Intelligent animal fiber classification with artificial neural networks

Artificial neural networks (ANN) are increasingly used to solve many problems related to pattern recognition and object classification. In this paper, we report on a study using artificial neural networks to classify two kinds of animal fibers: merino and mohair. We have developed two different models, one extracting nine scale parameters with image processing, and the other using an unsupervised artificial neural network to extract features automatically, which are determined in accordance with the complexity of the scale structure and the accuracy of the model. Although the first model can achieve higher accuracy, it requires more effort for image processing and more prior knowledge, since the accuracy of the ANN largely depends on the parameters selected. The second model is more robust than the first, since only raw images are used. Because only ordinary optical images taken with a microscope are employed, we can use the approach for many textile applications without expensive equipment such as scanning electron microscopy.

Using geometric shape variations to create an inverse model for a sheet metal process

The output of the sheet metal forming process is subject to much variation. This paper develops a method to measure shape variation in channel forming and relate this back to the corresponding process parameter levels of the manufacturing set-up to create an inverse model. The shape variation in the channels is measured using a modified form of the point distribution model (also known as the active shape model). This means that channels can be represented by a weighting vector of minimal linear dimension that contains all the shape variation information from the average formed channel.

The inverse models were created using classifiers that related the weighting vectors to the process parameter levels for the blank holder force (BHF), die radii (DR) and tool gap (TG) of the parameters. Several classifiers were tested: linear, quadratic Gaussian and artificial neural networks. The quadratic Gaussian classifiers were the most accurate and the most consistent type of classifier over all the parameters.

Information disclosure and corporate governance in listed companies in China: from Yinguangxia to Enron

China's path to the development of a modern securities market has not been a smooth one. This article argues that efforts to impose Western securities market models on China have been fraught with difficulty. This is especially clear from the adoption of information disclosure principles and practices. While the integrity of disclosure practices is a fundamental element in maintaining investors' confidence in securities markets, disclosure practices need to be attuned to China '5 systemic features, especially in regard to its legal structure and rules. Market failures, such as the collapse of Enron in the United States, have led to a realisation that US disclosure models have their own difficulties and that these should not be uncritically used. This article reviews recent Chinese law andpractice (using the Yinguangxia false disclosure scandal as an example) in this area and calls for the adoption of a more critical approach towards the use of Western models with particular regard to China's own distinctive pathways of reform.

Predicting worsted spinning performance with an artificial neural network model

For a given fiber spun to pre-determined yarn specifications, the spinning performance of the yarn usually varies from mill to mill. For this reason, it is necessary to develop an empirical model that can encompass all known processing variables that exist in different spinning mills, and then generalize this information and be able to accurately predict yarn quality for an individual mill. This paper reports a method for predicting worsted spinning performance with an artificial neural network (ANN) trained with backpropagation. The applicability of artificial neural networks for predicting spinning performance is first evaluated against a well established prediction and benchmarking tool (Sirolan YarnspecTM). The ANN is then subsequently trained with commercial mill data to assess the feasibility of the method as a mill-specific performance prediction tool. Incorporating mill-specific data results in an improved fit to the commercial mill data set, suggesting that the proposed method has the ability to predict the spinning performance of a specific mill accurately.

Estimating the economic value of Mount Buffalo National Park with the travel cost and contingent valuation models

The Mount Buffalo National Park is the oldest national park in Victoria, Australia. There has been a rapid increase in the number of visitors to the park during the last decade and park management has been a concern, especially in the light of declining budgetary allocations and potential damage due to the increased visitor numbers. Policy options to increase park revenue remain unclear because of a lack of information on demand parameters and user costs. This study estimates the economic value of the park using the travel cost method (TCM) and the contingent valuation method (CVM). The TCM gives higher consumer surplus (CS) than the CVM. The CS shows that the economic value of the park is high and that there are opportunities to introduce innovative fee schemes to enhance its revenue. Present entry fee systems do not capture the economic value of the park.

