Combined neural network and reduced FRF techniques for slight damage detection using measured response data

Summary  This paper deals with structural damage detection using measured frequency response functions (FRF) as input data to artificial neural networks (ANN). A major obstacle, the impracticality of using full-size FRF data with ANNs, was circumvented by applying a data-reduction technique based on principal component analysis (PCA). The compressed FRFs, represented by their projection onto the most significant principal components, were used as the ANN input variables instead of the raw FRF data. The output is a prediction of the actual state of the specimen, i.e. healthy or damaged. A further advantage of this particular approach is its ability to deal with relatively high measurement noise, which is a common occurrence when dealing with industrial structures. The methodology was applied to detect three different states of a space antenna: reference, slight mass damage and slight stiffness damage. About 600 FRF measurements, each with 1024 spectral points, were included in the analysis. Six 2-hidden layer networks, each with an individually-optimised architecture for a specific FRF reduction level, were used for damage detection. The results showed that it was possible to distinguish between the three states of the antenna with good accuracy, subject to using an adequate number of principal components together with a suitable neural network configuration. It was also found that the quality of the raw FRF data remained a major consideration, though the method was able to filter out some of the measurement noise. The convergence and detection properties of the networks were improved significantly by removing those FRFs associated with measurement errors. Received 9 March 2000; accepted for publication 12 December 2000     

Synthesis and Crystal Structure of Mercury(II) Metal Crown Ether with N‐Heterocyclic Carbene Linkage

The mercury(II) metal crown ether ( 2a ) was obtained in high yield by reaction of the carbene precursor 1,2‐bis[N‐(1‐naphthylmethylene)imidazoliumethoxy]benzene dihexafluorophosphate ( 1 ) and Hg(OAc)₂. Addition of NaI to the acetone solution of 2a resulted in precipitation of pale yellow solid 2b . The structures of 2a and 2b were determined by single‐crystal X‐ray diffractometry. Both molecules display a helical conformation with a torsional cycle. The mercury atom in complex 2a is tricoordinated by two intramolecular carbene carbon atoms and an acetate oxygen atom. The mercury atom in complex 2b is tetracoordinated by two intramolecular carbene carbon atoms and two cis‐iodine atoms.     

All-silicon chiral metasurfaces and wavefront shaping assisted by interference

The usage of full-color imaging in digital pathology produces significant results. Compared with a grayscale image or a pseudocolor image containing contrast information, a full-color image can identify and detect the target object better with color texture information. Fourier ptychographic microscopy(FPM) is a high-throughput computational imaging technique that breaks the tradeoff between high resolution(HR) and a large field of view. It also eliminates the artifacts of scanning and stitching in digital pathology and improves its imaging efficiency. However, the conventional full-color digital pathology based on FPM is still time-consuming because of the repeated experiments with tri-wavelengths. A color transfer FPM approach termed "CFPM" was reported. The color texture information of a low-resolution full-color pathologic image is directly transferred to the HR grayscale FPM image captured by only a single wavelength. Both of the color space of FPM based on the standard CIE-XYZ color model and the display based on the standard RGB color space were established. Different FPM colorization schemes were analyzed and compared with 30 biological samples. Three types of evaluation approaches were provided, including the root-mean-square error(RMSE), the difference maps, and the image histogram cosine similarity. The average RMSE values of the conventional method and CFPM compared with the ground truth were 5.3% and 5.7%, respectively. Therefore, the reconstruction time is significantly reduced by 2/3 with the sacrifice of precision of only 0.4%. The CFPM method is also compatible with advanced fast FPM approaches to further reduce computation time.