Sensor Selection in Arbitrary Dimensions

We address the sensor selection problem which arises in tracking and localization applications. In sensor selection, the goal is to select a small number of sensors whose measurements provide a good estimate of a target's state (such as location). We focus on the bounded uncertainty sensing model where the target is a point in the d -dimensional Euclidean space. Each sensor measurement corresponds to a convex polyhedral subset of the space. The measurements are merged by intersecting corresponding sets. We show that, on the plane, four sensors are sufficient (and sometimes necessary) to obtain an estimate whose area is at most twice the area of the best possible estimate (obtained by intersecting all measurements). We also extend this result to arbitrary dimensions and show that a constant number of sensors suffice for a constant factor approximation in arbitrary dimensions. Both constants depend on the dimensionality of the space but are independent of the total number of sensors in the network.     

Integrating trust into the CyberCraft Initiative via the Trust Vectors model [Application Notes]

This research supports the hypothesis that the Trust Vector model can be modified to fit the CyberCraft Initiative, and that there are limits to the utility of historical data. This research proposed some modifications and expansions to the Trust Model Vector, and identified areas for future research.     

Evolving artificial neural network ensembles

Using a coordinated group of simple solvers to tackle a complex problem is not an entirely new idea. Its root could be traced back hundreds of years ago when ancient Chinese suggested a team approach to problem solving. For a long time, engineers have used the divide-and-conquer strategy to decompose a complex problem into simpler sub-problems and then solve them by a group of solvers. However, knowing the best way to divide a complex problem into simpler ones relies heavily on the available domain knowledge. It is often a manual process by an experienced engineer. There have been few automatic divide-and-conquer methods reported in the literature. Fortunately, evolutionary computation provides some of the interesting avenues to automatic divide-and-conquer methods. An in-depth study of such methods reveals that there is a deep underlying connection between evolutionary computation and ANN ensembles. Ideas in one area can be usefully transferred into another in producing effective algorithms. For example, using speciation to create and maintain diversity had inspired the development of negative correlation learning for ANN ensembles, and an in-depth study of diversity in ensembles. This paper will review some of the recent work in evolutionary approaches to designing ANN ensembles.     

Reliability assessment of an automated distribution system

Automation can greatly enhance distribution-network reliability by speeding up service restoration and thus significantly reduce customer-outage time. The paper presents an approach to assess quantitatively the adequacy of a particular automated distribution scheme designated as the `low interruption system' (LIS). Owing to the use of a high-speed communication system and line sensors, this automated scheme can reduce drastically the number of interruptions, the service interruption time and also the area affected by the fault. This scheme provides a simple and cost-effective way to automate distribution systems in which the remotely controlled switches speed up isolation of faulted sections and the restoration of healthy sections through alternative routes. The step-by-step calculation procedure is presented using a typical small automated distribution system. The proposed technique is then applied to a larger distribution system to examine the effectiveness of the technique and also to examine the level of reliability improvement achieved by automation     

Learning user preferences of route choice behaviour for adaptive route guidance

As the use of navigation systems becomes more widespread, the demand for advanced functions of navigation systems also increases. In the light of user satisfaction, personalisation of route guidance by incorporating user preferences is one of the most desired features. A user model applied to personalised route guidance is presented. The user model adaptively updates route selection rules when it discovers the predicted choice differs from the actual choice of the driver. This study employs a decision tree learning algorithm, the C4.5 algorithm, which has advantages over other data mining methods in terms of its comprehensible model structure. Simulation experiments with a real-world network were conducted to analyse the applicability of the model to adaptive route guidance and the accuracy of its prediction     

Algorithms of Ant System and Simulated Annealing for the p-median Problem

Consideration was given to the p-median problem for minimum in the integer formulation which is known to be NP-hard. Variants of the algorithms of ant system and simulated annealing were proposed for it, and the results of computer experiments were analyzed.     

Guest Editors' Introduction: Overview of Sensor Networks

Wireless sensor networks could advance many scientific pursuits while providing a vehicle for enhancing various forms of productivity, including manufacturing, agriculture, construction, and transportation.     

Phase change process with free convection in a circular enclosure: numerical simulations

A numerical study of unsteady natural convection flow during freezing of water in a circular enclosure is presented. Mathematical model for phase change is based on apparent capacity method formulation and the governing equations are discretized on a fixed grid by means of finite element method. Water’s temperature is initially higher than its freezing temperature. Then, the temperature of the enclosure’s boundary is dropped to a temperature lower than freezing temperature. Ice forms at the enclosure boundary while natural convection flow is induced in the liquid region. Calculations have been made for the rate of change of solid fraction and temperature distributions, for conduction and conduction plus convection modes of heat transfer, and density inversion near freezing temperature phenomenon of water is considered. High resolution capturing of solid/liquid moving boundary as well as the details of flow structure is presented. The results indicate that the effect of natural convection is dominant over conduction if the Rayleigh number is higher than 5 × 10⁶ and relatively insignificant if the Rayleigh number is less than 1 × 10⁶.     

The speed of mouse-click as a measure of anxiety during human-computer interaction

The monitoring of the human-computer interaction process is one of the essential aspects in the evaluation and enhancement of both task and affective outcome of human-computer interaction. However, although objective measures exist for task outcome, most affective measures are subjective. This study represented an investigation into the speed of mouse-click as a possible measure in human-computer interaction, and was based principally on the suggestions that a relationship exists between stress and motor activities involved in the operation of the fingers. Two groups of 30 subjects were exposed to different sets of human-computer interaction conditions, and the speed of mouse-click and state anxiety were examined. No correlation was found between the speed of mouse-click and state anxiety. However, a significant difference was found in the speed of mouse-click between the groups and the different human-computer interaction situations. The implication of these findings and the possible advantages of using the computer mouse to collect data relating to the computer user's covert state during human-computer interaction are discussed.     

Stable social foraging swarms in a noisy environment

Bacteria, bees, and birds often work together in groups to find food. A group of robots can be designed to coordinate their activities to search for and collect objects. Networked cooperative uninhabited autonomous vehicles are being developed for commercial and military applications. Suppose that we refer to all such groups of entities as "social foraging swarms". In order for such multiagent systems to succeed it is often critical that they can both maintain cohesive behaviors and appropriately respond to environmental stimuli (e.g., by optimizing the acquisition of nutrients in foraging for food). In this paper, we characterize swarm cohesiveness as a stability property and use a Lyapunov approach to develop conditions under which local agent actions will lead to cohesive foraging even in the presence of "noise" characterized by uncertainty on sensing other agent's position and velocity, and in sensing nutrients that each agent is foraging for. The results quantify earlier claims that social foraging is in a certain sense superior to individual foraging when noise is present, and provide clear connections between local agent-agent interactions and emergent group behavior. Moreover, the simulations show that very complicated but orderly group behaviors, reminiscent of those seen in biology, emerge in the presence of noise.