A conceptual approach to information warfare : security risk analysis

With information warfare (IW) becoming a reality, the need for a new security methodology to deal with the new and unique attack threats and vulnerabilities associated with the new information technology security paradigm. With the shift from computer security to information warfare, logical transformation models (LTMS) were looked at as a solution to quantifying information system requirements. The paper will introduce the concepts involved with fourth generational models and it's application to IW. The basic advantages and disadvantages will also be discussed and presented.

An improved approach for the discovery of causal models via MML

Discovering a precise causal structure accurately reflecting the given data is one of the most essential tasks in the area of data mining and machine learning. One of the successful causal discovery approaches is the information-theoretic approach using the Minimum Message Length Principle[19]. This paper presents an improved and further experimental results of the MML discovery algorithm. We introduced a new encoding scheme for measuring the cost of describing the causal structure. Stiring function is also applied to further simplify the computational complexity and thus works more efficiently. The experimental results of the current version of the discovery system show that: (1) the current version is capable of discovering what discovered by previous system; (2) current system is capable of discovering more complicated causal models with large number of variables; (3) the new version works more efficiently compared with the previous version in terms of time complexity.

The influence of collectivism in society and its implications for research on information systems and national culture

This paper argues that the nature of IS research that deals with indigenous culture must be informed as much by context as it is by culture models, which has been the focus of such research in the past. This is considered important because it better reflects the meaning of the data collected for the researcher. To appreciate the importance of context this papers also argues that research subjects from designated individualist societies will inform the researcher in different ways from those subjects located in collectivist societies. To illustrate the practical implications of this argument the paper reports three separate case studies in IS research where the researchers reflect on the impact that a collectivist view has had on the research findings. The paper suggests that (1) similar ethnicity and appearance are significant in gaining the trust of subjects in a collectivist society; that is the researcher is part of the in-group as they belong to the same culture or ethnic group; that (2) who introduced the researcher to the subject is significant in that trust is best reflected when a member of the group/collective plays an important role in the research process itself; and that (3) an ability to (a) communicate in the natural language and (b) understand the implicit body language and (c) cultural codes is important in gaining significant and more meaningful research outcomes. This is enabled via the implicit meanings embedded in members of the collectivist society.

Concepts, models and analyses of electronic demolition of buildings

The demolition of building structures produces enormous amounts of waste materials. In most current demolition projects, a great number of demolished materials are directly sent to landfill after their primary usage due to the difficulties in finding their next usage immediately. At the same time, because of limited supply of second-hand materials, new and high quality materials are used in construction projects whose design standards can be fitted using the secondary or used materials. However, this is an inefficient method to reduce waste because off the flow nature of the current waste-exchange systems and the demolition procedure. The recent concept using deconstruction rather than destruction for demolishing a constructed facility fails to achieve widespread understanding or acceptance due to various practical limitations. In this paper, for the purpose of envisaging the deconstruction implementations in practice and promoting cascading usages of construction materials, the concept of electronic demolition (e-Demotion, eDemolition) is put forward for the first time. E-demolition is a virtual demolition approach by which the demolition information, progress and outputs are operated before the physical demolition. Furthermore, the authors set up the essential models to implement electronic demolition of buildings from the viewpoints of demolition progress, business, and information. Each model is demonstrated in accord with the conventional demolition practice and subject to the ideal deconstruction implementation. Following the electronic demolition of a real project, the physical demolition can be anticipated with a minimum of construction waste emission.

CIIP-RAM - a step-wise security risk analysis methodology for critical information infrastructure protection

Wilh the protection of critical information infrastructure becoming a priority for all levels of management. there is a need for a new security methodology to deal with the new and unique attack threats and vulnerabilities associated with the new information technology security paradigm. The fourth generation security risk analysis melhod which copes wilh the shift from computer/information security to critical information iinfrastructure protectionl is lhe next step toward handling security risk at all levels. The paper will present the methodology of
fourth generation models and their application to critical information infrastructure protection and the associated advantagess of this methodology.

