Online motor fault detection and diagnosis using a hybrid FMM-CART model

In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.

Detection on application layer DDoS using random walk model

Application Layer Distributed Denial of Service (ALDDoS) attacks have been increasing rapidly with the growth of Botnets and Ubiquitous computing. Differentiate to the former DDoS attacks, ALDDoS attacks cannot be efficiently detected, as attackers always adopt legitimate requests with real IP address, and the traffic has high similarity to legitimate traffic. In spite of that, we think, the attackers' browsing behavior will have great disparity from that of the legitimate users'. In this paper, we put forward a novel user behavior-based method to detect the application layer asymmetric DDoS attack. We introduce an extended random walk model to describe user browsing behavior and establish the legitimate pattern of browsing sequences. For each incoming browser, we observe his page request sequence and predict subsequent page request sequence based on random walk model. The similarity between the predicted and the observed page request sequence is used as a criterion to measure the legality of the user, and then attacker would be detected based on it. Evaluation results based on real collected data set has demonstrated that our method is very effective in detecting asymmetric ALDDoS attacks. © 2014 IEEE.

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.

GEAM: a general and event-related aspects model for Twitter event detection

Event detection on Twitter has become a promising research direction due to Twitter's popularity, up-to-date feature, free writing style and so on. Unfortunately, it's a challenge to analyze Twitter dataset for event detection, since the informal expressions of short messages comprise many abbreviations, Internet buzzwords, spelling mistakes, meaningless contents etc. Previous techniques proposed for Twitter event detection mainly focus on clustering bursty words related to the events, while ignoring that these words may not be easily interpreted to clear event descriptions. In this paper, we propose a General and Event-related Aspects Model (GEAM), a new topic model for event detection from Twitter that associates General topics and Event-related Aspects with events. We then introduce a collapsed Gibbs sampling algorithm to estimate the word distributions of General topics and Event-related Aspects in GEAM. Our experiments based on over 7 million tweets demonstrate that GEAM outperforms the state-of-the-art topic model in terms of both Precision and DERate (measuring Duplicated Events Rate detected). Particularly, GEAM can get better event representation by providing a 4-tuple (Time, Locations, Entities, Keywords) structure of the detected events. We show that GEAM not only can be used to effectively detect events but also can be used to analyze event trends. © 2013 Springer-Verlag.

Fault detection in a cold forging process through feature extraction with a neural network

This paper investigates the application of neural networks to the recognition of lubrication defects typical to an industrial cold forging process employed by fastener manufacturers. The accurate recognition of lubrication errors, such as coating not being applied properly or damaged during material handling, is very important to the quality of the final product in fastener manufacture. Lubrication errors lead to increased forging loads and premature tool failure, as well as to increased defect sorting and the re-processing of the coated rod. The lubrication coating provides a barrier between the work material and the die during the drawing operation; moreover it needs be sufficiently robust to remain on the wire during the transfer to the cold forging operation. In the cold forging operation the wire undergoes multi-stage deformation without the application of any additional lubrication. Four types of lubrication errors, typical to production of fasteners, were introduced to a set of sample rods, which were subsequently drawn under laboratory conditions. The drawing force was measured, from which a limited set of features was extracted. The neural network based model learned from these features is able to recognize all types of lubrication errors to a high accuracy. The overall accuracy of the neural network model is around 98% with almost uniform distribution of errors between all four errors and the normal condition.

Effect of hydrogen/air flow rates and scan rate on the flame ionisation detection response of phopholipids, and their quantitative and qualitative analysis by Iatroscan (Mark‐6s) TLC‐FID

The combined effect of scan speed, hydrogen and air flow rates on the flame ionization detection (FID) peak response of phospholipid classes has been studied to determine the optimum levels of these parameters. The phospholipid composition of different types of commercial lecithins, as well as lecithins combined with fish oils, has been analyzed by Iatroscan TLC‐FID Mark‐6s under optimized conditions. An air flow rate of 2 L/min, a hydrogen flow rate of 150–160 mL/min, and a scan speed of 30 s/rod seem to be the ideal conditions for scanning phospholipids with complete pyrolysis in the flame in the Mark‐6 model. Increasing the scan speed rapidly decreased the FID response. A hydrogen flow rate as high as 170 mL/min could be used at relatively low air flow rates (&#x003C2 L/min) and the response declined when both air flow rate and hydrogen flow rate increased simultaneously. Both linear and curvilinear relationships had highly significant correlations (p&#x003C0.01) with the sample load. Time course reactions, including the hydrolysis of phosphatidylserine using enzymes, can be successfully monitored by the Iatroscan TLC‐FID Chromarod system.

