Copolymers of acrylonitrile with some furans and their Diels-Alder adducts

Copolymers of acrylonitrile with furan, 2-methylfuran and with 2,5-dimethyl furan have been prepared by free radical copolymerizations, and their ¹³C spectra examined for evidence of the three types of structure that formed, according to previous studies. We have characterized the Diels-Alder adducts of acrylonitrile with furan and with 2-methylfuran, and have found that they may also copolymerize with acrylonitrile by addition at their double bonds. The enchained adducts were most readily recognized in the ¹H spectrum by shifts at 4.8 and 4.6 ppm respectively from protons at the oxygen bridgehead. The ¹³C spectra of these various polymers have been assigned with the aid of model compounds. Below δ=60 ppm the nitrile shift was dispersed over a larger range of shifts than is found in PAN itself, and was joined by the shifts of unsaturated carbon atoms and of carbon atoms linked to the oxygen atoms within these structures. Above 60 ppm the shifts of carbons in acrylonitrile units were found to be perturbed systematically from their normal places by the presence of neighbouring structures formed from the different furan-containing molecules. In both the ¹H and ¹³C spectra shifts characteristic of the end groups from the AIBN initiator were noted. Fine structure in the spectra was attributed to monomer sequence effects and to the tactic structure of the polymer. Chiral centres appeared to be created from the furan rings at random, at least when protonated carbons were the site of initial attachment, but the mode of addition to the ring—whether cis, trans or both, was not certain. Assignments were made of bands in the i.r. spectra to modes of vibration of the units formed from the furan monomers.     

Global optimization for neural network training

We propose a novel global minimization method, called NOVEL (Nonlinear Optimization via External Lead), and demonstrate its superior performance on neural network learning problems. The goal is improved learning of application problems that achieves either smaller networks or less error prone networks of the same size. This training method combines global and local searches to find a good local minimum. In benchmark comparisons against the best global optimization algorithms, it demonstrates superior performance improvement     

Addition of Methyl Radical to Substituted Alkenes: A Theoretical Study of the Reaction Mechanism

Ab initio calculations at the QCISD(T)/6–311G^** level have been carried out to study the addition of the methyl radical to a series of substituted alkenes, and the results analyzed with the aid of the curve-crossing model. It is found that (a) reaction exothermicity is the main factor that dominates reactivity, (b) polar contributions to the transition states are generally small and of minor energetic consequences, and (c) the general observation that π-electron-accepting substituents in the alkene enhance reactivity is a secondary correlation that is a consequence of the effect of these substituents on reaction exothermicity. There is no evidence for the prevalent view that the methyl radical is generally nucleophilic towards alkenes.     

Simple adaptive vectors encoding for two-dimensional redundancy reduction (saver)

An efficient and practical source encoding algorithm for compressing binary facsimile data is presented. The vertical correlation is taken into account by forming vectors and the horizontal correlation is considered by the run-length coding of these vectors. New proposals for an end-of-line codeword and error correction are described.     

Partially loaded elastic plates

A general procedure for the analysis of elastic plates under partial loading is described. The method applies to any elastic plate with rectilinear boundaries. Loads distributed over a circular region and a rectangular region are investigated.     

Using Informative Score for Instance Selection Strategy in Semi-Supervised Sentiment Classification

Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service. Existing studies heavily rely on sentiment classification methods that require fully annotated inputs. However, there is limited labelled text available, making the acquirement process of the fully annotated input costly and labour-intensive. Lately, semi-supervised methods emerge as they require only partially labelled input but perform comparably to supervised methods. Nevertheless, some works reported that the performance of the semi-supervised model degraded after adding unlabelled instances into training. Literature also shows that not all unlabelled instances are equally useful; thus identifying the informative unlabelled instances is beneficial in training a semi-supervised model. To achieve this, an informative score is proposed and incorporated into semi-supervised sentiment classification. The evaluation is performed on a semi-supervised method without an informative score and with an informative score. By using the informative score in the instance selection strategy to identify informative unlabelled instances, semi-supervised models perform better compared to models that do not incorporate informative scores into their training. Although the performance of semi-supervised models incorporated with an informative score is not able to surpass the supervised models, the results are still found promising as the differences in performance are subtle with a small difference of 2% to 5%, but the number of labelled instances used is greatly reduced from 100% to 40%. The best finding of the proposed instance selection strategy is achieved when incorporating an informative score with a baseline confidence score at a 0.5:0.5 ratio using only 40% labelled data.     

SECOND ORDER SLIDING MODE CONTROL OF A DIESEL ENGINE

A 2nd order sliding mode algorithm is applied to control the speed of a diesel power generator set. Tuning guidelines are given. The robustness of the controller is investigated and the performance of the 2nd order sliding mode controller is compared with that obtained by a commercial diesel engine controller and a classical proportional‐integral (PI) controller. Robustness to unmodelled dynamics is discussed and implementation results presented.     

Control of nonlinear systems with time-varying output constraints

This paper presents control design for strict feedback nonlinear systems with time-varying output constraints. An asymmetric time-varying Barrier Lyapunov Function (BLF) is employed to ensure constraint satisfaction. By allowing the barriers to vary with the desired trajectory in time, the initial condition requirements are relaxed. Through a change of tracking error coordinates, we eliminate the explicit dependence of the BLF on time, thereby simplifying the analysis of constraint satisfaction. We show that asymptotic output tracking is achieved without violation of the output constraint, and also quantify the transient performance bound as a function of time that converges to zero. To handle parametric model uncertainty, we present an adaptive controller that ensures constraint satisfaction during the transient phase of online parameter adaptation. The performance of the proposed control is illustrated through a simulation example.     

Universal Approximation and QoS Violation Application of Extreme Learning Machine

Neural networks have been successfully applied to many applications due to their approximation capability. However, complicated network structures and algorithms will lead to computational and time-consuming burdens. In order to satisfy demanding real-time requirements, many fast learning algorithms were explored in the past. Recently, a fast algorithm, Extreme Learning Machine (ELM) (Huang et al. 70:489–501, 2006) was proposed. Unlike conventional algorithms whose neurons need to be tuned, the input-to-hidden neurons of ELM are randomly generated. Though a large number of experimental results have shown that input-to-hidden neurons need not be tuned, there lacks a rigorous proof whether ELM possesses the universal approximation capability. In this paper, based on the universal approximation property of an orthonormal method, we firstly illustrate the equivalent relationship between ELM and the orthonormal method, and further prove that neural networks with ELM are also universal approximations. We also successfully apply ELM to the identification of QoS violation in the multimedia transmission.