Unifying manufacturing simulation models using HLA

The increasing use of simulation in manufacturing has seen an increase in simulation models created using many simulation package. This use of different simulators can create simulation islands in a manufacturing factory, making it difficult to get a true simulated overview of the factory. At present, there are only a few cases where manufacturing simulations have been linked to enable multiple simulation models to run as one. This research expands upon these cases. For this paper the topic of discussion is the research in connecting different 'Commercial Off The Shelf' simulators together to allow flow of all information through the connected models using high level architecture.

Contemporaneous intra-day volume, option, and futures volatility transmissions across parallel markets

The primary objective of this article is to investigate volatility transmission across three parallel markets operating on the Sydney Futures Exchange (SFE), both within and out of sample. Half-hourly observations are sampled from transaction data for the share price index (SPI) futures, SPI futures options, and 90-day bank accepted bill (BAB) futures markets, and the analysis is carried out using the simultaneous volatility (SVL) system of equations as well as competing volatility models. The results confirm the poor ability of GARCH models to fit intraday data. This study also applies an artificial nesting procedure to evaluate the out-of-sample volatility forecasts. Implied volatility has very limited (if any) predictive power when evaluated in isolation, whereas the SVL model with implied volatility embedded provides incremental information relative to competing model forecasts.

Conceptualising best practices in cooperative industry-based learning in information systems and information technology: an inter-institutional investigation in Australian higher education

The professional fields of information systems and information technology are drivers and enablers of the global economy. Moreover, their theoretical scope and practices are global in focus. University graduates need to develop a range of leadership, conceptual and technical capacities to work effectively in, and contribute to, the shaping of companies, business models and systems which operate in globalised settings. This paper reports a study of the operation of industry-based learning (IBL) at three Australian universities, which employ different models and approaches, as part of a series of investigations of the needs, circumstances and perspectives of various stakeholders (program coordinator, faculty teaching staff, the students, industry mentors, and the professional body which has supported the most recent stage of this study). The focus of this paper is a discussion of salient pragmatic considerations as we attempt to conceptualise what constitutes best practice in offering industry-based learning for higher education students in the disciplines of information systems and information technology.

Roles, responsibilities and futures of chief information officers (CIOs) in the public sector

This paper discusses in its entirety, the CIO (Chief Information Officer) position in the public sector. Governments are increasingly adopting Information Technology for internal processes and for the delivery of service to their citizens. It includes literature on CIO roles and responsibilities which is heavily based on the private sector, due to the recognition of the role in this sector over twenty years. The position is just evolving in the public sector, and due to the context in which the public sector operates there are some similarities as well as vast differences. An evaluation of existing CIO models and theories form the basis for research on CIO roles, responsibilities and future within the public sector.

Evaluation of the robustness of predictive yield models based on catchment characteristics using GIS for reservoir fisheries in Sri Lanka

Land-use patterns in the catchment areas of Sri Lankan reservoirs, which were quantified using Geographical Information Systems (GIS), were used to develop quantitative models for yield prediction. The validity of these models was evaluated through the application to five reservoirs that were not used in the development of the models, and by comparing with the actual fish yield data of these reservoirs collected by an independent body. The robustness of the predictive models developed was tested by principal component analysis (PCA) on limnological characteristics, land-use patterns of the catchments and fish yields. The predicted fish yields in five Sri Lankan reservoirs, using the empirical models based on the ratios of forest cover and/or shrub cover to reservoir capacity or reservoir area were in close agreement with the observed fish yields. The scores of PCA ordination of productivity-related limnological parameters and those of land-use patterns were linearly related to fish yields. The relationship between the PCA scores of limnological characteristics and land-use types had the appropriate algebraic form, which substantiates the influence of the limnological factors and land-use types on reservoir fish yields. It is suggested that the relatively high predictive power of the models developed on the basis of GIS methodologies can be used for more accurate assessment of reservoir fisheries. The study supports the importance and the need for an integrated management strategy for the whole watershed to enhance fish yields.