Options for change in the Australian cervical cancer screening program in context of HPV detection and vaccination

Introduction:
Cervical cancer screening has been implemented for over a decade in Australia and has significantly reduced the mortality and morbidity of the disease. The emergence of new technologies for cervical cancer, such as the Human Papillomavirus (HPV) vaccine and DNA testing has encouraged debate regarding the effective use of resources in cervical cancer prevention. The present study evaluates the cost-effectiveness, from a health sector perspective, of various screening strategies in the era of these new technologies.

Methods:
A stochastic epidemiological model using a discrete event and continuous algorithm was developed to describe the natural history of cervical cancer. By allowing one member of the cohort into the model at a time, this micro-simulation model encompasses the characteristics of heterogeneity and can track individual life histories. To evaluate the cost-effectiveness of the HPV vaccine a Markov model was built to simulate the effect on the incidence of HPV and subsequent cervical cancer. A number of proposed screening strategies were evaluated with the stochastic model for the application of HPV DNA testing, with changes in the screening interval and target population. Health outcomes were measured by Disability-Adjusted Life-Years (DALYs), adjusted for application within an evaluation setting (i.e. the mortality component of the DALY was adjusted by a disability weight when early mortality due to cervical cancer is avoided). Costs in complying with the Australian updated guidelines were assessed by pathway analysis to estimate the resources associated with cervical cancer and its pre-cancerous lesion treatment. Sensitivity analyses were performed to investigate the key parameters that influenced the cost-effectiveness results.

Results:
Current practice has already brought huge health gain by preventing more than 4,000 deaths and saving more than 86,000 life-years in a cohort of a million women. Any of the alternative screening strategies alter the total amount of health gain by a small margin compared to current practice. The results of incremental analyses of the alternative screening strategies compared to current practice suggest the adoption of the HPV DNA test as a primary screening tool every 3 years commencing at age 18, or the combined pap smear/HPV test every 3 years commencing at age 25, are more costly than current practice but with reasonable ICERs (AUD$1,810 per DALY and AUD$18,600 per DALY respectively). Delaying commencement of Pap test screening to age 25 is less costly than current practice, but involves considerable health loss. The sensitivity analysis shows, however, that the screening test accuracy has a significant impact on these conclusions. Threshold analysis indicates that a sensitivity ranging from 0.80 to 0.86 for the combined test in women younger than 30 is required to produce an acceptable incremental cost-effectiveness ratio.

Conclusions:
The adoption of HPV and combined test with an extended screening interval is more costly but affordable, resulting in reasonable ICERs. They appear good value for money for the Australian health care system, but need more information on test accuracy to make an informed decision. Potential screening policy change under current Australian HPV Vaccination Program is current work in progress. A Markov model is built to simulate the effect on the incidence of HPV and subsequent cervical cancer. Adoption of HPV DNA test as a primary screening tool in the context of HPV vaccination is under evaluation.

Chaos theory based detection against network mimicking DDoS attacks

DDoS attack traffic is difficult to differentiate from legitimate network traffic during transit from the attacker, or zombies, to the victim. In this paper, we use the theory of network self-similarity to differentiate DDoS flooding attack traffic from legitimate self-similar traffic in the network. We observed that DDoS traffic causes a strange attractor to develop in the pattern of network traffic. From this observation, we developed a neural network detector trained by our DDoS prediction algorithm. Our preliminary experiments and analysis indicate that our proposed chaotic model can accurately and effectively detect DDoS attack traffic. Our approach has the potential to not only detect attack traffic during transit, but to also filter it.

