分布计算软件平台StarBus及其在"数据九七"中的应用

分布计算软件平台ＳｔａｒＢｕｓ是采用分布对象技术,遵循ＣＯＲＢＡ标准的系统集成中间件。利用ＳｔａｒＢｕｓ开发分布式应用可以极大地提高垂发效率。该文在ＳｔａｒＢｕｓ系统结构进行介绍的基础上,着重阐述了ＳｔａｒＢｕｓ在数据业务综合管理服务系统（简称“数据九七”）中的应用。     

Fault tolerant multiple event detection in a wireless sensor network

With an increasing acceptance of Wireless Sensor Networks (WSNs), the health of individual sensor is becoming critical in identifying important events in the region of interest. One of the key challenges in detecting event in a WSN is how to detect it accurately transmitting minimum information providing sufficient details about the event. At the same time, it is also important to devise a strategy to handle multiple events occurring simultaneously. In this paper, we propose a Polynomial-based scheme that addresses these problems of Event Region Detection (PERD) by having a aggregation tree of sensor nodes. We employ a data aggregation scheme, TREG (proposed in our earlier work) to perform function approximation of the event using a multivariate polynomial regression. Only coefficients of the polynomial (P$P$) are passed instead of aggregated data. PERD includes two components: event recognition and event report with boundary detection. This can be performed for multiple simultaneously occurring events. We also identify faulty sensor(s) using the aggregation tree. Performing further mathematical operations on the calculated P$P$ can identify the maximum (max) and minimum (min) values of the sensed attribute and their locations. Therefore, if any sensor reports a data value outside the [min, max] range, it can be identified as a faulty sensor. Since PERD is implemented over a polynomial tree on a WSN in a distributed manner, it is easily scalable and computation overhead is marginal. Results reveal that event(s) can be detected by PERD with error in detection remaining almost constant achieving a percentage error within a threshold of 10%$10\%$ with increase in communication range. Results also show that a faulty sensor can be detected with an average accuracy of 94%$94\%$ and it increases with increase in node density.     

自控网系统的仿真分析与硬件实现的研究

自控网系统是Petri网的一个子类,与P/T系统相比,有更强的描述能力和更复杂的性质。由于它的非线性关系,无法直接套用其它网系统的分析技术,影响了对它的研究。文章提出了用硬件描述语言对自控网系统进行描述,通过EDA工具进行仿真分析,揭示系统的性质。文章针对计算Fibonacci数列的自控网系统模型,详细介绍了这一方法,给出了ABEL语言源代码和仿真波形,源代码经编译、优化、适配并下载到在系统可编程逻辑器件中,得到了计算Fibonacci数列的专用芯片,这为自控网系统的分析和应用研究开辟了新的途径。     

Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times

Business processes are dynamic and change due to diverse factors. While existing approaches aim to detect drifts in the process structure, Tesseract looks for temporal drifts in activity interim times. This orthogonal view on the process extends the traditional data cube of events – case id, activities and timestamps – by a fourth dimension and improves the operational support by a visualization of temporal drifts in real-time.Insights about temporal deviations lead to an augmented awareness of imminent failures or improved service times. The detection of related structural concept drifts can be improved by early warning, as operation times of critical parts often increase before they catastrophically fail.     

一种中文事件事实性识别方法

事件事实性指出了事件发生与否的确定性程度,是自然语言理解的基础。在研究过程中,针对中文事件的事实性识别问题,提出了一种基于特征工程的有效识别方法。该方法选取事件的事实性相关信息进行特征的处理和转化。同时,考虑到部分特征与事件事实性之间的联系,依据规则进行特征融合。实验证明,相比基于规则的事件事实性识别方法,该方法有着更好的识别效果。     

Change visualisation: Analysing the resource and timing differences between two event logs

With organisations facing significant challenges to remain competitive, Business Process Improvement (BPI) initiatives are often conducted to improve the efficiency and effectiveness of their business processes, focussing on time, cost, and quality improvements. Event logs which contain a detailed record of business operations over a certain time period, recorded by an organisation's information systems, are the first step towards initiating evidence-based BPI activities. Given an (original) event log as a starting point, an approach to explore better ways to execute a business process was developed, resulting in an improved (perturbed) event log. Identifying the differences between the original event log and the perturbed event log can provide valuable insights, helping organisations to improve their processes. However, there is a lack of automated techniques and appropriate visualisations to detect the differences between two event logs. Therefore, this research aims to develop visualisation techniques to provide targeted analysis of resource reallocation and activity rescheduling. The differences between two event logs are first identified. The changes between the two event logs are conceptualised and realised with a number of visualisations. With the proposed visualisations, analysts are able to identify resource- and time-related changes that resulted in a cost reduction, and subsequently investigate and translate them into actionable items for BPI in practice. Ultimately, analysts can make use of this comparative information to initiate evidence-based BPI activities.     

Making use of prognostics health management information for aerospace spare components logistics network optimisation

Although research has evolved significantly over the last decade, there are still a large number of Grand Challenges confronting modelling, model deployment, and model-based decision making of large-scale complex Discrete Event Logistics Systems (DELS) to be tackled, as identified and reviewed during a Dagstuhl workshop in March 2010. This paper illustrates how several of these challenges are already being addressed, based on a series of case studies from the Aerospace Spare Components Logistics domain, where consolidated operational Prognostics and Health Management (PHM) information can be used for tactical planning and optimisation of spare components logistics networks. In this setting, the growing potential of PHM technology to facilitate the maintenance and support of commercial and military aircraft emphasises the need for tools to determine the impacts and benefits of a PHM system. To achieve this, the prognostics parameters and related logistics policies were identified, modelled, and subsequently incorporated into a simulation-based decision support framework.     

A resource-efficient event algebra

Events play many roles in computer systems, ranging from hardware interrupts, over event-based software architecture, to monitoring and managing of complex systems. In many applications, however, individual event occurrences are not the main point of concern, but rather the occurrences of certain event patterns. Such event patterns can be defined by means of an event algebra, i.e., expressions representing the patterns of interest are built from simple events and operators such as disjunction, sequence, etc.We propose a novel event algebra with intuitive operators (a claim which is supported by a number of algebraic properties). We also present an efficient detection algorithm that correctly detects any expression with bounded memory, which makes this algebra particularly suitable for resource-constrained applications such as embedded systems.     

Quantifying target spotting performances with complex geoscientific imagery using ERP P300 responses

Geoscientific data interpretation is a challenging task, which requires the detection and synthesis of complex patterns within data. As a first step towards better understanding this interpretation process, our research focuses on quantitative monitoring of interpreters' brain responses associated with geoscientific target spotting. This paper presents a method that profiles brain responses using electroencephalography (EEG) to detect P300-like responses that are associated with target spotting for complex geoscientific data. In our experiment, eight interpreters with varying levels of expertise and experience were asked to detect features, which are likely to be copper–gold rich porphyry systems within magnetic geophysical data. The target features appear in noisy background and often have incomplete shape. Magnetic images with targets and without targets were shown to participants using the “oddball” paradigm. Event related potentials were obtained by averaging the EEG epochs across multiple trials and the results show delayed P3 response to the targets, likely due to the complexity of the task. EEG epochs were classified and the results show reliable single trial classification of EEG responses with an average accuracy of 83%. The result demonstrated the usability of the P300-like responses to quantify the geoscientific target spotting performances.