A learning style classification mechanism for e-learning

With the growing demand in e-learning, numerous research works have been done to enhance teaching quality in e-learning environments. Among these studies, researchers have indicated that adaptive learning is a critical requirement for promoting the learning performance of students. Adaptive learning provides adaptive learning materials, learning strategies and/or courses according to a student’s learning style. Hence, the first step for achieving adaptive learning environments is to identify students’ learning styles. This paper proposes a learning style classification mechanism to classify and then identify students’ learning styles. The proposed mechanism improves k-nearest neighbor (k-NN) classification and combines it with genetic algorithms (GA). To demonstrate the viability of the proposed mechanism, the proposed mechanism is implemented on an open-learning management system. The learning behavioral features of 117 elementary school students are collected and then classified by the proposed mechanism. The experimental results indicate that the proposed classification mechanism can effectively classify and identify students’ learning styles.     

一种复合故障预测动态建模方法

本文提出一种复合故障预测动态建模方法,将原有故障数据样本经过参数相关性分析,按特征输入支持向量回归机建立相应的故障预测模型,可以很有效预测未来较长时间的数据变化.对航空发动机的飞行参数进行预测的结果表明,该方法的预测精度优于现有的支持向量回归方法.     

A dynamic probabilistic material flow modeling method

Material flow modeling constitutes an important approach to predicting and understanding the flows of materials through the anthroposphere into the environment. The new “Dynamic Probabilistic Material Flow Analysis (DPMFA)” method, combining dynamic material flow modeling with probabilistic modeling, is presented in this paper. Material transfers that lead to particular environmental stocks are represented as systems of mass-balanced flows. The time-dynamic behavior of the system is calculated by adding up the flows over several consecutive periods, considering changes in the inflow to the system and intermediate delays in local stocks. Incomplete parameter knowledge is represented and propagated using Bayesian modeling. The method is implemented as a simulation framework in Python to support experts from different domains in the development of their application models. After the introduction of the method and its implementation, a case study is presented in which the framework is applied to predict the environmental concentrations of carbon nanotubes in Switzerland.     

Neural critic learning toward robust dynamic stabilization

In this article, we focus on developing a neural‐network‐based critic learning strategy toward robust dynamic stabilization for a class of uncertain nonlinear systems. A type of general uncertainties involved both in the internal dynamics and in the input matrix is considered. An auxiliary system with actual action and auxiliary signal is constructed after dynamics decomposition and combination for the original plant. The reasonability of the control problem transformation from robust stabilization to optimal feedback design is also provided theoretically. After that, the adaptive critic learning method based on a neural network is established to derive the approximate optimal solution of the transformed control problem. The critic weight can be initialized to a zero vector, which apparently facilitates the learning process. Numerical simulation is finally presented to illustrate the effectiveness of the critic learning approach for neural robust stabilization.     

A cooperative radial basis function method for variable-fidelity surrogate modeling

By coupling the low-fidelity (LF) model with the high-fidelity (HF) samples, the variable-fidelity model (VFM) offers an efficient way to overcome the expensive computing challenge in multidisciplinary design optimization (MDO). In this paper, a cooperative radial basis function (Co-RBF) method for the VFM is proposed by modifying the basis function of RBF. The RBF method is constructed on the HF samples, while the Co-RBF method incorporates the entire information of the LF model with the HF samples. In Co-RBF, the LF model is regard as a basis function of Co-RBF and the HF samples are utilized to compute the Co-RBF model coefficients. Two numerical functions and three engineering problems are adopted to verify the proposed Co-RBF method. The predictive results of Co-RBF are compared with those of RBF and Co-Kriging, which show that the Co-RBF method improves the efficiency, accuracy and robustness of the existing VFMs.     

Surveying instructor and learner attitudes toward e-learning

The trend of using e-learning as a learning and/or teaching tool is now rapidly expanding into education. Although e-learning environments are popular, there is minimal research on instructors’ and learners’ attitudes toward these kinds of learning environments. The purpose of this study is to explore instructors’ and learners’ attitudes toward e-learning usage. Accordingly, 30 instructors and 168 college students are asked to answer two different questionnaires for investigating their perceptions. After statistical analysis, the results demonstrate that instructors have very positive perceptions toward using e-learning as a teaching assisted tool. Furthermore, behavioral intention to use e-learning is influenced by perceived usefulness and self-efficacy. Regarding to learners’ attitudes, self-paced, teacher-led, and multimedia instruction are major factors to affect learners’ attitudes toward e-learning as an effective learning tool. Based on the findings, this research proposes guidelines for developing e-learning environments.     

