A versatile microfadometer for lightfastness testing and pigment identification

The design and experimental method for the use of a novel instrument for lightfastness measurements on artwork is presented. The new microfadometer design offers increased durability and portability over the previous, published design, broadening the scope of locations at which data can be acquired. This reduces the need for art handling or transportation in order to gain evidence-based risk assessments for the display of light-sensitive artworks. The instrument focuses a stabilized high powered xenon lamp to a spot 0.25 millimeters (FWHM) while simultaneously monitoring color change. This makes it possible to identify pigments and determine the lightfastness of materials effectively and non-destructively. With 2.59mW or 0.82 lumens (1.7 x107 lux for a 0.25mm focused spot) the instrument is capable of fading Blue Wool 1 to a measured 11 ΔEab value (using CIE standard illuminant D65) in 15 minutes. The temperature increase created by focused radiation was measured to be 3 to 4°C above room temperature. The system was stable within 0.12 ΔEab over 1 hour and 0.31 ΔEab over 7 hours. A safety evaluation of the technique is discussed which concludes that some caution should be employed when fading smooth, uniform areas of artworks. The instrument can also incorporate a linear variable filter. This enables the researcher to identify the active wavebands that cause certain degradation reactions and determine the degree of wavelength dependence of fading. Some preliminary results of fading experiments on Prussian blue samples from the paint box of J. M. W Turner (1755-1851) are presented

Photochemical colour change for traditional watercolour pigments in low oxygen levels

An investigation for light exposure on pigments in low-oxygen environments (in the range 0–5% oxygen) was conducted using a purpose-built automated microfadometer for a large sample set including multiple samples of traditional watercolour pigments from nineteenth-century and twentieth-century sources, selected for concerns over their stability in anoxia. The pigments were prepared for usage in watercolour painting: ground and mixed in gum Arabic and applied to historically accurate gelatine glue-sized cotton and linen-based papers. Anoxia benefited many colorants and no colorant fared worse in anoxia than in air, with the exception of Prussian blue and Prussian green (which contains Prussian blue). A Prussian blue sampled from the studio materials of J.M.W. Turner (1775 − 1851) was microfaded in different environments (normal air (20.9% oxygen) 0, 1, 2, 3.5, or 5% oxygen in nitrogen) and the subsequent dark behaviour was measured. The behaviour of the sample (in normal air, anoxia, and 5% oxygen in nitrogen) proved to be consistent with the 55 separately sourced Prussian blue samples. When exposed to light in 5% oxygen in nitrogen, Prussian blue demonstrated the same light stability as in air (at approximately 21°C and 1 atmosphere). Storage in 5% oxygen is proposed for ‘anoxic’ display of paper-based artworks that might contain Prussian blue, to protect this material while reducing light-induced damage to other components of a watercolour, including organic colorants and the paper support

Development of portable microfading spectrometers for measurement of light sensitivity of materials

Microfading was originally designed for efficiently detecting extremely light-sensitive materials on objects in situ to determine the appropriate exhibition lighting conditions. By focusing an intense beam of light to a tiny submillimetre sized spot and simultaeously monitoring the colour change over time, the fading rate of the material can be measured without producing noticeable damage. The increased intensity of light allows rapid determination of light-fastness of materials. This paper examines an improved design of microfading spectrometer that is easy to assemble, compact, robust, capable of fully automatic acquisition of data with precision control of the fading time to produce higher precision measurements and to allow simultaneous monitoring of colour, spectral reflectance and other changes in real time. The effects of various parameters such as thickness and concentration of paint layer, the binding medium and substrate on the fading rates are examined for selected pigments and found that in certain cases substrates, binding media and thickness can affect the fading rate. Reciprocity in the context of microfading compared with realistic exhibition conditions is examined and found that it breaks down for some pigment

Correlated EEMD and effective feature extraction for both periodic and irregular faults diagnosis in rotating machinery