A microscopic competition model and its dynamics analysis on network attacks

Modeling network traffic has been a critical task in the development of Internet. Attacks and defense are prevalent in the current Internet. Traditional network models such as Poisson-related models do not consider the competition behaviors between the attack and defense parties. In this paper, we present a microscopic competition model to analyze the dynamics among the nodes, benign or malicious, connected to a router, which compete for the bandwidth. The dynamics analysis demonstrates that the model can well describe the competition behavior among normal users and attackers. Based on this model, an anomaly attack detection method is presented. The method is based on the adaptive resonance theory, which is used to learn the model by normal traffic data. The evaluation shows that it can effectively detect the network attacks.

Knowledge on road information in sub-urban lane detection via multiple cue integration

Detection of lane boundaries of a road based on the images or video taken by a video capturing device in a suburban environment is a challenging task. In this paper, a novel lane detection algorithm is proposed without considering camera parameters; which robustly detects lane boundaries in real-time especially for sub-urban roads. Initially, the proposed method fits the CIE L*a*b* transformed road chromaticity values (that is a* and b* values) to a bi-variate Gaussian model followed by the classification of road area based on Mahalanobis distance. Secondly, the classified road area acts as an arbitrary shaped region of interest (AROI) in order to extract blobs resulting from the filtered image by a two dimensional Gabor filter. This is considered as the first cue of images. Thirdly, another cue of images was employed in order to obtain an entropy image. Moreover, results from the color based image cue and entropy image cue were integrated following an outlier removing process. Finally, the correct road lane points are fitted with Bezier splines which act as control points that can form arbitrary shapes. The algorithm was implemented and experiments were carried out on sub-urban roads. The results show the effectiveness of the algorithm in producing more accurate lane boundaries on curvatures and other objects on the road.

Conjugated polymer−titania nanoparticle hybrid films : random lasing action and ultrasensitive detection of explosive vapors

We have first demonstrated that a random laser action generated by a hybrid film composed of a semiconducting organic polymer (SOP) and TiO₂ nanoparticles can be used to detect 2,4,6-trinitrotoluene (TNT) vapors. The hybrid film was fabricated by spin-casting SOP solution dispersed with nanosized TiO₂ particles on quartz glass. The SOP in the hybrid film functioned as both the gain medium and the sensory transducer. A random lasing action was observed with a certain pump power when the size (diameter of 50 nm) and concentration (8.9 - 10¹²/cm³) of TiO₂ nanoparticles were optimized. Measurements of fluorescence quenching behavior of the hybrid film in TNT vapor atmosphere (10 ppb) showed that attenuated lasing in optically pumped hybrid film displayed a sensitivity to vapors of explosives more than 20 times higher than was observed from spontaneous emission. This phenomenon has been explained with the four-level laser model. Since the sensory transducer used in the hybrid polymer/nanoparticles system could be replaced by other functional materials, the concept developed could be extended to more general domains of chemical or environment detection.

Pedestrian detection for mobile bus surveillance

In this paper, we present a system for pedestrian detection involving scenes captured by mobile bus surveillance cameras in busy city streets. Our approach integrates scene localization, foreground and background separation, and pedestrian detection modules into a unified detection framework. The scene localization module performs a two stage clustering of the video data. In the first stage, SIFT Homography is applied to cluster frames in terms of their structural similarities and second stage further clusters these aligned frames in terms of lighting. This produces clusters of images which are differential in viewpoint and lighting. A kernel density estimation (KDE) method for colour and gradient foreground-background separation are then used to construct background model for each image cluster which is subsequently used to detect all foreground pixels. Finally, using a hierarchical template matching approach, pedestrians can be identified. We have tested our system on a set of real bus video datasets and the experimental results verify that our system works well in practice.

Adaptive model for foreground extraction in adverse lighting conditions

Background elimination models are widely used in motion tracking systems. Our aim is to develop a system that performs reliably under adverse lighting conditions. In particular, this includes indoor scenes lit partly or entirely by diffuse natural light. We present a modified "median value" model in which the detection threshold adapts to global changes in illumination. The responses of several models are compared, demonstrating the effectiveness of the new model.