动态搜索和协同进化的鲸鱼优化算法

针对基本鲸鱼优化算法寻优精度低、收敛速度慢及容易陷入局部最优等缺陷,提出了一种动态搜索和协同进化的鲸鱼优化算法。首先,通过等价替换和Faure序列提高初始解的质量;其次,通过对种群进行分工,提高种群多样性并增强算法跳出局部最优解的能力;最后,根据种群进化信息动态调整搜索策略,从而提高算法的收敛速度和寻优精度。仿真实验结果表明,提出的改进算法相比基本鲸鱼优化算法和部分改进算法具有较好的寻优性能。     

A new approach to fuzzy modeling of nonlinear dynamic systems with noise: relevance vector learning mechanism

This paper presents a new fuzzy inference system for modeling of nonlinear dynamic systems based on input and output data with measurement noise. The proposed fuzzy system has a number of fuzzy rules and parameter values of membership functions which are automatically generated using the extended relevance vector machine (RVM). The RVM has a probabilistic Bayesian learning framework and has good generalization capability. The RVM consists of the sum of product of weight and kernel function which projects input space into high dimensional feature space. The structure of proposed fuzzy system is same as that of the Takagi-Sugeno fuzzy model. However, in the proposed method, the number of fuzzy rules can be reduced under the process of optimizing a marginal likelihood by adjusting parameter values of kernel functions using the gradient ascent method. After a fuzzy system is determined, coefficients in consequent part are found by the least square method. Examples illustrate effectiveness of the proposed new fuzzy inference system.     

压电式压力传感器的一种动态建模方法

针对数学模型在动态校准实验数据处理中的重要地位,结合激波管对压力传感器测试系统动态校准所得实验数据,介绍了一种基于广义最小二乘法的动态数学模型建立方法.并利用Matlab/Simulink模块进行了仿真验证,由仿真结果表明该时间域数据处理方法具有简洁性、准确性的特点,特别适合于动态校准中建立差分方程模型.     

A proposal toward the development of accessible e-learning content by human involvement

Most of the existing efforts for supporting the design, preparation, and deployment of accessible e-learning applications propose guidelines that primarily address technical accessibility issues. Little, if any, consideration is given to the real actors involved in the learning experience, such as didactical experts and disabled learners. Moreover, implementing artifacts addressed to the e-learning world requires a wide range of particular skills which are related not only to technical but also to didactical, pedagogical, usability, and accessibility aspects of the produced material. This paper argues that the know-how of a number of stakeholders should be blended into a joint design activity, and that it should be possible to determine the role of each participant in the successive phases of the development lifecycle of e-learning applications. The paper sketches the methodological guidelines of a design framework based on involving the users with disabilities, as well as pedagogical experts, in the development process. The novelty of this proposal mainly stems from being built up around the core of strategies and choices specifically bound to accessibility requirements. Characteristic elements of learner-centered design are then further integrated into processes and methodologies which are typical of participatory and contextual design approaches. Following such guidelines, it will be possible to gain a deeper understanding of the requirements and of the operational context of people needing accessible material, either as learners or educators. The underlying objective is to increase the potential to realize learning systems that better meet different user needs and that provide a more satisfying learning experience. Moreover, when people get involved in the development process, they gain a sense of ownership of the system and are therefore more likely to accept and “promote” it.     

A development of learning widget on m-learning and e-learning environments

This article describes the development of learning widget on m-learning and e-learning environments. A widget is a small, simple and useful application supporting user-oriented contents. The user may select and install widgets that are convenient as well as an auto-updating application including weather or calendar. These widgets are especially more useful, because they are able to be installed on a mobile device, a website or a desktop computer. If we take advantage of widgets for education, we may use this learning tool for delivering and pulling learning contents, essences of lessons or word learning. To that end, we developed an effective learning widget and then verified its usability, usefulness and effectiveness for m-learning and e-learning. That is, we evaluated the learning widget with a heuristic evaluation method. We identified 72 interface problems by using a set of 10 usability criteria or heuristics. In addition, we considered how to design the learning widget with consideration given to devices on m-learning and e-learning. Moreover, we experimented by conducting a pilot test with 34 students, a field test with 60 teachers and technology acceptance model (TAM) analysis with 15 teachers. We verified the effectiveness and usefulness of learning with a questionnaire, a quiz and TAM, where the subjects, after using the learning widget in real learning activities, rated the widget's efficacy. The result shows that the learning widget is useful for m-learning and e-learning environments.     

基于MAS的动态企业建模方法

动态企业建模技术是为适应企业多变的市场环境和经常的企业业务过程重构而发展起来的技术;而Agent技术和MAS系统具有自治性、社会性、理性等突出特点。对运用具有不同性质的Agent构成三层企业模型MAS的方法进行了深入的研究,它在不同层次上支持企业业务、组织结构的重组,增加了企业模型的柔性。     

A step toward a human-robot cooperative system

In the near future, we will have to live with many machines such as home robots, hospital robots, and so on. User-friendly communication between humans and those machines is very important for a cooperative system utilizing their features and abilities. As an example of a human-robot cooperative system, we propose an intuitive approach to robot teaching with multimedia tools.     

