An efficient fuzzy deep learning approach to recognize 2D faces using FADF and ResNet-164 architecture

Deep learning using fuzzy is highly modular and more accurate. Adaptive Fuzzy Anisotropy diffusion filter (FADF) is used to remove noise from the image while preserving edges, lines and improve smoothing effects. By detecting edge and noise information through pre-edge detection using fuzzy contrast enhancement, post-edge detection using fuzzy morphological gradient filter and noise detection technique. Convolution Neural Network (CNN) ResNet-164 architecture is used for automatic feature extraction. The resultant feature vectors are classified using ANFIS deep learning. Top-1 error rate is reduced from 21.43% to 18.8%. Top-5 error rate is reduced to 2.68%. The proposed work results in high accuracy rate with low computation cost. The recognition rate of 99.18% and accuracy of 98.24% is achieved on standard dataset. Compared to the existing techniques the proposed work outperforms in all aspects. Experimental results provide better result than the existing techniques on FACES 94, Feret, Yale-B, CMU-PIE, JAFFE dataset and other state-of-art dataset.

Dutomatic Detection Technology of Intelligent Meter Based On Machine Vision

In view of the low detection rate of LCD appearance quality of smart meters in the current low contrast environment, this study puts forward the research of intelligent meters automation based on machine vision, and puts forward corresponding solutions and strategies according to this problem. Firstly, the image of the LCD area of the smart meter is extracted. On this basis, the image of the LCD screen of the ammeter is enhanced by the method of wavelet transform. Then, the LCD area of the smart meter is accurately divided. Then the morphological gradient is used to reconstruct the character information, and finally realize the automatic detection of intelligent electricity. The experimental results show that the method proposed in this paper has certain feasibility and effectiveness, improves the accuracy of appearance detection of smart meter, and has certain practical significance in power industry.

Investigation of stochastic-threshold incision models across a climatic and morphological gradient

Long-term landscape evolution is controlled by tectonic and climatic forcing acting through surfaces processes. Rivers are the main drivers of continental denudation because they set the base level of most hillslopes and the mechanisms of fluvial incision are a key focus in geomorphological research and require accurate representation and models. River incision is often modeled with the Stream Power Model (SPM), based on the along-stream evolution of drainage area and channel elevation gradient, but can also incorporate more complex processes such as threshold effects and statistical discharge dis-tributions, which are fundamental features of river dynamics. Despite their importance in quantitative geomorphology, such model formulations have been confronted with fields data only in a limited number of cases. Here we investigate the behavior of stochastic-threshold incision models across the south-eastern margin of the Massif Central in France which is characterized by significant relief and the regular occurrence of high-discharge events. Our study is based on a new dedicated dataset combining measurements of discharge variability from gauging stations, denudation rates on 34 basins from 10Be cosmogenic radionuclide (CRN) concentration measurements in river sediments, morphometric analysis of river long-profiles analysis and field observations. This new dataset is used for a systematic investigation of various formulations of the SPM and discuss the importance of incision thresholds. Denudation rates across the SE margin of the Massif Central are in the 20–120 mm/ka range and they positively correlate with slope and precipitations. However, the relationship with steepness index is complex and hints at the importance of taking into account the spatial variations in parameters controlling the SPM. Overall, the range of denudation rate across the margin can mainly be explained using a simple version of the SPM accounting for spatially heterogeneous runoff. More complex formulations including stochastic discharge and incision thresholds yield poorer performances unless spatial variations in bedload characteristics, controlling incision threshold, are taken into account. Our results highlight the importance of the hypotheses used on such threshold in SPM application to field studies and notably the impact of actual constraints on bedload size.

An abundance- and morphology-based similarity index

Classic similarity indices measure community resemblance in terms of incidence (the number of shared species) and abundance (the extent to which the shared species are an equivalently large component of the ecosystem). Here we describe a general method for increasing the amount of information contained in the output of these indices and describe a new “soft” ecological similarity measure (here called “soft Chao-Jaccard similarity”). The new measure quantifies community resemblance in terms of shared species, while accounting for intraspecific variation in abundance and morphology between samples. We demonstrate how our proposed measure can reconstruct short ecological gradients using random samples of taxa, recognizing patterns that are completely missed by classic measures of similarity. To demonstrate the utility of our new index, we reconstruct a morphological gradient driven by river flow velocity using random samples drawn from simulated and real-world data. Results suggest that the new index can be used to recognize complex short ecological gradients in settings where only information about specimens is available. We include open-source R code for calculating the proposed index.

The genus Deschampsia and the epithet “ alpina”

The epithet “alpina” has been recurrently used in the genus Deschampsia to name plants located in northern regions of Europe, Asia and North America, as a species (Deschampsia alpina (L.) Roem. & Schult.), but also in infraspecific categories (Deschampsia cespitosa subsp. alpina Tzvel. and Deschampsia cespitosa var. alpina Schur.). The morphological and molecular available evidence suggests the existence of a single species, Deschampsia cespitosa (L.) P. Beauv., in which individuals belonging to the same morphological gradient have received different names in different taxonomic categories throughout its wide distribution range. An evaluation of the available names indicates that all uses of the epithet “alpina” are illegitimate. A new combination is proposed at the infraspecific level as Deschampsia cespitosa subsp. neoalpina Chiapella, Xue & Greimler.

Photovoltaic Power Station Electromagnetic Environment Complexity Evaluation Utilizing Logarithmic Morphological Gradient Spectrum

In this paper, a feature extraction method for evaluating the complexity of the Electromagnetic Environment (EME) of the photovoltaic power station is presented by using logarithmic morphological gradient spectrum (LMGS) based on the mathematical morphological theory. We use LMGS to evaluate electromagnetic environment signals. We also explored the impact of structure element (SE) on the MS, MGS, and LMGS. Three types of SE, mean the line SE, square SE and diamond SE, are utilized and compared for computing the LMGS. EME signals with four complexity degrees are simulated to evaluate the effectiveness of the presented method. The experimental results have shown that the feature extraction scheme proposed in this paper is a reasonable method to classify the complexity of EME.