Use of superposition principle to derive a general mathematical model to simulate one-to-one, one-to-multi and multi-to-multi saw filter designs

This paper explains and summarizes a new attempt to derive a general mathematical model [GMM] to simulate surface acoustic wave (SAW) filters, using the superposition principle and delta function model. GMM can be used to simulate One-to-One, One-to-Multi and Multi-to-Multi SAW filter devices. The simulation program was written using MATLAB (the language of technical computing). Four-design structures (One-to-One, One-to-Two, One-to-Three and Ten-to-Ten) ware selected to test the correctness of GMM. The frequency response of the simulation and test results are similar in center frequency and 3-dB bandwidth, but the insertion loss is different, because of some second order effects (Issa Haitham, 1999).     

USE OF SUPERPOSITION PRINCIPLE TO DERIVE A GENERAL MATHEMATICAL MODEL TO SIMULATE ONE-TO-ONE,ONE-TO-MULTI AND MULTI-TO-MULTI SAW FILTER DESIGNS

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用于交通运营管理的实时交通流状态分类高斯混合模型

采用高斯混合模型GMM,同时以交通流量、平均速度和密度3种交通流宏观特征为指标,对交通流状态进行聚类和分类.和其他聚类分类方法比较,高斯混合模型是结构化的模型,适合于各种情形交通流参数.高斯混合模型中子类的个数通过Gap统计量结合交通流的领域知识加以确定,而模型的其他参数则由E-M算法进行估计.所建立的GMM模型可以作...     

Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter

In this paper, a prediction model is developed that combines a Gaussian mixture model (GMM) and a Kalman filter for online forecasting of traffic safety on expressways. Raw time-to-collision (TTC) samples are divided into two categories: those representing vehicles in risky situations and those in safe situations. Then, the GMM is used to model the bimodal distribution of the TTC samples, and the maximum likelihood (ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization (EM) algorithm. We propose a new traffic safety indicator, named the proportion of exposure to traffic conflicts (PETTC), for assessing the risk and predicting the safety of expressway traffic. A Kalman filter is applied to forecast the short-term safety indicator, PETTC, and solves the online safety prediction problem. A dataset collected from four different expressway locations is used for performance estimation. The test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different datasets. These results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.     

Growth Mixture Modeling With Nonnormal Distributions: Implications for Data Transformation

This study investigated the extent to which class-specific parameter estimates are biased by the within-class normality assumption in nonnormal growth mixture modeling (GMM). Monte Carlo simulations for nonnormal GMM were conducted to analyze and compare two strategies for obtaining unbiased parameter estimates: relaxing the within-class normality assumption and using data transformation on repeated measures. Based on unconditional GMM with two latent trajectories, data were generated under different sample sizes (300, 800, and 1500), skewness (0.7, 1.2, and 1.6) and kurtosis (2 and 4) of outcomes, numbers of time points (4 and 8), and class proportions (0.5:0.5 and 0.25:0.75). Of the four distributions, it was found that skew-t GMM had the highest accuracy in terms of parameter estimation. In GMM based on data transformations, the adjusted logarithmic method was more effective in obtaining unbiased parameter estimates than the use of van der Waerden quantile normal scores. Even though adjusted logarithmic transformation in nonnormal GMM reduced computation time, skew-t GMM produced much more accurate estimation and was more robust over a range of simulation conditions. This study is significant in that it considers different levels of kurtosis and class proportions, which has not been investigated in depth in previous studies. The present study is also meaningful in that investigated the applicability of data transformation to nonnormal GMM.     

The Impact of Misspecifying Class-Specific Residual Variances in Growth Mixture Models

The purpose of this study was to examine the impact of misspecifying a growth mixture model (GMM) by assuming that Level-1 residual variances are constant across classes, when they do, in fact, vary in each subpopulation. Misspecification produced bias in the within-class growth trajectories and variance components, and estimates were substantially less precise than those obtained from a correctly specified GMM. Bias and precision became worse as the ratio of the largest to smallest Level-1 residual variances increased, class proportions became more disparate, and the number of class-specific residual variances in the population increased. Although the Level-1 residuals are typically of little substantive interest, these results suggest that researchers should carefully estimate and report these parameters in published GMM applications.     

Application of gram-schmidt regression to modeling of giant magnetostrictive material

A giant magnetostrictive material (GMM) model is developed based on the hysteretic nonlinear theory. The Gram-Schmidt regression method is introduced to determine the parameters of the model as well as the relation- ship between the material strain and the strength and frequency of magnetic field in the model. Through comparison, it is shown that this regression method has good performance in significance test. Then the model is applied to study the motion law of a circular plate in classical GMM transducer, which helps control the transducer rapidly and accurately.     

Poisson Growth Mixture Modeling of Intensive Longitudinal Data: An Application to Smoking Cessation Behavior

Intensive longitudinal data (ILD) have become increasingly common in the social and behavioral sciences; count variables, such as the number of daily smoked cigarettes, are frequently-used outcomes in many ILD studies. We demonstrate a generalized extension of growth mixture modeling (GMM) to Poisson-distributed ILD for identifying qualitatively distinct trajectories in the context of developmental heterogeneity in count data. Accounting for the Poisson outcome distribution is essential for correct model identification and estimation. In addition, setting up the model in a way that is conducive to ILD measures helps with data complexities - large data volume, missing observations, and differences in sampling frequency across individuals. We present technical details of model fitting, summarize an empirical example of patterns of smoking behavior change, and describe research questions the generalized GMM helps to address.     

Learning to be a smart citizen

ABSTRACT

The international Smart Cities and the Learning Cities movements are not often linked. However, there are learning questions at stake here. Smart city agendas are often criticised as being technocratic and instrumental, prioritising market-led solutions to urban issues. Such criticism has led to moves to place the citizen at the centre of these discussions. This raises educational challenges: what theories and forms of learning are required for citizens to play a role in the development of digital, urban futures? This paper adopts ethnographic methods to study the assumptions about learning in a Europe-wide smart city project that included a component of citizen-led development. Our argument provides important messages for smart city planners and developers keen to include citizens in smart city development. It suggests that the current ‘banking’ models of learning adopted in relation to citizen participation are not fit for purpose and that a new model is needed. This needs to recognise citizen learning as situated in social and material contexts and embedded in unequal relations of power, knowledge and resources. We make the case for smart city initiatives to offer city inhabitants critical, creative learning opportunities that begin to address the inequalities that constitute the contemporary smart city.     

智慧课堂教学新形态：纸笔数字书写技术教学应用

纸笔数字书写是指对手持工具进行自然书写产生的轨迹或图形等视觉符号进行数字化处理的过程,具有保留传统纸笔书写行为、采集完整书写数据、实现远程共享书写空间等优势。纸笔数字书写技术是对传统纸笔书写的信息技术赋能,它与传统课堂教学的融合能为当前信息化教学“屏幕过度依赖”带来的健康和教学问题提供新的解决方案,因而也可能成为一种新型的智慧课堂教学形态。为了证实纸笔数字书写技术的教学应用潜力,以“腾讯企鹅智笔课堂”为例开展的实证调查表明:学生使用点阵笔的整体满意度较高,女生满意度水平普遍高于男生;技术自我效能感、互动学习活动、技术使用态度和感知有用性是影响学生使用点阵笔满意度的主要因素,其中技术自我效能感和互动学习活动的影响程度最大;技术接受模型中的感知易用性对学生使用点阵笔的满意度影响并不显著,因此还需要在教学实践中不断摸索以纸笔数字书写技术为支持的最佳教学策略。     

A Monte Carlo Study Investigating the Impact of Item Parceling Strategies on Parameter Estimates and Their Standard Errors in CFA

Growth mixture modeling (GMM) has become a more popular statistical method for modeling population heterogeneity in longitudinal data, but the performance characteristics of GMM enumeration indexes in correctly identifying heterogeneous growth trajectories are largely unknown. Few empirical studies have addressed this issue. This study considered both homogeneous (a k = 1 growth trajectory) and heterogeneous (k = 3 different but unobserved growth trajectories) situations, and examined the performance of GMM in correctly identifying the latent trajectories in sample data. Four design conditions were manipulated: (a) sample size, (b) latent trajectory class proportions, (c) shapes of latent growth trajectories, and (d) degree of separation among latent growth trajectories. The findings suggest that, for k = 1 condition (1 homogenous growth trajectory), GMM's performance is reasonable in correctly identifying 1 latent growth trajectory (cf. Type I error control). However, for the k = 3 conditions (3 heterogeneous latent growth trajectories), GMM's general performance is very questionable (cf. Type II error). Different enumeration indexes varied considerably in their respective performances. Comparing the current results with previous GMM studies, the limitations of this study and future GMM enumeration research avenues are all discussed.     

基于Fuzzy的地方高校智慧校园环境成熟度评价体系研究

运用模糊综合评价法推进智慧校园发展。对某高校智慧校园环境建设中基础设施、支撑平台、体制机制等3个方面的内容进行讨论分析,结合专家咨询的方法,确定了各维度指标的权重,利用模糊综合评价法计算出各维度的模糊隶属度,建立矩阵,分析评价结果。采用多级模糊综合评价方法构建的智慧校园环境建设评估模型,具有相对客观性和合理性,评价结果能较好地指导智慧校园发展建设与改进目标。     

Are academics ready for smart learning?

