Granule mining and its application for network traffic characterization

Decision table and decision rules play an important role in rough set based data analysis, which compress databases into granules and describe the associations between granules. Granule mining was also proposed to interpret decision rules in terms of association rules and multi-tier structure. In this paper, we further extend granule mining to describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other ganules, it provides a kind of novel knowledge in databases. Some experiments are conducted to test the proposed new concepts for describing the characteristics of a real network traffic data collection. The results show that the proposed concepts are promising.

Impact of region-of-interest video coding on perceived quality in mobile video

Effective streaming of video can be achieved by providing more bits to the most important region in the frame at the cost of reduced bits in the less important regions. This strategy can be beneficial for delivering high quality videos in mobile devices, especially when the availability of bandwidth is usually low and limited. While the state-of-the-art video codecs such as H.264 may have been optimised for perceived quality, it is hypothesised that users will give more attention to interesting region/object when watching videos. Therefore, giving a higher quality to region of interest (ROI)while reducing quality of other areas may result in improving the overall perceived quality without necessarily increasing the bitrate. In this paper, the impact of ROI-based encoded video on perceived quality is investigated by conducting a user study for varous target bitrates. The results from the user study demonstrate that ROI-based video coding has superior perceived quality compared to normal encoded video at the same bitrate in the lower bitrate range.

Sustainable Road Management in Texas : network-level flexible pavement structural condition analysis using data mining techniques

The research team recognized the value of network-level Falling Weight Deflectometer (FWD) testing to evaluate the structural condition trends of flexible pavements. However, practical limitations due to the cost of testing, traffic control and safety concerns and the ability to test a large network may discourage some agencies from conducting the network-level FWD testing. For this reason, the surrogate measure of the Structural Condition Index (SCI) is suggested for use. The main purpose of the research presented in this paper is to investigate data mining strategies and to develop a prediction method of the structural condition trends for network-level applications which does not require FWD testing. The research team first evaluated the existing and historical pavement condition, distress, ride, traffic and other data attributes in the Texas Department of Transportation (TxDOT) Pavement Maintenance Information System (PMIS), applied data mining strategies to the data, discovered useful patterns and knowledge for SCI value prediction, and finally provided a reasonable measure of pavement structural condition which is correlated to the SCI. To evaluate the performance of the developed prediction approach, a case study was conducted using the SCI data calculated from the FWD data collected on flexible pavements over a 5-year period (2005 – 09) from 354 PMIS sections representing 37 pavement sections on the Texas highway system. The preliminary study results showed that the proposed approach can be used as a supportive pavement structural index in the event when FWD deflection data is not available and help pavement managers identify the timing and appropriate treatment level of preventive maintenance activities.

Strategizing public sector human resource management : the implications of working network

Networks have come to the fore as a means by which government can achieve its strategic objectives, particularly when addressing complex or “wicked” issues. Such joined-up arrangements differ in their operations from other forms of organizing as they require collaborative effort to deliver the collaborative advantage. Strategic Human Resource Management is concerned with the matching of human resource practices to the strategic direction of organizations. It is argued that the strategic direction of government has been towards network involvement and that, as a result, a reconfiguration of Human Resource Management practices is needed to support this new direction. Drawing on eight network case studies findings are presented in relation to the roles government is expected to play in networks and conclusions are drawn about what types of human resource management practices would best support those roles. Implications for Strategic Human Resource Management are posited.

Optimising container transfers at multimodal terminals

The use of containers have greatly reduced handling operations at ports and at all other transfer points, thus increasing the efficiency and speed of transportation. This was done in an attempt to cut down the cost of maritime transport, mainly by reducing cargo handling and costs, and ships' time in port by speeding up handling operations. This paper discusses the major factors influencing the transfer efficiency of seaport container terminals. A network model is designed to analyse container progress in the system and applied to a seaport container terminal. The model presented here can be seen as a decision support system in the context of investment appraisal of multimodal container terminals. (C) 2000 Elsevier Science Ltd.

Assessing the representativeness of a groundwater quality monitoring network

The monitoring sites comprising a state of the environment (SOE) network must be carefully selected to ensure that they will be representative of the broader resource. Hierarchical cluster analysis (HCA) is a data-driven technique that can potentially be employed to assess the representativeness of a SOE monitoring network. The objective of this paper is to explore the use of HCA as an approach for assessing the representativeness of the New Zealand National Groundwater Monitoring Programme (NGMP), which is comprised of 110 monitoring sites across the country.

