Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning

In this paper, a hybrid online learning model that combines the fuzzy min-max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks. © 2014 Springer Science+Business Media New York.

A hybrid FMM-CART model for human activity recognition

In this paper, the application of a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) to human activity recognition is presented. The hybrid FMM-CART model capitalizes the merits of both FMM and CART in data classification and rule extraction. To evaluate the effectiveness of FMM-CART, two data sets related to human activity recognition problems are conducted. The results obtained are higher than those reported in the literature. More importantly, practical rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM- CART. This outcome positively indicates the potential of FMM- CART in undertaking human activity recognition tasks.

Microarray classification using statistical learning and decision tree algorithms

This considers the challenging task of cancer prediction based on microarray data for the medical community. The research was conducted on mostly common cancers (breast, colon, long, prostate and leukemia) microarray data analysis, and suggests the use of modern machine learning techniques to predict cancer.

Rapid determination of protein contents in microencapsulated fish oil supplements by ATR-FTIR spectroscopy and partial least square regression (PLSR) analysis

Following the recent success in quantitative analysis of essential fatty acid compositions in a commercial microencapsulated fish oil (?EFO) supplement, we extended the application of portable attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopic technique and partial least square regression (PLSR) analysis for rapid determination of total protein contents-the other major component in most commercial ?EFO powders. In contrast to the traditional chromatographic methodology used in a routine amino acid analysis (AAA), the ATR-FTIR spectra of the ?EFO powder can be acquired directly from its original powder form with no requirement of any sample preparation, making the technique exceptionally fast, noninvasive, and environmentally friendly as well as being cost effective and hence eminently suitable for routine use by industry. By optimizing the spectral region of interest and number of latent factors through the developed PLSR strategy, a good linear calibration model was produced as indicated by an excellent value of coefficient of determination R2 = 0.9975, using standard ?EFO powders with total protein contents in the range of 140-450 mg/g. The prediction of the protein contents acquired from an independent validation set through the optimized PLSR model was highly accurate as evidenced through (1) a good linear fitting (R2 = 0.9759) in the plot of predicted versus reference values, which were obtained from a standard AAA method, (2) lowest root mean square error of prediction (11.64 mg/g), and (3) high residual predictive deviation (6.83) ranked in very good level of predictive quality indicating high robustness and good predictive performance of the achieved PLSR calibration model. The study therefore demonstrated the potential application of the portable ATR-FTIR technique when used together with PLSR analysis for rapid online monitoring of the two major components (i.e., oil and protein contents) in finished ?EFO powders in the actual manufacturing setting.

Condition monitoring of induction motors : A review and an application of an ensemble of hybrid intelligent models

In this paper, a review on condition monitoring of induction motors is first presented. Then, an ensemble of hybrid intelligent models that is useful for condition monitoring of induction motors is proposed. The review covers two parts, i.e.; (i) a total of nine commonly used condition monitoring methods of induction motors; and (ii) intelligent learning models for condition monitoring of induction motors subject to single and multiple input signals. Based on the review findings, the Motor Current Signature Analysis (MCSA) method is selected for this study owing to its online, non-invasive properties and its requirement of only single input source; therefore leading to a cost-effective condition monitoring method. A hybrid intelligent model that consists of the Fuzzy Min-Max (FMM) neural network and the Random Forest (RF) model comprising an ensemble of Classification and Regression Trees is developed. The majority voting scheme is used to combine the predictions produced by the resulting FMM-RF ensemble (or FMM-RFE) members. A benchmark problem is first deployed to evaluate the usefulness of the FMM-RFE model. Then, the model is applied to condition monitoring of induction motors using a set of real data samples. Specifically, the stator current signals of induction motors are obtained using the MCSA method. The signals are processed to produce a set of harmonic-based features for classification using the FMM-RFE model. The experimental results show good performances in both noise-free and noisy environments. More importantly, a set of explanatory rules in the form of a decision tree can be extracted from the FMM-RFE model to justify its predictions. The outcomes ascertain the effectiveness of the proposed FMM-RFE model in undertaking condition monitoring tasks, especially for induction motors, under different environments. © 2014 Elsevier Ltd. All rights reserved.

Strategic approaches of corporate codes of ethics in Australia: a framework for classification and empirical illustration

Purpose - Research has so far not approached the contents of corporate code of ethics from a strategic classification point of view. Therefore, the objective of this paper is to introduce and describe a framework of classification and empirical illustration to provide insights into the strategic approaches of corporate code of ethics content within and across contextual business environments.

Design/methodology/approach - The paper summarizes the content analysis of code prescription and the intensity of codification in the contents of 78 corporate codes of ethics in Australia.

Findings - The paper finds that, generally, the studied corporate codes of ethics in Australia are of standardized and replicated strategic approaches. In particular, customized and individualized strategic approaches are far from penetrating the ethos of corporate codes of ethics content.

Research limitations/implications - The research is limited to Australian codes of ethics. Suggestions for further research are provided in terms of the search for best practice of customized and individualized corporate codes of ethics content across countries.

Practical implications - The framework contributes to an identification of four strategic approaches of corporate codes of ethics content, namely standardized, replicated, individualized and customized.

Originality/value - The principal contribution of this paper is a generic framework to identify strategic approaches of corporate codes of ethics content. The framework is derived from two generic dimensions: the context of application and the application of content. The timing of application is also a crucial generic dimension to the success or failure of codes of ethics content. Empirical illustrations based upon corporate codes of ethics in Australia's top companies underpin the topic explored.

Adolescent girls in physical education and sport : an analysis of influences on participation

This study investigates the influences on participation in physical activity of thirty adolescent girls from a metropolitan secondary school in Victoria. It seeks to understand how they perceived, experienced and explained their involvement or non involvement in both competitive and non competitive physical activity during four years of their secondary schooling. Participants experienced physical education as both a single sex group in Years 7 and 9 and a coeducational group in Years 8 and 10. They were exposed to a predominantly competitive curriculum in Years 7 to 9 and a less structured, more social, recreational program in Year 10. These experiences enabled them to compare the differences between class structures and activity programs and identify the significant issues which impacted on their participation. Large Australian population studies have revealed that fewer girls participated in sport and regular physical activity than boys. An important consequence is that girls miss out on the health benefits associated with participating in physical activity. Other research has found adolescence is the time that girls drop out of competitive sport. However, an important issue is whether girls who drop out of competitive sport cease to be involved in any physical activity. There are some studies which have reported good participation rates by adolescent girls in non competitive, recreational forms of physical activity and the possibility exists that they may drop out of competitive and into non competitive physical activity. This study primarily utilises a qualitative approach in contrast to previous studies which have largely relied upon the use of surveys and questionnaires. Whilst quantitative research has provided useful information about the bigger picture, there are limitations caused by reliance on the researchers' own interpretations of the data. Additionally there is no opportunity for any clarification and explanation of findings and trends by the respondents themselves. The current study utilized qualitative individual and collective interviews in three stages. Questions were asked in the broad areas of coeducation and single sex classes, preferences for competitive or recreational activity and body image issues. Some quantitative information focusing on nature and extent of current activity patterns was also gathered in the first stage. Thirty Year 10 girls participated in individual first interviews. Nine selected girls then took part in the second (individual) and third (collective) interview stages. Results revealed three groups based on the nature of physical activity involvement: [1] competitive activity group, [2] social activity group and [3] transition group. The transition group represented those who were in the process of withdrawing from competitive sport to take up more non competitive, recreational activity. The most significant difference between groups was skill level. On the whole those entering adolescence with the highest skill levels, such as those in the competitive group, were the most confident and relished competing against others. The social group was low in skill and confidence and had predominantly negative experiences in physical education and sport because their deficiencies were plainly visible to all. Similarly, a lack of skill improvement relative to those of 'better performers' affected the interest and confidence levels of those in the transition group. Boys' domination in coeducational classes through verbal and physical intimidation of the less competent and confident girls and exclusion of very competent girls was a major issue. Social and transition group members demonstrated compliance with boys' power by hanging back and sitting out of competitive activities. Conversely, the competitive group resisted boy's attempts to dominate but had to work hard to demonstrate their athletic capabilities in order to do so. Body image issues such as the skimpy physical education and sport uniform along with body revealing activities such as swimming and gymnastics, heightened feelings of self-consciousness and embarrassment for most girls. When strategies were adopted by social and transition group members to avoid any body exposure or physical humiliation, participation levels were subsequently affected. However, where girls felt confident about their physical abilities and body image, they were able to ignore their unflattering uniforms and thus participation was unaffected. Specific teaching practices such as giving more attention to boys, for example by segregating the sexes in mixed classes to focus attention on boys, reinforced stereotypical notions of gender and contributed to the inequities for girls in physical education. The competitive group were frustrated with having to prove themselves as capable as boys in order to receive greater teacher attention. The transition group rejected teacher's attempts to coerce them into participating in the inter school sports program. The social group believed that teachers viewed and treated them less favourably than others because of their limited skills. Girls were not passive in the face of these obstacles. Rather than give up physical activity they disengaged from competitive sport and took up other forms of activity which they had the confidence to perform. These activity choices also reflected their expanding social interests such as spending time with male and female friends outside school and increased demands on their time by study and part time work commitments. This study not only highlighted the diversity and complexity of attitudes and behaviours of girls towards physical activity but also demonstrated that they display agency in making conscious, sensible decisions about their physical activity choices. Plain Language Summary of Thesis Adolescent girls in physical education and sport; An analysis of influences on participation by Julia Whitty Submitted for the degree of Master of Applied Science Deakin University Supervisor: Dr Judy Ann Jones This study investigates the influences on participation in physical activity of thirty adolescent girls from a metropolitan secondary school in Victoria in order to understand how girls' perceived, experienced and explained their involvement or non involvement in both competitive and non competitive physical activity. Qualitative individual and collective interviews were conducted. Questions focussed on attitudes about coeducation and single sex classes, preferences for competitive or recreational activity and feelings about body image. Some quantitative information about the nature and extent of current activity patterns was also gathered in the first stage. Thirty Year 10 girls participated in individual first interviews. Nine selected girls then took part in the second (individual) and third (collective) interview stages. Results revealed three clearly different groups based on the nature of physical activity involvement (1) Competitive, (2) Social and (3) Transition (those in the process of withdrawing from competitive sport to take up more non competitive, recreational activity). The major difference between groups was skill level. Those entering adolescence with the highest skill levels were more competent and confident in the coeducational and competitive sport setting. Other significant issues included boys' domination, body image and teaching behaviours and practices.

