Machine vision system for automatic inspection of surface defects in aluminium die casting

A machine vision system is presented for the automatic inspection of surface defects in aluminium die casting. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.

An email classification model based on rough set theory

The communication via email is one of the most popular services of the Internet. Emails have brought us great convenience in our daily work and life. However, unsolicited messages or spam, flood our email boxes, which results in bandwidth, time and money wasting. To this end, this paper presents a rough set based model to classify emails into three categories - spam, no-spam and suspicious, rather than two classes (spam and non-spam) in most currently used approaches. By comparing with popular classification methods like Naive Bayes classification, the error ratio that a non-spam is discriminated to spam can be reduced using our proposed model.

Numerical modeling of interactions between a macro-crack and a cluster of micro-defects

The interactions between a macro-crack and a cluster of micro-defects are studied numerically by using a series of special finite elements each containing a defect. These special finite elements, which contain defects such as holes, cracks, and inhomogeneities, are developed based on the hybrid displacement, complex potential and conformal mapping techniques. These hybrid-type elements can be used together with the conventional finite elements without any difficulty. Thus, simple finite element models can be devised to study the interactions between a macro-crack and a cluster of micro-defects. In this paper, the mathematical and finite element modeling procedures for the study of the above-mentioned problems are presented.

Cultured muscle cells display defects of mitochondrial myopathy ameliorated by anti-oxidants

The mitochondrial DNA A3243G mutation causes neuromuscular disease. To investigate the muscle-specific pathophysiology of mitochondrial disease, rhabdomyosarcoma transmitochondrial hybrid cells (cybrids) were generated that retain the capacity to differentiate to myotubes. In some cases, striated muscle-like fibres were formed after innervation with rat embryonic spinal cord. Myotubes carrying A3243G mtDNA produced more reactive oxygen species than controls, and had altered glutathione homeostasis. Moreover, A3243G mutant myotubes showed evidence of abnormal mitochondrial distribution, which was associated with down-regulation of three genes involved in mitochondrial morphology, Mfn1, Mfn2 and DRP1. Electron microscopy revealed mitochondria with ultrastructural abnormalities and paracrystalline inclusions. All these features were ameliorated by anti-oxidant treatment, with the exception of the paracrystalline inclusions. These data suggest that rhabdomyosarcoma cybrids are a valid cellular model for studying muscle-specific features of mitochondrial disease and that excess reactive oxygen species production is a significant contributor to mitochondrial dysfunction, which is amenable to anti-oxidant therapy.

Managing email overload with an automatic nonparametric clustering approach

Email overload is a recent problem that there is increasingly difficulty people have faced to process the large number of emails received daily. Currently this problem becomes more and more serious and it has already affected the normal usage of email as a knowledge management tool. It has been recognized that categorizing emails into meaningful groups can greatly save cognitive load to process emails and thus this is an effective way to manage email overload problem. However, most current approaches still require significant human input when categorizing emails. In this paper we develop an automatic email clustering system, underpinned by a new nonparametric text clustering algorithm. This system does not require any predefined input parameters and can automatically generate meaningful email clusters. Experiments show our new algorithm outperforms existing text clustering algorithms with higher efficiency in terms of computational time and clustering quality measured by different gauges.

Email categorization using multi-stage classification technique

This paper presents an innovative email categorization using a serialized multi-stage classification ensembles technique. Many approaches are used in practice for email categorization to control the menace of spam emails in different ways. Content-based email categorization employs filtering techniques using classification algorithms to learn to predict spam e-mails given a corpus of training e-mails. This process achieves a substantial performance with some amount of FP tradeoffs. It has been studied and investigated with different classification algorithms and found that the outputs of the classifiers vary from one classifier to another with same email corpora. In this paper we have proposed a multi-stage classification technique using different popular learning algorithms with an analyser which reduces the FP (false positive) problems substantially and increases classification accuracy compared to similar existing techniques.

An innovative analyser for email classification based on grey list analysis

In this paper we propose a new technique of email classification based on grey list (GL) analysis of user emails. This technique is based on the analysis of output emails of an integrated model which uses multiple classifiers of statistical learning algorithms. The GL is a list of classifier/(s) output which is/are not considered as true positive (TP) and true negative (TN) but in the middle of them. Many works have been done to filter spam from legitimate emails using classification algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection the FP problem is unacceptable, sometimes. The proposed technique will provide a list of output emails, called "grey list (GL)", to the analyser for making decisions about the status of these emails. It has been shown that the performance of our proposed technique for email classification is much better compare to existing systems, in order to reducing FP problems and accuracy.