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Intelligent fault diagnosis of complex machinery is crucial for industries to reduce the maintenance cost and to improve fault prediction performance. Acoustic signal is an ideal source for diagnosis because of its inherent characteristics in terms of being non-directional and insensitive to structural resonances. However, there are also two main drawbacks of acoustic signal, one of which is the low signal to noise ratio (SNR) caused by its high sensitivity and the other one is the low computational efficiency caused by the huge data size. These would decrease the performance of the fault diagnosis system. Therefore, it is significant to develop a proper feature extraction method to improve computational efficiency and performance in both periodic and irregular fault diagnosis. To enhance SNR of the acquired acoustic signal, the correlation coefficient (CC) method is employed to eliminate the redundant intrinsic mode functions (IMF), which comes from the decomposition procedure of pre-processing known as ensemble empirical mode decomposition (EEMD), because the higher the correlated coefficient of an IMF is, the more significant fault signatures it would contain, and the redundant IMF would compromise both the SNR and the computational cost performance. Singular value decomposition (SVD) and sample Entropy (SampEn) are subsequently used to extract the fault feature, by exploiting their sensitivities to irregular and periodic fault signals, respectively. In addition, the proposed feature extraction method using sparse Bayesian based pairwise coupled extreme learning machine (PC-SBELM) outperforms the existing pairwise-coupling probabilistic neural network (PC-PNN) and pairwise-coupling relevance vector machine (PC-RVM) by 1.8%and 2%, respectively, to achieve an accuracy of 93.9%. The experiments conducted on the periodic and irregular faults in the gears and bearings have demonstrated that the proposed hybrid fault diagnosis system is effective

Comparison of depth-averaged concentration and bed load flux sediment transport models of dam-break flow

This paper presents numerical simulations of dam-break flow over a movable bed. Two different mathematical models were compared: a fully coupled formulation of shallow water equations with erosion and deposition terms (a depth-averaged concentration flux model), and shallow water equations with a fully coupled Exner equation (a bed load flux model). Both models were discretized using the cell-centered finite volume method, and a second-order Godunov-type scheme was used to solve the equations. The numerical flux was calculated using a Harten, Lax, and van Leer approximate Riemann solver with the contact wave restored (HLLC). A novel slope source term treatment that considers the density change was introduced to the depth-averaged concentration flux model to obtain higher-order accuracy. A source term that accounts for the sediment flux was added to the bed load flux model to reflect the influence of sediment movement on the momentum of the water. In a one-dimensional test case, a sensitivity study on different model parameters was carried out. For the depth-averaged concentration flux model, Manning's coefficient and sediment porosity values showed an almost linear relationship with the bottom change, and for the bed load flux model, the sediment porosity was identified as the most sensitive parameter. The capabilities and limitations of both model concepts are demonstrated in a benchmark experimental test case dealing with dam-break flow over variable bed topography

Wave propagation speeds and source term influences in single and integral porosity shallow water equations

In urban flood modeling, so-called porosity shallow water equations (PSWEs), which conceptually account for unresolved structures, e.g., buildings, are a promising approach to addressing high CPU times associated with state-of-the-art explicit numerical methods. The PSWE can be formulated with a single porosity term, referred to as the single porosity shallow water model (SP model), which accounts for both the reduced storage in the cell and the reduced conveyance, or with two porosity terms: one accounting for the reduced storage in the cell and another accounting for the reduced conveyance. The latter form is referred to as an integral or anisotropic porosity shallow water model (AP model). The aim of this study was to analyze the differences in wave propagation speeds of the SP model and the AP model and the implications of numerical model results. First, augmented Roe-type solutions were used to assess the influence of the source terms appearing in both models. It is shown that different source terms have different influences on the stability of the models. Second, four computational test cases were presented and the numerical models were compared. It is observed in the eigenvalue-based analysis as well as in the computational test cases that the models converge if the conveyance porosity in the AP model is close to the storage porosity. If the porosity values differ significantly, the AP model yields different wave propagation speeds and numerical fluxes from those of the BP model. In this study, the ratio between the conveyance and storage porosities was determined to be the most significant parameter

Sorption of lead from aqueous solutions by spent tea leaf

An adsorbent was prepared using spent tea leaf and was used to remove lead (Pb) from solution. The Pb removal by the spent tea leaf adsorbent depended on pretreatment of spent tea leaf, adsorption contact time and adsorbent dosage. The optimum pretreatment conditions were confirmed to be that tea leaf was ground to 0.28-0.45 mm in diameter and then drenched in 0.3 M NaOH for 12 h. Adsorption kinetic study showed that the adsorption of Pb onto the spent tea leaf followed the pseudo-secondordermodel. The adsorption was time dependent and adsorbent dosage dependent. The optimum contact time was 8 h

Multi-Fault Rapid Diagnosis for Wind Turbine Gearbox Using Sparse Bayesian Extreme Learning Machine

© 2013 IEEE. In order to reduce operation and maintenance costs, reliability, and quick response capability of multi-fault intelligent diagnosis for the wind turbine system are becoming more important. This paper proposes a rapid data-driven fault diagnostic method, which integrates data pre-processing and machine learning techniques. In terms of data pre-processing, fault features are extracted by using the proposed modified Hilbert-Huang transforms (HHT) and correlation techniques. Then, time domain analysis is conducted to make the feature more concise. A dimension vector will then be constructed by including the intrinsic mode function energy, time domain statistical features, and the maximum value of the HHT marginal spectrum. On the other hand, as the architecture and the learning algorithm of pairwise-coupled sparse Bayesian extreme learning machine (PC-SBELM) are more concise and effective, it could identify the single- and simultaneous-fault more quickly and precisely when compared with traditional identification techniques such as pairwise-coupled probabilistic neural networks (PC-PNN) and pairwise-coupled relevance vector machine (PC-RVM). In this case study, PC-SBELM is applied to build a real-time multi-fault diagnostic system. To verify the effectiveness of the proposed fault diagnostic framework, it is carried out on a real wind turbine gearbox system. The evaluation results show that the proposed framework can detect multi-fault in wind turbine gearbox much faster and more accurately than traditional identification techniques

Gender reflections on social crisis : experience with "SARS" in Hong Kong

2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Optimization of fermentation medium for nisin production from Lactococcus lactis subsp. lactis using response surface methodology (RSM) combined with artificial neural network-genetic algorithm (ANN-GA)

Nisin is a bacteriocin approved in more than 50 countries as a safe natural food preservative. Response surface methodology (RSM) combined with artificial neural network-genetic algorithm (ANN-GA) was employed to optimize the fermentation medium for nisin production. Plackett-Burman design (PBD) was used for identifying the significant components in the fermentation medium. After that, the path of steepest ascent method (PSA) was employed to approach their optimal concentrations. Sequentially, Box-Behnken design experiments were implemented for further optimization. RSM combined with ANNGA were used for analysis of data. Specially, a RSM model was used for determining the individual effect and mutual interaction effect of tested variables on nisin titer (NT), an ANN model was used for NT prediction, and GA was employed to search for the optimum solutions based on the ANN model. As the optimal medium obtained by ANN-GA was located at the verge of the test region, a further Box- Behnken design based on the RSM statistical analysis results was implemented. ANN-GA was implemented using the further Box-Behnken design data to locate the optimum solution which was as follow (g/l): Glucose (GLU) 15.92, peptone (PEP) 30.57, yeast extraction powder (YEP) 39.07, NaCl 5.25, KH2PO4 10.00, and MgSO4·7H2O 0.20, with expected NT of 22216 IU/ml. The validation experiments with the optimum solution were implemented in triplicate and the average NT was 21423 IU/ml, which was 2.13 times higher than that without ANN-GA methods and 8.34 times higher than that without optimization.Key words: Response surface methodology, artificial neural network, genetic algorithm, nisin titer