Partial least squares and principal components analysis of wine vintage by high performance liquid chromatography with chemiluminescence detection

HPLC with acidic potassium permanganate chemiluminescence detection was employed to analyse 17 Cabernet Sauvignon wines across a range of vintages (1971–2003). Partial least squares regression analysis and principal components analysis was used in order to investigate the relationship between wine composition and vintage. Tartaric acid, vanillic acid, catechin, sinapic acid, ethyl gallate, myricetin, procyanadin B and resveratrol were found to be important components in terms of differences between the vintages.

Incorporating domain knowledge with video and voice data analysis in news broadcasts

This paper addresses the area of video annotation, indexing and retrieval, and shows how a set of tools can be employed, along with domain knowledge, to detect narrative structure in broadcast news. The initial structure is detected using low-level audio visual processing in conjunction with domain knowledge. Higher level processing may then utilize the initial structure detected to direct processing to improve and extend the initial classification.

The structure detected breaks a news broadcast into segments, each of which contains a single topic of discussion. Further the segments are labeled as a) anchor person or reporter, b) footage with a voice over or c) sound bite. This labeling may be used to provide a summary, for example by presenting a thumbnail for each reporter present in a section of the video. The inclusion of domain knowledge in computation allows more directed application of high level processing, giving much greater efficiency of effort expended. This allows valid deductions to be made about structure and semantics of the contents of a news video stream, as demonstrated by our experiments on CNN news broadcasts.

Corner Inlet and Nooramunga Habitat Mapping Project – Part 2 : High resolution mapping and change detection analysis using 2011 Rapid Eye Satellite Image compared to 2009 ALOS Satellite Image

Building on a habitat mapping project completed in 2011, Deakin University was commissioned by Parks Victoria (PV) to apply the same methodology and ground-truth data to a second, more recent and higher resolution satellite image to create habitat maps for areas within the Corner Inlet and Nooramunga Marine and Coastal Park and Ramsar area. A ground-truth data set using in situ video and still photographs was used to develop and assess predictive models of benthic marine habitat distributions incorporating data from both RapidEye satellite imagery (corrected for atmospheric and water column effects by CSIRO) and LiDAR (Light Detection and Ranging) bathymetry. This report describes the results of the mapping effort as well as the methodology used to produce these habitat maps.

Overall accuracies of habitat classifications were good, with error rates similar to or better than the earlier classification (>73 % and kappa values > 0.58 for both study localities). The RapidEye classification failed to accurately detect Pyura and reef habitat classes at the Corner Inlet locality, possibly due to differences in spectral frequencies. For comparison, these categories were combined into a ‘non-seagrass’ category, similar to the one used at the Nooramunga locality in the original classification. Habitats predicted with highest accuracies differed from the earlier classification and were Posidonia in Corner Inlet (89%), and bare sediment (no-visible seagrass class) in Nooramunga (90%). In the Corner Inlet locality reef and Pyura habitat categories were not distinguishable in the repeated classification and so were combined with bare sediments. The majority of remaining classification errors were due to the misclassification of Zosteraceae as bare sediment and vice versa. Dominant habitats were the same as those from the 2011 classification with some differences in extent. For the Corner Inlet study locality the no-visible seagrass category remained the most extensive (9059 ha), followed by Posidonia (5,513 ha) and Zosteraceae (5,504 ha). In Nooramunga no-visible seagrass (6,294 ha), Zosteraceae (3,122 ha) and wet saltmarsh (1,562 ha) habitat classes were most dominant.

