A sparse representation based fast detection method for surface defect detection of bottle caps

A practical machine-vision-based system is developed for fast detection of defects occurring on the surface of bottle caps. This system can be used to extract the circular region as the region of interests (ROI) from the surface of a bottle cap, and then use the circular region projection histogram (CRPH) as the matching features. We establish two dictionaries for the template and possible defect, respectively. Due to the requirements of high-speed production as well as detecting quality, a fast algorithm based on a sparse representation is proposed to speed up the searching. In the sparse representation, non-zero elements in the sparse factors indicate the defect's size and position. Experimental results in industrial trials show that the proposed method outperforms the orientation code method (OCM) and is able to produce promising results for detecting defects on the surface of bottle caps.

A theoretical assessment of surface defect machining and hot machining of nanocrystalline silicon carbide

In this paper, a newly proposed machining method named “surface defect machining” (SDM) [Wear, 302, 2013 (1124-1135)] was explored for machining of nanocrystalline beta silicon carbide (3C-SiC) at 300K using MD simulation. The results were compared with isothermal high temperature machining at 1200K under the same machining parameters, emulating ductile mode micro laser assisted machining (µ-LAM) and with conventional cutting at 300 K. In the MD simulation, surface defects were generated on the top of the (010) surface of the 3C-SiC work piece prior to cutting, and the workpiece was then cut along the <100> direction using a single point diamond tool at a cutting speed of 10 m/sec. Cutting forces, sub-surface deformation layer depth, temperature in the shear zone, shear plane angle and friction coefficient were used to characterize the response of the workpiece. Simulation results showed that SDM provides a unique advantage of decreased shear plane angle which eases the shearing action. This in turn causes an increased value of average coefficient of friction in contrast to the isothermal cutting (carried at 1200 K) and normal cutting (carried at 300K). The increase of friction coefficient however was found to aid the cutting action of the tool due to an intermittent dropping in the cutting forces, lowering stresses on the cutting tool and reducing operational temperature. Analysis shows that the introduction of surface defects prior to conventional machining can be a viable choice for machining a wide range of ceramics, hard steels and composites compared to hot machining.

Enabling ultra high precision on hard steels using surface defect machining

This paper is an extension to an idea coined during the 13th EUSPEN Conference (P6.23) named "surface defect machining" (SDM). The objective of this work was to demonstrate how a conventional CNC turret lathe can be used to obtain ultra high precision machined surface finish on hard steels without recourse to a sophisticated ultra precision machine tool. An AISI 4340 hard steel (69 HRC) workpiece was machined using a CBN cutting tool with and without SDM. Post-machining measurements by a Form Talysurf and a Scanning Electron Microscope (FEI Quanta 3D) revealed that SDM culminates to several key advantages (i) provides better quality of the machined surface integrity and offers (ii) lowering feed rate to 5μm/rev to obtain a machined surface roughness of 30 nm (optical quality).

Shared Pseudomonas aeruginosa genotypes are common in Australian cystic fibrosis centres

Recent molecular-typing studies suggest cross-infection as one of the potential acquisition pathways for Pseudomonas aeruginosa in patients with cystic fibrosis (CF). In Australia, there is only limited evidence of unrelated patients sharing indistinguishable P. aeruginosa strains. We therefore examined the point-prevalence, distribution, diversity and clinical impact of P. aeruginosa strains in Australian CF patients nationally. 983 patients attending 18 Australian CF centres provided 2887 sputum P. aeruginosa isolates for genotyping by enterobacterial repetitive intergenic consensus-PCR assays with confirmation by multilocus sequence typing. Demographic and clinical details were recorded for each participant. Overall, 610 (62%) patients harboured at least one of 38 shared genotypes. Most shared strains were in small patient clusters from a limited number of centres. However, the two predominant genotypes, AUST-01 and AUST-02, were widely dispersed, being detected in 220 (22%) and 173 (18%) patients attending 17 and 16 centres, respectively. AUST-01 was associated with significantly greater treatment requirements than unique P. aeruginosa strains. Multiple clusters of shared P. aeruginosa strains are common in Australian CF centres. At least one of the predominant and widespread genotypes is associated with increased healthcare utilisation. Longitudinal studies are now needed to determine the infection control implications of these findings.

A Lightweight Tool for Anomaly Detection in Cloud Data Centres

Cloud data centres are critical business infrastructures and the fastest growing service providers. Detecting anomalies in Cloud data centre operation is vital. Given the vast complexity of the data centre system software stack, applications and workloads, anomaly detection is a challenging endeavour. Current tools for detecting anomalies often use machine learning techniques, application instance behaviours or system metrics distribu- tion, which are complex to implement in Cloud computing environments as they require training, access to application-level data and complex processing. This paper presents LADT, a lightweight anomaly detection tool for Cloud data centres that uses rigorous correlation of system metrics, implemented by an efficient corre- lation algorithm without need for training or complex infrastructure set up. LADT is based on the hypothesis that, in an anomaly-free system, metrics from data centre host nodes and virtual machines (VMs) are strongly correlated. An anomaly is detected whenever correlation drops below a threshold value. We demonstrate and evaluate LADT using a Cloud environment, where it shows that the hosting node I/O operations per second (IOPS) are strongly correlated with the aggregated virtual machine IOPS, but this correlation vanishes when an application stresses the disk, indicating a node-level anomaly.

Advances in the surface defect machining (SDM) of hard steels

This paper reports the realisation of precision surface finish (Ra 30 nm) on AISI 4340 steel using a conventional turret lathe by adapting and incorporating a surface defect machining (SDM) method [Wear, 302, 2013 (1124-1135)]. Conventional ways of machining materials are limited by the use of a critical feed rate, experimentally determined as 0.02 mm/rev, beyond which no appreciable improvement in the machined quality of the surface is obtained. However, in this research, the novel application of an SDM method was used to overcome this minimum feed rate limitation ultimately reducing it to 0.005 mm/rev and attaining an average machined surface roughness of 30 nm. From an application point of view, such a smooth finish is well within the values recommended in the ASTM standards for total knee joint prosthesis. Further analysis was done using SEM imaging, white light interferometry and numerical simulations to verify that adapting SDM method provides improved surface integrity by reducing the extent of side flow, microchips and weldments during the hard turning process.

The VINEYARD Approach: Versatile, Integrated, Accelerator-Based, Heterogeneous Data Centres.

Emerging web applications like cloud computing, Big Data and social networks have created the need for powerful centres hosting hundreds of thousands of servers. Currently, the data centres are based on general purpose processors that provide high flexibility buts lack the energy efficiency of customized accelerators. VINEYARD aims to develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by employing typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). This programming framework will, further, allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer high flexibility and energy efficiency. VINEYARD will foster the expansion of the soft-IP core industry, currently limited in the embedded systems, to the data-centre market. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases (a) a bio-informatics application for high-accuracy brain modeling, (b) two critical financial applications, and (c) a big-data analysis application.