考

结合沧州市网络的实际情况，主要分析HFC电缆分配系统双向传输改造的建设标准和实施方案。     

LDPE薄膜雾度和透光率测试的影响因素

本文研究了制样方法、试样划伤、表面污染以及试样厚度等因素对LDPE薄膜透光率和浊度的影响。     

地下输水管网的腐蚀调查

介绍了中国石油天然气股份有限公司大庆石化分公司地下输水管网的腐蚀现状与调查分析情况,提出了对地下输水管网实施以深井阳极地床外加电流阴极保护为主、局部欠保护区辅以镁合金牺牲阳极的阴极保护方案。经分析,采取上述防护措施后,每年可产生2488万元的经济效益。     

甲磺酸铜催化合成草酸二异戊酯

制备了甲磺酸铜催化剂,对其结构进行了IR和热重分析,并用于催化草酸和异戊醇的酯化反应。结果表明,甲磺酸铜具有催化活性高、稳定,易分离,重复使用性能优良,对环境友好的特点。最佳酯化工艺条件为:n(异戊醇):n(草酸)=2．8:1,w(甲磺酸铜)=0．3％,以过量的异戊醇为带水剂,回流反应2．0 h,酯化率可达到96．8％。     

Applications and Technologies of All-Solid State Blue Laser

1 Introduction Along with the matureness of laser diode (LD) manufacturing technology, the performance of LD has been improved greatly since 1980s, so various kinds of laser devices based on LD have been developed rapidly, especially the all-solid state lasers. After early experiments and researches, the all-solid state lasers have been commercialized successfully.     

焊膏印刷工艺参数的选择与优化

表面贴装工艺流程的关键工序之一就是焊膏印刷,其工艺控制的好坏直接影响着装配的线路板的质量。通过对印刷工艺参数的分析,利用试验设计的方法来找出关键工艺参数,并加以优化设定,从而为控制工序参数奠定基础。     

结合RDF实现对XML文档的RBAC

文章提出了一种面向XML文档的基于XMLschema并结合RDF的访问控制模型,它实现了对XML文档的细粒度的安全访问控制,同时提供了对XML文档中associationsecurityobject的安全访问控制。     

ADN氧化剂的合成及其在推进剂中的应用

综述了ADN的合成、性质及其在推进剂中的应用，认为球形ADN具有能量高、感度低、特征信号低，对环境无污染等优点，将其作为氧化剂在高能低特征信号推进剂中有着广阔的应用前景。     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto