Complexities of digital technology use and the teaching and learning of function

Many argue that digital technologies have the potential to enhance the teaching and learning of mathematics. However, the availability of technology is not sufficient to realise this potential. The study reported takes a detailed approach to investigate the utility of the particular offerings of the available technologies in the teaching and learning of a specific area of mathematics, functions. Sixteen affordances identified in the data are described. The complexity of the process involved in resolving a situation where particular affordances would be useful so as they are perceived and enacted is detailed. Finally, a grounded theory framework arising from the data analysis from this study that can be used to explain, predict and guide action in other digital environments is presented.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

构建媒体网络信息安全体系

通过对广州市电视台综合信息平台网络系统的应用现状及安全风险进行分析,提出基于网络层、系统层、用户层和安全策略管理全方位的网络信息安全体系.     

一种具有带孔补偿过滤器的γ剂量计的能响补偿计算

对在低能区有过高响应的γ辐射剂量计可采用带孔补偿过滤器进行能响补偿。本文介绍了对这类补偿过滤器选择最佳补偿参数(材料厚度和孔隙率)的近似求解方法;并对 CaSO:Tm 热释光剂量计用此方法作了实例计算。文中还计算了自制带有鼠笼结构的这类补偿过滤器的 CaSO:,Tm热释光剂量计对照射量的能量响应,与实测结果比较,各能量点响应的最大相差为10%。     

Indicators for inspection and maintenance planning of concrete structures

Based on an idea introduced by Benjamin and Cornell (1970. Probability, statistics and decision for civil engineers. New York: McGaw Hill) and previous works by the authors it is demonstrated how condition indicators may be formulated for the general purpose of quality control and for assessment and inspection planning in particular. The formulation facilitates quality control based on sampling of indirect information about the condition of the considered components. This allows for a Bayesian formulation of the indicators whereby the experience and expertise of the inspection personnel may be fully utilized and consistently updated as frequentistic information is collected. The approach is illustrated on an example considering a concrete structure subject to corrosion. It is shown how half-cell potential measurements may be utilized to update the probability of excessive repair after 50 years. Furthermore in the same example it is shown how the concept of condition indicators might be applied to develop a cost optimal maintenance strategy composed of preventive and corrective repair measures.     

Effect of sampling density on the measurement of stream condition indicators in two lowland Australian streams

There is widespread application of indicators to the assessment of environmental condition of streams. These indicators are intended for use by managers in making various comparative and absolute assessments and often have a role in resource allocation and performance assessment. Therefore, the problem of formally defining confidence in the results is important but difficult because the sampling strategies used are commonly based on a compromise between the requirements of statistical rigour and the pragmatic issues of access and resources. It is rare to see this compromise explicitly considered and consequently there is seldom quantification of the uncertainty that could affect the confidence a manager has in an indicator. In this paper, we present a method for quantitatively assessing the tradeoffs between sampling density and uncertainty in meeting various monitoring objectives. Assessments using judgement‐based representative reaches are shown to be unreliable; instead a sampling approach is recommended based on the random selection of measuring sites. A detailed dataset was collected along two streams in Victoria, Australia, and the effect of sampling density was assessed by subsampling from this dataset with precision related to the number of sites assessed per reach length and the intensity of the sampling at each site. The sampling scheme to achieve a given precision is shown to depend on the monitoring objective. In particular, three objectives were considered: (1) making a baseline assessment of current condition; (2) change detection; and (3) detection of a critical threshold in condition. Change detection is shown to be more demanding than assessing baseline condition with additional sampling effort required to achieve the same precision. Sampling to detect a critical threshold depends on nominating acceptable values of Type I and II error and the size of the effect to be detected. Copyright © 2006 John Wiley & Sons, Ltd.     

Introducing Students to Professional Practice in Civil Engineering

This paper describes a module that was introduced into a civil engineering degree program with the help of professional engineers. The aim was to develop a bridge between the world of learning and professional practice by putting students in the role of consulting engineers working with industry to produce a feasible solution to a real inquiry from a client. The module is placed in context by comparing the goals of accredited civil engineering programs in the United Kingdom and America, by describing how it is linked to the degree program and by explaining the matrix developed to identify the skills the students needed to demonstrate their ability to practice as professional engineers. Details of the module are given with examples of student work and feedback.     

正确处理三种关系,促进测量学教学健康发展--非测绘专业测量学的教学研究

结合教学实践，对如何正确处理好教学与科研、传统技术与新技术、传统教学与多媒体教学之间的关系进行了研究，并得出正确处理好这三种关系是促进测量学教学健康发展和提高教学质量的有效途径的结论。     

Application of stochastic filtering for lifetime prediction

This paper introduces a stochastic filtering modeling approach for predicting the remaining lifetime of a component based on information on the stochastic degradation process and uncertain condition monitoring measurements. The model is illustrated by a case study, where the degradation is assumed to be a simplified fatigue crack growth process. The model accounts for uncertainties in both degradation process and condition measurements in a sound way. If completed with information on costs of monitoring, failure and replacement, such model could be used in optimizing both the condition monitoring intervals and, e.g. the replacement time for the component.     

State observability and condition observability for a class of interacting discrete event systems

This paper introduces the concepts of state observability and condition observability for condition systems, a class of systems composed of discrete state components which interact via discrete binary signals called conditions. Given a set of externally observed conditions, state observability implies that the state of the system can be determined from the observations, and condition observability implies that all unobserved input and output conditions of the system can be determined from the observations. In this paper, we present a class of systems which is state observable and condition observable. We present a method to synthesize an observer system to provide state and condition signal estimates for a single component subsystem.