Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

Flow pattern analysis in a highly stenotic patient-specific carotid bifurcation model using a turbulence model

The aim of this study is to investigate the blood flow pattern in carotid bifurcation with a high degree of luminal stenosis, combining in vivo magnetic resonance imaging (MRI) and computational fluid dynamics (CFD). A newly developed two-equation transitional model was employed to evaluate wall shear stress (WSS) distribution and pressure drop across the stenosis, which are closely related to plaque vulnerability. A patient with an 80% left carotid stenosis was imaged using high resolution MRI, from which a patient-specific geometry was reconstructed and flow boundary conditions were acquired for CFD simulation. A transitional model was implemented to investigate the flow velocity and WSS distribution in the patient-specific model. The peak time-averaged WSS value of approximately 73Pa was predicted by the transitional flow model, and the regions of high WSS occurred at the throat of the stenosis. High oscillatory shear index values up to 0.50 were present in a helical flow pattern from the outer wall of the internal carotid artery immediately after the throat. This study shows the potential suitability of a transitional turbulent flow model in capturing the flow phenomena in severely stenosed carotid arteries using patient-specific MRI data and provides the basis for further investigation of the links between haemodynamic variables and plaque vulnerability. It may be useful in the future for risk assessment of patients with carotid disease.

Pattern visual evoked potentials in dyslexic versus normal children

Purpose: Presence of neurophysiological abnormalities in dyslexia has been a conflicting issue. This study was performed to evaluate the role of sensory visual deficits in the pathogenesis of dyslexia. Methods: Pattern visual evoked potentials (PVEP) were recorded in 72 children including 36 children with dyslexia and 36 children without dyslexia (controls) who were matched for age, sex and intelligence. Two check sizes of 15 and 60 min of arc were used with temporal frequencies of 1.5 Hz for transient and 6 Hz for steady‑state methods. Results: Mean latency and amplitude values for 15 min arc and 60 min arc check sizes using steady state and transient methods showed no significant difference between the two study groups (P values: 0.139/0.481/0.356/0.062).Furthermore, no significant difference was observed between two methods of PVEPs in dyslexic and normal children using 60min arc with high contrast(Pvalues: 0.116, 0.402, 0.343 and 0.106). Conclusion: The sensitivity of PVEP has high validity to detect visual deficits in children with dyslexic problem. However, no significant difference was found between dyslexia and normal children using high contrast stimuli.

Susceptibility variants for male-pattern baldness on chromosome 20p11

We carried out a genome-wide association study in 296 individuals with male-pattern baldness (androgenetic alopecia) and 347 controls. We then investigated the 30 best SNPs in an independent replication sample and found highly significant association for five SNPs on chromosome 20p11 (rs2180439 combined P = 2.7 x 10(-15)). No interaction was detected with the X-chromosomal androgen receptor locus, suggesting that the 20p11 locus has a role in a yet-to-be-identified androgen-independent pathway.

Shrinking Violets

Description of the work Shrinking Violets is comprised of two half scale garments in laser cut silk organza, developed with a knotting device to allow for disassembly and reassembly. The first is a jacket in layered red organza including black storm flap details. The second is a vest in jade organza with circles of pink organza attached through a pattern of knots. Research Background This practice-led fashion design research sits within the field of Design for Sustainability (DfS) in fashion that seeks to mitigate the environmental and ethical impacts of fashion consumption and production. The research explores new systems of garment construction for DfS, and examines how these systems may involve ‘designing’ new user interactions with the garments. The garments’ construction system allows them to be disassembled and recycled or reassembled by users to form a new garment. Conventional garment design follows a set process of cutting and construction, with pattern pieces permanently machine-stitched together. Garments typically contain multiple fibre types; for example a jacket may be constructed from a shell of wool/polyester, an acetate lining, fusible interlinings, and plastic buttons. These complex inputs mean that textile recycling is highly labour intensive, first to separate the garment pieces and second to sort the multiple fibre types. This difficulty results in poor quality ‘shoddy’ comprised of many fibre types and unsuitable for new apparel, or in large quantities of recyclable textile waste sent to landfill (Hawley 2011). Design-led approaches that consider the garment’s end of life in the design process are a way of addressing this problem. In Gulich’s (2006) analysis, use of single materials is the most effective way to ensure ease of recycling, with multiple materials that can be detached next in effectiveness. Given the low rate of technological innovation in most apparel manufacturing (Ruiz 2011), a challenge for effective recycling is how to develop new manufacturing methods that allow for garments to be more easily disassembled at end-of-life. Research Contribution This project addresses the research question: How can design for disassembly be considered within the fashion design process? I have employed a practice-led methodology in which my design process leads the research, making use of methods of fashion design practice including garment and construction research, fabric and colour research, textile experimentation, drape, patternmaking, and illustration as well as more recent methods such as laser cutting. Interrogating the traditional approaches to garment construction is necessarily a technical process; however fashion design is as much about the aesthetic and desirability of a garment as it is about the garment’s pragmatics or utility. This requires a balance between the technical demands of designing for disassembly with the aesthetic demands of fashion. This led to the selection of luxurious, semi-transparent fabrics in bold floral colours that could be layered to create multiple visual effects, as well as the experimentation with laser cutting for new forms of finishing and fastening the fabrics together. Shrinking Violets makes two contributions to new knowledge in the area of design for sustainability within fashion. The first is in the technical development of apparel modularity through the system of laser cut holes and knots that also become a patterning device. The second contribution lies in the design of a system for users to engage with the garment through its ability to be easily reconstructed into a new form. Research Significance Shrinking Violets was exhibited at the State Library of Queensland’s Asia Pacific Design Library, 1-5 November 2015, as part of The International Association of Societies of Design Research’s (IASDR) biannual design conference. The work was chosen for display by a panel of experts, based on the criteria of design innovation and contribution to new knowledge in design. References Gulich, B. (2006). Designing textile products that are easy to recycle. In Y. Wang (Ed.), Recycling in Textiles (pp. 25-37). London: Woodhead. Hawley, J. M. (2011). Textile recycling options: exploring what could be. In A. Gwilt & T. Rissanen (Eds.), Shaping Sustainable Fashion: Changing the way we make and use clothes (pp. 143 - 155). London: Earthscan. Ruiz, B. (2014). Global Apparel Manufacturing. Retrieved 10 August 2014, from http://clients1.ibisworld.com/reports/gl/industry/default.aspx?entid=470

Using multi-label classification for acoustic pattern detection and assisting bird species surveys

Acoustics is a rich source of environmental information that can reflect the ecological dynamics. To deal with the escalating acoustic data, a variety of automated classification techniques have been used for acoustic patterns or scene recognition, including urban soundscapes such as streets and restaurants; and natural soundscapes such as raining and thundering. It is common to classify acoustic patterns under the assumption that a single type of soundscapes present in an audio clip. This assumption is reasonable for some carefully selected audios. However, only few experiments have been focused on classifying simultaneous acoustic patterns in long-duration recordings. This paper proposes a binary relevance based multi-label classification approach to recognise simultaneous acoustic patterns in one-minute audio clips. By utilising acoustic indices as global features and multilayer perceptron as a base classifier, we achieve good classification performance on in-the-field data. Compared with single-label classification, multi-label classification approach provides more detailed information about the distributions of various acoustic patterns in long-duration recordings. These results will merit further biodiversity investigations, such as bird species surveys.