A new approach to fuzzy modeling and control for nonlinear dynamic systems: Neuro-fuzzy dynamic characteristic modeling and adaptive control mechanism

The study on nonlinear control system has received great interest from the international research field of automatic engineering. There are currently some alternative and complementary methods used to predict the behavior of nonlinear systems and design nonlinear control systems. Among them, characteristic modeling (CM) and fuzzy dynamic modeling are two effective methods. However, there are also some deficiencies in dealing with complex nonlinear system. In order to overcome the deficiencies, a novel intelligent modeling method is proposed by combining fuzzy dynamic modeling and characteristic modeling methods. Meanwhile, the proposed method also introduces the low-level learning power of neural network into the fuzzy logic system to implement parameters identification. This novel method is called neuro-fuzzy dynamic characteristic modeling (NFDCM). The neuro-fuzzy dynamic characteristic model based overall fuzzy control law is also discussed. Meanwhile the local adaptive controller is designed through the golden section adaptive control law and feedforward control law. In addition, the stability condition for the proposed closed-loop control system is briefly analyzed. The proposed approach has been shown to be effective via an example. Recommended by Editor Young-Hoon Joo. This work was jointly supported by National Natural Science Foundation of China under Grant 60604010, 90716021, and 90405017 and Foundation of National Laboratory of Space Intelligent Control of China under Grant SIC07010202. Xiong Luo received the Ph.D. degree from Central South University, Changsha, China, in 2004. From 2005 to 2006, he was a Postdoctoral Fellow in the Department of Computer Science and Technology at Tsinghua University. He currently works as an Associate Professor in the Department of Computer Science and Technology, University of Science and Technology Beijing. His research interests include intelligent control for spacecraft, intelligent optimization algorithms, and intelligent robot system. Zengqi Sun received the bachelor degree from Tsinghua University, Beijing, China, in 1966, and the Ph.D. degree from Chalmers University of the Technology, Gothenburg, Sweden, in 1981. He currently works as a Professor in the Department of Computer Science and Technology, Tsinghua University. His research interests include intelligent control of robotics, fuzzy neural networks, and intelligent flight control. Fuchun Sun received the Ph.D. degree from Tsinghua University, Beijing, China, in 1998. From 1998 to 2000, he was a Postdoctoral Fellow in the Department of Automation at Tsinghua University, where he is currently a Professor in the Department of Computer Science and Technology. His research interests include neural-fuzzy systems, variable structure control, networked control systems, and robotics.     

Interaction learning for dynamic movement primitives used in cooperative robotic tasks

Since several years dynamic movement primitives (DMPs) are more and more getting into the center of interest for flexible movement control in robotics. In this study we introduce sensory feedback together with a predictive learning mechanism which allows tightly coupled dual-agent systems to learn an adaptive, sensor-driven interaction based on DMPs. The coupled conventional (no-sensors, no learning) DMP-system automatically equilibrates and can still be solved analytically allowing us to derive conditions for stability. When adding adaptive sensor control we can show that both agents learn to cooperate. Simulations as well as real-robot experiments are shown. Interestingly, all these mechanisms are entirely based on low level interactions without any planning or cognitive component.     

基于二阶相似度的即时学习软测量建模方法

针对即时(惰性)学习模型频率降低间接导致的精度下降问题,提出一种二阶相似性的即时学习方法。该方法综合顾及到样本集的整体分布特性,在传统一阶相似度准则的基础上建立二阶相似度准则,采用与测试样本具有绝大部分相同近邻的二阶相似样本建立当前时刻的模型;同时将累计相似度因子用于建立局部模型时样本量的确定,并采用相似度阈值的方式判断此刻模型是否需要重新建立。该方法在青霉素发酵过程产物浓度的预测实验中得到了有效的验证。     

A measurement fusion method for nonlinear system identification using a cooperative learning algorithm

Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.     

Web服务动态组合方法研究与设计

目前基于工作流的Web服务组合方法大多要求预先定义流程结构,不能适应现代企业业务流程复杂多变的需求.提出了一种Web服务动态组合方法,将流程中动态部分用黑盒子流程进行封装,在执行时根据预定义规则和用户需求将黑盒子流程自动组合展开为实际运行流程.对具体服务、抽象服务、黑盒子流程等概念和服务选取规则、服务组合规则进行了定义,详细描述黑盒子流程展开算法,并设计了一个实现动态服务流程组合的框架结构.