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Since Z3, the first automatic, programmable and operational computer, emerged in 1941, computers have become an unshakable tool in varieties of engineering researches, studies and applications. In the field of hydroinformatics, there exist a number of tools focusing on data collection and management, data analysis, numerical simulations, model coupling, post-processing, etc. in different time and space scales. However, one crucial process is still missing — filling the gap between available mass raw data and simulation tools.
In this research work, a general software framework for time series scenario composition is proposed to improve this issue. The design of this framework is aimed at facilitating simulation tasks by providing input data sets, e.g. Boundary Conditions (BCs), generated for user-specified what-if scenarios. These scenarios are based on the available raw data of different sources, such as field and laboratory measurements and simulation results. In addition, the framework also monitors the workflow by keeping track of the related metadata to ensure its traceability.
This framework is data-driven and semi-automatic. It contains four basic modules: data pre-processing, event identification, process identification, and scenario composition. These modules mainly involve Time Series Knowledge Mining (TSKM), fuzzy logic and Multivariate Adaptive Regression Splines (MARS) to extract features from the collected data and interconnect themselves. The extracted features together with other statistical information form the most fundamental elements, MetaEvents, for scenario composition and further time series generation. The MetaEvents are extracted through semi-automatic steps forming Aspects, Primitive Patterns, Successions, and Events from a set of time series raw data. Furthermore, different state variables are interconnected by the physical relationships derived from process identification. These MetaEvents represent the complementary features and consider identified physical relationships among different state variables from the available time series data of different sources rather than the isolated ones. The composed scenarios can be further converted into a set of time series data as, for example, BCs, to facilitate numerical simulations.
A software prototype of this framework was designed and implemented on top of the Java and R software technologies. The prototype together with four prototype application examples containing mathematical function-generated data, artificial model-synthetic hydrological data, and measured hydrological and hydrodynamic data, are used to demonstrate the concept. The results from the application examples present the capability of reproducing similar time series patterns from specific scenarios compared to the original ones as well as the capability of generating artificial time series data from composed scenarios based on the interest of users, such as numerical modelers. In this respect, it demonstrates the concept’s capability of answering the impacts from what-if scenarios together with simulation tools. The semi-automatic concept of the prototype also prevents from inappropriate black-box applications and allows the consideration of the knowledge and experiences of domain experts. Overall, the framework is a valuable and progressive step towards holistic hydroinformatics systems in reducing the gap between raw data and simulation tools in an engineering suitable manner.
Definition and configuration of reliable event detection for application in wireless sensor networks
(2010)
Ubiquitous systems based on wireless sensor networks will amazingly increase our quality of life. These systems are to be deployed in large areas with high density where hundreds or thousands of nodes are used. Certainly that demands to use low cost devices with limited resources, which in turn are prone to faulty behaviour. This work introduces a novel concept for wireless sensor network configuration considering fault tolerance, energy efficiency and convenience as primary goals while being tailored to user needs. It allows to ignore low-level details like node resources, network structures, node availability etc. and enables the programmer to work on a high abstraction level, namely the event itself including event related constraints. The definition of events characterising real world phenomena is of prominent use in sensor networks. The presented concept autonomously configures and monitors events, even if it requires to organise collaboration between nodes to deliver the results. The contribution of this work is threefold. An intuitive XML-based ESL simplifies event configuration to a level that is even suitable for non-professionals. It features hardware independent description elements to define complex phenomena and enhances these by tailor-made voting schemes and application constraints. Based on that, a novel, fully decentralised mechanism to autonomously set up distributed event detection called EDT and a cost efficient means to maintain such EDT, are presented. EDTs can be efficiently constructed on every device by using a tiny generating finite state machine requiring eight states only. It enables every node to self-divide event queries according to its own resources and self-adapt to the tasks assigned. Simultaneously, the EDT provides the interface for efficient collaboration using a lease-based publish/subscribe approach. The simulations clearly show that this concept works well and the applied collaboration scheme outperforms even idealised acknowledgement-based approaches. On top of the EDT, a means is developed that enhances the reliability of detection beyond the scope of Boolean event decision. It examines behavioural trends in sensor readings to indicate the significance of actual measurements in relation to the configured event. Measured data is investigated in detail to finally attach a significance indicator "is" to each event. This automatically generated indicator shall support users or overlaying systems in decision-making. In the example scenario based on data of real test cases, the "is" indicates a flaming fire 88 seconds and a smouldering fire 48 seconds before the threshold-based method triggers the alarm.