The effect of artificial defects on the compressive properties of hollow sphere cellular aluminium (HSCA)

Hollow sphere cellular aluminium (HSCA) samples were fabricated by bonding together two kinds of single aluminium hollow spheres with the same outside diameter of 4 mm but different wall thicknesses of 0.1 mm and 0.3 mm, in which the hollow spheres with the thinner sphere wall thickness were used as artificial defects. Four types of HSCA samples with the same relative density but various distributions of artificial defects were prepared by simple cubic packing. For comparing, HSCA sample without defective hollow spheres inside was also prepared. The effects of the distribution of the artificial defects on the deformation behaviours and mechanical properties were investigated by compressive tests. Results indicated that the nominal stress - nominal strain curve and the deformation behavior of the HSCA samples varied with the distribution of the artificial defects in spite of the same relative density. It is therefore suggested that the deformation behavior and mechanical property of cellular materials were also significantly affected by the distribution of defects. In particular, the plateau stress of the HSCA samples increased with the decrease in number of contact points between the normal hollow spheres and the defective hollow spheres in the loading direction during deformation.

A controlled trial of internet-based cognitive-behavioural therapy for panic disorder with face-to-face support from a general practitioner or email support from a psychologist

Background: Panic disorder (PD) is one of the most common anxiety disorders seen in general practice, but provision of evidence-based cognitive-behavioural treatment (CBT) is rare. Many Australian GPs are now trained to deliver focused psychological strategies, but in practice this is time consuming and costly.

Objective: To evaluate the efficacy of an internet-based CBT intervention (Panic Online) for the treatment of PD supported by general practitioner (GP)-delivered therapeutic assistance.

Design: Panic Online supported by GP-delivered face-to-face therapy was compared to Panic Online supported by psychologist-delivered email therapy.

Methods: Sixty-five people with a primary diagnosis of PD (78% of whom also had agoraphobia) completed 12 weeks of therapy using Panic Online and therapeutic assistance with his/her GP (n = 34) or a clinical psychologist (n = 31). The mean duration of PD for participants allocated to these groups was 59 months and 58 months, respectively. Participants completed a clinical diagnostic interview delivered by a psychologist via telephone and questionnaires to assess panic-related symptoms, before and after treatment.

Results: The total attrition rate was 20%, with no group differences in attrition frequency. Both treatments led to significant improvements in panic attack frequency, depression, anxiety, stress, anxiety sensitivity and quality of life. There were no statistically significant differences in the two treatments on any of these measures, or in the frequency of participants with clinically significant PD at post assessment.

Conclusions: When provided with accessible online treatment protocols, GPs trained to deliver focused psychological strategies can achieve patient outcomes comparable to efficacious treatments delivered by clinical psychologists. The findings of this research provide a model for how GPs may be assisted to provide evidence-based mental healthcare successfully.

Email correspondence : a qualitative data collection tool for organisational researchers

This paper proposes a novel method for qualitative data collection in organisational research, that of email correspondence. This approach involves written communication between the researcher and each respondent, as a conversational dialogue is constructed. An overview of this method of engaging vvith respondents is provided. The author then discusses how email correspondence was used in two studies of middle managers, outlining both the benefits and challenges experienced. Lessons learned for future use of the method are also considered. Email correspondence proved a valuable tool in revealing respondents' workplace experiences, and this method provides opportunity for organisational researchers seeking to explore employees' personal reflections.

Comparison of defects in houses constructed by owners and registered builders in Victoria 1988-1996

The lack of attention to quality control by house builders in the Australian State of Victoria has been a contentious issue for more than two decades. Ina an attempt to improve the quality of housing, various mechanisms such as voluntary and compulsory registration schemes have been adopted and discarded by industry-based organisations and government. While builders are encouraged to improve construction quality, little is known and published about the quality of housing produced by owner builders specifically during the seven year warranty period after construction is completed. With this in mind, this thesis presents research findings that compare the latent defects in houses built by owner builders with those of registered builders. Using inspection reports provided by Archicentre a sample of 1772 houses, of which 1002 were owner builders and 770 were registered builders

Multi-classifier classification of spam email on an ubiquitous multi-core architecture

This paper presents an innovative fusion based multi-classifier email classification on a ubiquitous multi-core architecture. Many approaches use text-based single classifiers or multiple weakly trained classifiers to identify spam messages from a large email corpus. We build upon our previous work on multi-core by apply our ubiquitous multi-core framework to run our fusion based multi-classifier architecture. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our proposed multi-classifier based filtering system. Our proposed architecture also provides a safeguard of user mailbox from different malicious attacks. Our experimental results show that we achieved an average of 30% speedup at the average cost of 1.4 ms. We also reduced the instance of false positive, which is one of the key challenges in spam filtering system, and increases email classification accuracy substantially compared with single classification techniques.

Email categorization using (2+1)-tier classification algorithms

In this paper we have proposed a spam filtering technique using (2+1)-tier classification approach. The main focus of this paper is to reduce the false positive (FP) rate which is considered as an important research issue in spam filtering. In our approach, firstly the email message will classify using first two tier classifiers and the outputs will appear to the analyzer. The analyzer will check the labeling of the output emails and send to the corresponding mailboxes based on labeling, for the case of identical prediction. If there are any misclassifications occurred by first two tier classifiers then tier-3 classifier will invoked by the analyzer and the tier-3 will take final decision. This technique reduced the analyzing complexity of our previous work. It has also been shown that the proposed technique gives better performance in terms of reducing false positive as well as better accuracy.