Change detection analyses between the 2009 and 2011 imagery were undertaken as part of this project, following the analyses presented in Monk et al. (2011) and incorporating error estimates from both classifications. These analyses indicated some shifts in classification between Posidonia and Zosteraceae as well as a general reduction in the area of Zosteraceae. Issues with classification of mixed beds were apparent, particularly in the main Posidonia bed at Nooramunga where a mosaic of Zosteraceae and Posidonia was seen that was not evident in the ALOS classification. Results of a reanalysis of the 1998-2009 change detection illustrating effects of binning of mixed beds is also provided as an appendix.

This work has been successful in providing baseline maps at an improved level of detail using a repeatable method meaning that any future changes in intertidal and shallow water marine habitats may be assessed in a consistent way with quantitative error assessments. In wider use, these maps should also allow improved conservation planning, advance fisheries and catchment management, and progress infrastructure planning to limit impacts on the Inlet environment.

Interobserver reliability of classification and characterization of proximal humeral fractures: a comparison of two and three-dimensional CT

Interobserver reliability for the classification of proximal humeral fractures is limited. The aim of this study was to test the null hypothesis that interobserver reliability of the AO classification of proximal humeral fractures, the preferred treatment, and fracture characteristics is the same for two-dimensional (2-D) and three-dimensional (3-D) computed tomography (CT). Members of the Science of Variation Group--fully trained practicing orthopaedic and trauma surgeons from around the world--were randomized to evaluate radiographs and either 2-D CT or 3-D CT images of fifteen proximal humeral fractures via a web-based survey and respond to the following four questions: (1) Is the greater tuberosity displaced? (2) Is the humeral head split? (3) Is the arterial supply compromised? (4) Is the glenohumeral joint dislocated? They also classified the fracture according to the AO system and indicated their preferred treatment of the fracture (operative or nonoperative). Agreement among observers was assessed with use of the multirater kappa (κ) measure. Interobserver reliability of the AO classification, fracture characteristics, and preferred treatment generally ranged from "slight" to "fair." A few small but statistically significant differences were found. Observers randomized to the 2-D CT group had slightly but significantly better agreement on displacement of the greater tuberosity (κ = 0.35 compared with 0.30, p < 0.001) and on the AO classification (κ = 0.18 compared with 0.17, p = 0.018). A subgroup analysis of the AO classification results revealed that shoulder and elbow surgeons, orthopaedic trauma surgeons, and surgeons in the United States had slightly greater reliability on 2-D CT, whereas surgeons in practice for ten years or less and surgeons from other subspecialties had slightly greater reliability on 3-D CT. Proximal humeral fracture classifications may be helpful conceptually, but they have poor interobserver reliability even when 3-D rather than 2-D CT is utilized. This may contribute to the similarly poor interobserver reliability that was observed for selection of the treatment for proximal humeral fractures. The lack of a reliable classification confounds efforts to compare the outcomes of treatment methods among different clinical trials and reports.

Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach

This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference.

Rapid discrimination and determination of polyunsaturated fatty acid composition in marine oils by FTIR Spectroscopy and Multivariate Data Analysis

A rapid analytical approach for discrimination and quantitative determination of polyunsaturated fatty acid (PUFA) contents, particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), in a range of oils extracted from marine resources has been developed by using attenuated total reflection Fourier transform infrared spectroscopy and multivariate data analysis. The spectral data were collected without any sample preparation; thus, no chemical preparation was involved, but data were rather processed directly using the developed spectral analysis platform, making it fast, very cost effective, and suitable for routine use in various biotechnological and food research and related industries. Unsupervised pattern recognition techniques, including principal component analysis and unsupervised hierarchical cluster analysis, discriminated the marine oils into groups by correlating similarities and differences in their fatty acid (FA) compositions that corresponded well to the FA profiles obtained from traditional lipid analysis based on gas chromatography (GC). Furthermore, quantitative determination of unsaturated fatty acids, PUFAs, EPA and DHA, by partial least square regression analysis through which calibration models were optimized specifically for each targeted FA, was performed in both known marine oils and totally independent unknown n - 3 oil samples obtained from an actual commercial product in order to provide prospective testing of the developed models towards actual applications. The resultant predicted FAs were achieved at a good accuracy compared to their reference GC values as evidenced through (1) low root mean square error of prediction, (2) good coefficient of determination close to 1 (i.e., R 2≥ 0.96), and (3) the residual predictive deviation values that indicated the predictive power at good and higher levels for all the target FAs. © 2014 Springer Science+Business Media New York.