Excess power circulation in distribution networks containing distributed energy resources

The presence of large number of single-phase distributed energy resources (DERs) can cause severe power quality problems in distribution networks. The DERs can be installed in random locations. This may cause the generation in a particular phase exceeds the load demand in that phase. Therefore the excess power in that phase will be fed back to the transmission network. To avoid this problem, the paper proposes the use of distribution static compensator (DSTATCOM) that needs to be connected at the first bus following a substation. When operated properly, the DSTATCOM can facilitate a set of balanced current flow from the substation, even when excess power is generated by DERs. The proposals are validated through extensive digital computer simulation studies using PSCAD and MATLAB.

Document clustering evaluation : Divergence from a random baseline

Divergence from a random baseline is a technique for the evaluation of document clustering. It ensures cluster quality measures are performing work that prevents ineffective clusterings from giving high scores to clusterings that provide no useful result. These concepts are defined and analysed using intrinsic and extrinsic approaches to the evaluation of document cluster quality. This includes the classical clusters to categories approach and a novel approach that uses ad hoc information retrieval. The divergence from a random baseline approach is able to differentiate ineffective clusterings encountered in the INEX XML Mining track. It also appears to perform a normalisation similar to the Normalised Mutual Information (NMI) measure but it can be applied to any measure of cluster quality. When it is applied to the intrinsic measure of distortion as measured by RMSE, subtraction from a random baseline provides a clear optimum that is not apparent otherwise. This approach can be applied to any clustering evaluation. This paper describes its use in the context of document clustering evaluation.

Estimating the effects of environmental exposures using a weighted mean of monitoring stations

The health effects of environmental hazards are often examined using time series of the association between a daily response variable (e.g., death) and a daily level of exposure (e.g., temperature). Exposures are usually the average from a network of stations. This gives each station equal importance, and negates the opportunity for some stations to be better measures of exposure. We used a Bayesian hierarchical model that weighted stations using random variables between zero and one. We compared the weighted estimates to the standard model using data on health outcomes (deaths and hospital admissions) and exposures (air pollution and temperature) in Brisbane, Australia. The improvements in model fit were relatively small, and the estimated health effects of pollution were similar using either the standard or weighted estimates. Spatial weighted exposures would be probably more worthwhile when there is either greater spatial detail in the health outcome, or a greater spatial variation in exposure.

Neural network-based detection of virtual environment anomalies

The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.

Development of network-level project screening methods supporting the 4-Year pavement management plan in Texas

The Texas Department of Transportation (TxDOT) is concerned about the widening gap between pavement preservation needs and available funding. Thus, the TxDOT Austin District Pavement Engineer (DPE) has investigated methods to strategically allocate available pavement funding to potential projects that improve the overall performance of the District and Texas highway systems. The primary objective of the study presented in this paper is to develop a network-level project screening and ranking method that supports the Austin District 4-year pavement management plan development. The study developed candidate project selection and ranking algorithms that evaluated pavement conditions of each project candidate using data contained in the Pavement Management Information system (PMIS) database and incorporated insights from Austin District pavement experts; and implemented the developed method and supporting algorithm. This process previously required weeks to complete, but now requires about 10 minutes including data preparation and running the analysis algorithm, which enables the Austin DPE to devote more time and resources to conducting field visits, performing project-level evaluation and testing candidate projects. The case study results showed that the proposed method assisted the DPE in evaluating and prioritizing projects and allocating funds to the right projects at the right time.

Colour coding the fridge to reduce food waste

This paper introduces the first iteration of a study aimed at grouping similar food types together in a refrigerator to increase the awareness of available foods for consumers in a domestic environment. The goals of the project are twofold: i) Raise the awareness of available foods for all members of a household; ii) Reduce the amount of expired food waste in the household. The project implemented a paper-based colour scheme in refrigerators in households, assigning colours to particular food types (e.g. green to fruit and vegetables, red to meat, etc.). The findings show that the colour coding raised participants’ awareness of available food items in the fridge, particularly for those participants who were not directly involved in the shopping and initial storage of each food item. The findings also indicate that such awareness led to a reduction in expiration of food and thus general food waste in the household. These preliminary findings suggest that raising awareness of food availability through categorisation and efficient communication of this information may lead to a reduction in food waste in domestic environments.