Evolutionary Optimisation of Distributed Energy Resources
Genetic optimisation is used to minimise operational costs across a system of electrical loads and generators controlled by local intelligent agents and connected to the electricity grid at market rates. Experimental results in a simulated environment show that coordinated market-sensitive behaviours are achieved. A large network of 500 loads and generators, each characterised by different randomly selected parameters, was optimised using a two-stage genetic algorithm to achieve scalability.

title: Critical Damage Reporting in Intelligent Sensor Networks
abstract: In this paper, we present a Top-Down/Bottom-Up (TDBU) design approach for critical damage reporting in intelligent sensor networks This approach is a minimal hierarchical decomposition of the problem, which seeks a balance between achievability and complexity Our simulated environment models two-dimensional square cells as autonomous agents which sense their local environment, reporting critical damage as rapidly as possible to a report delivery site (portal) by using only the adjacent-cell communication links The global goal is to design agent properties which will allow the multi-agent network to detect critical damage anywhere on the network and to communicate this information to a portal whose location is unknown to the agents We apply a TDBU approach together with genetic algorithms (GA) to address the global goal Simulations show that our system can successfully report critical damage much better than random methods.

title: mSSL: extending SSL to support data sharing among collaborative clients
abstract: Client-server applications often do not scale well when a large number of clients access a single server. To solve this, a new trend is to allow a client to download data from other peer clients, in addition to from the server directly. This paradigm, which we call the hybrid peer-to-peer paradigm, is friendly to the server's scalability, but also faces new security challenges. For example, how can the server authenticate its clients and support data confidentiality? How can a client trust the data downloaded from other clients? What if a client refuses to acknowledge the service it received or overstates the service it offered? In this paper, we present a protocol, called mSSL, that provides a set of security functions to enable secure sharing of the data of a server among its clients. In addition to access control and confidentiality support, mSSL provides an original design on supporting data integrity and proof of service in this new context. Our evaluation further shows that mSSL has a reasonable overhead

title: Context-based multiscale classification of document images using wavelet coefficient distributions
abstract: In this paper, an algorithm is developed for segmenting document images into four classes: background, photograph, text, and graph. Features used for classification are based on the distribution patterns of wavelet coefficients in high frequency bands. Two important attributes of the algorithm are its multiscale nature-it classifies an image at different resolutions adaptively, enabling accurate classification at class boundaries as well as fast classification overall-and its use of accumulated context information for improving classification accuracy.

title: The Application of Bayesian Optimization and Classifier Systems in Nurse Scheduling
abstract: Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person’s assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

title: Online estimation of trifocal tensors for augmenting live video
abstract: We propose a method to augment live video based on the tracking of natural features, and the online estimation of the trinocular geometry. Previous without-marker approaches require the computation of camera pose to render virtual objects. The strength of our proposed method is that it doesn 7 require tracking of camera pose, and exploits the usual advantages of marker-based approaches for a fast implementation. A 3-view AR system is used to demonstrate our approach. It consists of an uncalibrated camera that moves freely inside the scene of interest, and of three reference frames taken at the time of system initialization. As the camera is moving, image features taken from an initial triplet set are tracked throughout the video sequence. And the trifocal tensor associated with each frame is estimated online. With this tensor, the square pattern that was visible in the reference frames is transferred to the video. This invisible pattern is then used by the ARToolkit to embed virtual objects.

title: Incorporating Legacy System into Semantic Grid Framework
abstract: The semantic grid is an extension of the current grid. Resources description framework (RDF) can be used to extend the current grid into semantic grid in which information and services are given well-defined meaning. This paper proposed an approach to recognizing the reusable components from the legacy system and representing these components by using XML and RDF, which could be performed for incorporating legacy system components into the semantic grid framework