Ownership of smartphones and tablets among the student population is growing. Students are using their devices to support their learning. Employers and employees are increasingly bringing their own smart devices into private and public organisations to support their business. This is leading to employees driving the Bring Your Own Device (BYOD) agenda in organisations. It is not clear the extent to which academics are embracing smart technology to manage their workload or to enhance the student experience of learning. This paper presents a qualitative study of how engineering academics are using their own BYOD or institutionally provided smart devices. A 6Cs (connect, communicate, collaborate, curate, create and coordinate) framework has been used to analyse the results. The findings indicate that academics are primarily using devices to create materials, second to coordinate their work and third to communicate with students about their learning. However, there are a number of inhibiting and enabling factors that need to be addressed by academic institutions to develop the effective adoption of smart technologies for academic practice. Infrastructure, including developing widespread access to WiFi, and the prioritisation of opportunities to support staff to learn how to apply the technology to enhance student learning and experience are key areas of necessary development.     

智慧教室评价指标体系的构建

文章首先介绍了智慧教室的概念与内涵,并通过文献梳理分析了其评价现状。接着,文章初拟智慧教室评价指标,并运用德尔菲法对这些指标进行了验证,由此确定了7个一级评价指标。随后,文章运用层次分析法,在一级评价指标的基础上,确定了26个二级评价指标,形成了智慧教室评价指标体系的层次结构模型。最后,文章通过构造判断矩阵、进行一致性检验,计算出各评价指标的权重,构建了智慧教室评价指标体系。智慧教室评价指标体系的构建,可为智慧教室的建设和评估提供参考,并有力促进课堂教学的改革发展。     

基于WLAN通信的智能小车设计

以智能小车作为远程控制对象,使用嵌入式Linux系统作为智能小车的操作系统,建立远程控制系统的基本模型,构建无线网络通信平台、智能小车软、硬件平台,分析智能小车与PC机服务器之间的通信和循迹,实现了基于WLAN通信的智能小车应用。     

Research on control method for machining non-cylinder pin hole of piston

INTRODUCTION With the development of engine power and ro- tational speed, the load of pin hole of piston is more and more heavy. By analyzing the load of common cylindrical pin hole, the load of each part of the pin hole is asymmetric when the engine is working. The stress of inner side is the most and that of the outer side is the least. So, to improve engine performance, people reduce the stress concentration of the inner side of the pin hole by changing the geometric shape and improve …     

智慧学习的概念框架与模式设计

智慧教育是信息技术与教育发展高度融合而产生的新型教育模式,代表了未来教育的发展与改革方向。智慧学校、智慧图书馆等智慧环境的不断建设与完善为实现数字化学习向智慧学习的转变提供了可能。该文在已有研究基础上界定了智慧学习的内涵,构建了智慧学习的概念框架,设计了四种智慧学习模式,分别是独立自助式学习模式、群组协作式学习模式、入境学习模式以及创客学习模式,期望能够为智慧学习的有效开展提供一定的指导。     

Adaptive foreground and shadow segmentation using hidden conditional random fields

Video object segmentation is important for video surveillance, object tracking, video object recognition and video editing. An adaptive video segmentation algorithm based on hidden conditional random fields (HCRFs) is proposed, which models spatio-temporal constraints of video sequence. In order to improve the segmentation quality, the weights of spatio-temporal con- straints are adaptively updated by on-line learning for HCRFs. Shadows are the factors affecting segmentation quality. To separate foreground objects from the shadows they cast, linear transform for Gaussian distribution of the background is adopted to model the shadow. The experimental results demonstrated that the error ratio of our algorithm is reduced by 23% and 19% respectively, compared with the Gaussian mixture model (GMM) and spatio-temporal Markov random fields (MRFs).     

智能时代课堂变革图景:智慧课堂及其构建策略

智能时代对课堂变革提出新要求,澄明智能技术“何以能”及“如何能”促进学生智慧发展的逻辑理路是开展智慧课堂实践的认识论前提。文章在分析智慧课堂内涵的基础上,首先,提出了课堂学习的物质实践观,即学习可以理解为是一种学习者以物质技术手段为中介的认知性实践,学习者使用物质技术手段的水平能反映其学习实践水平;其次,通过剖析智慧课堂学习方式、场域要素构成,揭示了课堂场域支持学生智慧发展的微观逻辑,构建了反映“智慧生成”及“技术优化”逻辑的智慧课堂模型;最后,提出了智慧课堂构建策略,包括聚焦学生智慧的多元高阶发展、建立问题解决的智慧生成效应、发挥智能技术的优化调节效应。以此,为智慧课堂教学实践提供理论借鉴及实践参照。     

嵌入式系统PCI总线接口设计

PCI总线是一种高性能外围设备接口总线,已经广泛地被应用于嵌入式系统.由于PCI总线协议的复杂性,其接口实现比较困难.通过设计一种身份认证智能卡,介绍了利用PCI 9054实现嵌入式系统中FPGA与PCI总线接口的设计过程,并指出了开发过程中应注意的问题.