The compatibility of poly(methyl methacrylate) and chlorinated polyethylene

The compatibility of poly(methyl methacrylate) with chlorinated polyethylene was studied using differential thermal analysis and dynamic mechanical measurements. For around 50% chlorination of polyethylene they were found to be compatible and show an LCST, but not at 27% chlorination as had previously been thought. The previous results were explained as due to a matching of refractive indices over some temperatures. The compatibility was explained by the heat of mixing which becomes favourable at chlorination levels above approximately 46%.     

Thermodynamics of oligomeric binary mixtures of polyethylene glycol and polypropylene glycol methylethers

Measurements are reported of the heats of mixing and cloud point curves of binary mixtures of methoxylated polyethylene glycol with methoxylated polypropylene glycol. The data are discussed in terms of the Flory equation of state theory and it is shown that both a temperature and concentration dependent X₁₂ parameter and a Q₁₂ term are required.     

QoS-based Task Group Deployment on Grid by Learning the Performance Data

Overhead of executing fine-grain tasks on computational grids led to task group or batch deployment in which a batch is resized according to the characteristics of the tasks, designated resource, and the interconnecting network. An economic grid demands an application to be processed within the given budget and deadline, referred to as the quality of service (QoS) requirements. In this paper, we increase the task success rate in an economic grid by optimally mapping the tasks to the resources prior to the batch deployment. The task-resource mapping (Advance QoS Planning) is decided based on QoS requirement and by mining the historical performance data of the application tasks using a genetic algorithm. The mapping is then used to assist in creating the task groups. Practical experiments are conducted to validate the proposed method and suggestions are given to implement our method in a cloud environment as well as to process real-time tasks.     

基于D-S论据理论的网络安全态势预测方法研究

随着信息技术的不断进步,网络安全问题日益突出,网络安全态势便在此基础上产生并逐渐成为研究者研究的重点,D-S论据理论的网络安全态势预测方法是为了获取网络在未来一段时间内的安全态势状况,构建D-S论据理论的网络安全态势预测模型能够将原有的理论和当前网络安全态势相融合,从而进一步降低网络信息的风险性,从而提高网络的监控能力,给人们的生活提供信息安全保障。本文将主要研究基于D-S论据理论的网络安全态势预测方法。     

An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation

This paper presents an adaptive spatial information-theoretic fuzzy clustering algorithm to improve the robustness of the conventional fuzzy c-means (FCM) clustering algorithms for image segmentation. This is achieved through the incorporation of information-theoretic framework into the FCM-type algorithms. By combining these two concepts and modifying the objective function of the FCM algorithm, we are able to solve the problems of sensitivity to noisy data and the lack of spatial information, and improve the image segmentation results. The experimental results have shown that this robust clustering algorithm is useful for MRI brain image segmentation and it yields better segmentation results when compared to the conventional FCM approach.     

Quantized Neural Modeling: Hybrid Quantized Architecture in Elman Networks

This paper presents a novel neural network model with hybrid quantized architecture to improve the performance of the conventional Elman networks. The quantum gate technique is introduced for solving the pattern mismatch between the inputs stream and one-time-delay state feedback. A quantized back-propagation training algorithm with an adaptive dead zone scheme is developed for providing an optimal or suboptimal tradeoff between the convergence speed and the generalization performance. Furthermore, the effectiveness of the new real time learning algorithm is demonstrated by proving the quantum gate parameter convergence based on Lyapunov method. The numerical experiments are carried out to demonstrate the accuracy of the theoretical results.     

Modeling primary school pre-service teachers’ Technological Pedagogical Content Knowledge (TPACK) for meaningful learning with information and communication technology (ICT)

Within the field of educational technology, Technological Pedagogical Content Knowledge (TPACK) has been theorized as a seven-factor construct to describe teacher’s integration of information and communication technology (ICT) in their teaching. However, this framework has yet to be successfully validated through survey instruments. This paper examines the construct validity of a TPACK survey that was contextualized for the pedagogical approaches employed in a 12-week ICT course designed with reference to the TPACK framework for Singaporean primary school pre-service teachers. Using this framework, the researchers were able to uncover five of the seven TPACK constructs which were a better model fit as compared with several extant studies of TPACK surveys. Using these results, pre and post-course structural equation models were constructed to explain the relationships amongst the different constructs of teachers’ TPACK perceptions. It was found that pedagogical knowledge had a direct impact on TPACK at the beginning of the course. As teachers made connections between their technological knowledge and pedagogical knowledge to form technological pedagogical knowledge during the course, the direct relation between pedagogical knowledge and TPACK became insignificant where as the relations between pedagogical knowledge and technological pedagogical knowledge, and technological pedagogical knowledge and TPACK were strengthened. The comparison between the pre and post-course models also revealed that the pre-service teachers’ perceived relations between content knowledge and TPACK changes from insignificant to significant. The implications of these findings and suggestions to improve the construct validation of the TPACK framework are discussed in this paper.     

Hybrid intelligent control for optimal operation of shaft furnace roasting process

A hybrid intelligent control method is proposed to control the technical indices into their desired range. This is realized by on-line adjusting the set-points of control loops for optimal operation of the shaft furnace in response to changes in operating points. The controller consists of six modules, namely a pre-setting model for control loop set-points, a predictive model for technical index, a feedforward compensator using predictive model, a feedback compensator, a fault working situation diagnosis and a fault tolerant control model. The proposed approach has been successfully applied to the roasting process of shaft furnace in a mineral processing plant in China and its efficiency has been verified by the practical application results.     

An extreme learning machine approach for speaker recognition

Over the last two decades, automatic speaker recognition has been an interesting and challenging problem to speech researchers. It can be classified into two different categories, speaker identification and speaker verification. In this paper, a new classifier, extreme learning machine, is examined on the text-independent speaker verification task and compared with SVM classifier. Extreme learning machine (ELM) classifiers have been proposed for generalized single hidden layer feedforward networks with a wide variety of hidden nodes. They are extremely fast in learning and perform well on many artificial and real regression and classification applications. The database used to evaluate the ELM and SVM classifiers is ELSDSR corpus, and the Mel-frequency Cepstral Coefficients were extracted and used as the input to the classifiers. Empirical studies have shown that ELM classifiers and its variants could perform better than SVM classifiers on the dataset provided with less training time.     

Model predictive control of a permanent magnet synchronous motor with experimental validation

This paper develops new results on the use of model predictive control to regulate the speed of a permanent magnet synchronous motor where the design is based on a linearized state-space representation of the nonlinear model that describes the dynamics. The model predictive controller employs integral action, resulting in zero steady-state error and load torque disturbance rejection. Design in the presence of constraints is considered, where a quadratic programming procedure is used to solve the resulting on-line optimization problem during experimental verification by application to an industry-sized motor.