Predicting house damage class using artificial intelligence method

The unsatisfactory performance of light structures founded on expansive soils subject to seasonal movements is frequently reported since the early 1950's in Australia. Excessive movements have caused damage to numerous structures that have not been adequately designed to accommodate soil volume changes. However, the sole presence of expansive soil is not necessarily the main cause of damage. Other factors such as vegetation, climate factors, types of construction materials and geology type may also contribute. This paper presents a model which predicts the damage class by analyzing combinations of the contributing factors using artificial intelligence methods. This model can help to identify if any serious and urgent repairs are necessary and immediate actions could be initiated without delay.

New artificial intelligence based tire size identification for fast and safe inflating cycle

Motor vehicle accidents are one of the main killers on the road. Modern vehicles have several safety features to improve the stability and controllability. The tire condition is critical to the proper function of the designed safety features. Under or over inflated tires adversely affects the stability of vehicles. It is generally the vehicle's user responsibility to ensure the tire inflation pressure is set and maintained to the required value using a tire inflator. In the tire inflator operation, the vehicle's user sets the desired value and the machine has to complete the task. During the inflation process, the pressure sensor does not read instantaneous static pressure to ensure the target value is reached. Hence, the inflator is designed to stop repetitively for pressure reading and avoid over inflation. This makes the inflation process slow, especially for large tires. This paper presents a novel approach using artificial neural network based technique to identify the tire size. Once the tire size is correctly identified, an optimized inflation cycle can be computed to improve performance, speed and accuracy of the inflation process. The developed neural network model was successfully simulated and tested for predicting tire size from the given sets of input parameters. The test results are analyzed and discussed in this paper.

Dynamic parallel job scheduling in multi-cluster computing systems

Job scheduling is a complex problem, yet it is fundamental to sustaining and improving the performance of parallel processing systems. In this paper, we address an on-line parallel job scheduling problem in heterogeneous multi-cluster computing systems. We propose a new space-sharing scheduling policy and show that it performs substantially better than the conventional policies.

Placement of file replicas in data grid environments

In this paper, we address the problem of file replica placement in Data Grids given a certain traffic pattern. We propose a new file replica placement algorithm and compare its performance with a standard replica placement algorithm using simulation. The results show that file replication improve the performance of the data access but the gains depend on several factors including where the file replicas are located, burstness of the request arrival, packet loses and file sizes.

Intelligent line segment perception with cortex-like mechanisms

This paper proposes a novel general framework for line segment perception, which is motivated by a biological visual cortex, and requires no parameter tuning. In this framework, we design a model to approximate receptive fields of simple cells. More importantly, the structure of biological orientation columns is imitated by organizing artificial complex and hypercomplex cells with the same orientation into independent arrays. Besides, an interaction mechanism is implemented by a set of self-organization rules. Enlightened by the visual topological theory, the outputs of these artificial cells are integrated to generate line segments that can describe nonlocal structural information of images. Each line segment is evaluated quantitatively by its significance. The computation complexity is also analyzed. The proposed method is tested and compared to state-of-the-art algorithms on real images with complex scenes and strong noises. The experiments demonstrate that our method outperforms the existing methods in the balance between conciseness and completeness.

Praxis modelling : an evaluation of and extension to feature based modelling

This thesis presents Relation Based Modelling as an extension to the Feature Based Modelling approach to student modelling. Relation Based Modelling dynamically creates new terms allowing the instructional designer to specify a set of primitives and operators from which the modelling system will create the necessary elements. Focal modelling is a new technique devised to manipulate and coordinate the addition of new terms. The thesis presents an evaluation of student modelling systems based on predictive accuracy.

Co-ordination in multi-agent systems

A multi-agent system is a complex software system which is composed of many relative autonomous smaller softwares called agents. The research on multi-agent systems is concerned with the interaction and coordination among these agents to let them help each other to solve complicated problems, such as finance investment management. The principal contributions represented by these 50 selected papers are "cooperation under uncertainty in distributed expert systems (DESs)", "a tool and algorithms to build DESs", and "information gathering and decision making in multi-agent systems (MASs)".

Information retrieval in documents using semantic criteria

The central problem of automatic retrieval from unformatted text is that computational devices are not adequately trained to look for associated information. However for complete understanding and information retrieval, a complete artificial intelligence would have to be built. This paper describes a method for achieving significant information retrieval by using a semantic search engine. The underlying semantic information is stored in a network of clarified words, linked by logical connections. We employ simple scoring techniques on collections of paths in this network to establish a degree of relevance between a document and a clarified search criterion. This technique has been applied with success to test examples and can be easily scaled up to search large documents.