Image processing and pattern recognition in textiles

Image processing and pattern recognition have been successfully applied in many textile related areas. For example, they have been used in defect detection of cotton fibers and various fabrics. In this work, the application of image processing into animal fiber classification is discussed. Integrated into / with artificial neural networks, the image processing technique has provided a useful tool to solve complex problems in textile technology. Three different approaches are used in this work forfiber classification and pattern recognition: feature extraction with image process, pattern recognition and classification with artificial neural networks, and feature recognition and classification with artificial neural network. All of them yieldssatisfactory results by giving a high level of accuracy in classification.

Special issue : Computational intelligence models for image processing and information reasoning

Computational Intelligence (CI) models comprise robust computing methodologies with a high level of machine learning quotient. CI models, in general, are useful for designing computerized intelligent systems/machines that possess useful characteristics mimicking human behaviors and capabilities in solving complex tasks, e.g., learning, adaptation, and evolution. Examples of some popular CI models include fuzzy systems, artificial neural networks, evolutionary algorithms, multi-agent systems, decision trees, rough set theory, knowledge-based systems, and hybrid of these models. This special issue highlights how different computational intelligence models, coupled with other complementary techniques, can be used to handle problems encountered in image processing and information reasoning.

Industrial application of thermal image processing and thermal control

Industrial application of infrared thermography is virtually boundless as it can be used in any situations where there are temperature differences. This technology has particularly been widely used in automotive industry for process evaluation and system design. In this work, thermal image processing technique will be introduced to quantitatively calculate the heat stored in a warm/hot object and consequently, a thermal control system will be proposed to accurately and actively manage the thermal distribution within the object in accordance with the heat calculated from the thermal images.

Visions of the virtual : the role of computers and artificial intelligence in a selection of Australian young adult fiction

Advances in computer technology over the last twenty years have resulted in a number of different visions of what it means to be real, and of what it means to be human. This paper will explore how computers and artificial intelligence are used as major themes in four Australian novels written for young adults: Gillian Rubinstein’s Space Demons trilogy — comprising Space Demons, Skymaze and Shinkei — and Michael Pryor’s The Mask of Caliban. In so doing, the paper will look at how these texts explore the relationship between increasingly developed technology and visions of a better world. By comparing a series of oppositions that occur in all four books, this paper will look at how the theme of technology is used to privilege particular values and to advocate particular beliefs.

An artificial intelligence examination of the influence of geological conditions and changes in climate on damage to light structures in Victoria

Damage to light structures in the state of Victoria can be caused by movements of expansive soils. The presentation will present the results of an examination of reports of increasing complaints of house damage in Victoria and particularly in the Melbourne area. The examination analyses the influence of geology and change in climate using Neural Network and Genetic Algorithm approaches and assesses their relative importance in contributing to the cause of the damage.

On the automated interpretation and indexing of American football

This work combines natural language understanding and image processing with incremental learning to develop a system that can automatically interpret and index American Football. We have developed a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain. Our representation combines expert knowledge, domain knowledge, spatial knowledge and temporal knowledge. We also present an incremental learning algorithm to improve the knowledge base as well as to keep previously developed concepts consistent with new data. The advantages of the incremental learning algorithm are that is that it does not split concepts and it generates a compact conceptual hierarchy which does not store instances.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

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.

Emotional intelligence and teaching : further validation evidence

Further evidence is presented to demonstrate the validity of a new measure of emotional intelligence: Reactions to Teaching Situations (RTS). Using criterion-related groups of high and low scorers on the RTS, it is shown that high scorers give more responses coded as emotional intelligence in their answers to sentence completion tasks relating to ten situations found in teaching. The questions of convergent and discriminant validity is tackled by examination of correlations of emotional intelligence scores and scores on the Multiple Intelligences Checklist for Adults (MICA) and information processing preferences as measured by the Myers-Briggs Type Indicator (MBTI). The results confirm that emotional intelligence (as assessed by the RTS) bears significant relationships to both intrapersonal and interpersonal intelligences and also to linguistic intelligence, but emotional intelligence shows no significant relationships with the MBTI preferences.

3-dimensional characterization of membrane with nanoporous structure using TEM tomography and image analysis

The nanoporous structure of membrane varies in 3-dimensional (3-D) space and has remarkable influences on the filtration or desalination achieved, fouling potentials and therefore, the quality of yielded water. Knowledge of the 3-D nanoporous structure is thus vital to understanding and predicting its performance. A novel method by incorporating transmission electronic microtomography, image processing and 3-D reconstruction is introduced to characterize membranes with nano structures. The reconstruction algorithm allows for the visualization of 3-D nanoporous structure in a non-destructive way from any directions. This novel technique Ieads to in-depth understanding and accurate prediction of filtration performance.

3-dimensional characterization of membrane with nanoporous structure using TEM tomography and image analysis

The nanoporous structure of a membrane varies in a 3-dimensional (3-D) space and has remarkable influences on the filtration or desalination achieved, fouling potentials and therefore, the quality of yielded water. Knowledge of the 3-D nanoporous structure is thus vital to understanding and predicting its performance. A novel method by incorporating transmission electronic microtomography, image processing and 3-D reconstruction is introduced to characterize membranes with nano structures. The reconstruction algorithm allows for the visualization of 3-D nanoporous structure in a non-destructive way from any directions. This novel technique leads to in-depth understanding and accurate prediction of filtration performance.