An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

The objective of this study was to design and produce highly comfortable shoe products guided by a plantar pressure imaging data-set. Previous studies have focused on the geometric measurement on the size of the plantar, while in this research a plantar pressure optical imaging data-set based classification technology has been developed. In this paper, an improved local binary pattern (LBP) algorithm is used to extract texture-based features and recognize patterns from the data-set. A calculating model of plantar pressure imaging feature area is established subsequently. The data-set is classified by a neural network to guide the generation of various shoe-last surfaces. Firstly, the local binary mode is improved to adapt to the pressure imaging data-set, and the texture-based feature calculation is fully used to accurately generate the feature point set; hereafter, the plantar pressure imaging feature point set is then used to guide the design of last free surface forming. In the presented experiments of plantar imaging, multi-dimensional texture-based features and improved LBP features have been found by a convolution neural network (CNN), and compared with a 21-input-3-output two-layer perceptual neural network. Three feet types are investigated in the experiment, being flatfoot (F) referring to the lack of a normal arch, or arch collapse, Talipes Equinovarus (TE), being the front part of the foot is adduction, calcaneus varus, plantar flexion, or Achilles tendon contracture and Normal (N). This research has achieved an 82% accuracy rate with 10 hidden-layers CNN of rotation invariance LBP (RI-LBP) algorithm using 21 texture-based features by comparing other deep learning methods presented in the literature

Study of Intermolecular Interaction between Small Molecules and Carbon Nanobelt: Electrostatic, Exchange, Dispersive and Inductive Forces

The conjugated structure of carbon is used in chemical sensing and small molecule catalysis because of its high charge transfer ability, and the interaction between carbon materials and small molecules is the main factor determining the performance of sensing and catalytic reactions. In this work, Reduced Density Gradient (RDG) and Symmetry-Adapted Perturbation Theory (SAPT) energy decomposition methods were used in combination to investigate the heterogeneity of catalytic substrates commonly used in energy chemistry with [6, 6] the carbon nanobelt ([6, 6] CNB, the interaction properties and mechanisms inside and outside the system). The results show that most of the attractive forces between dimers are provided by dispersive interactions, but electrostatic interactions cannot be ignored either. The total energy of the internal adsorption of [6, 6] CNB was significantly smaller than that of external adsorption, which led to the small molecules being more inclined to adsorb in the inner region of [6, 6] CNB. The dispersive interactions of small molecules adsorbed on [6, 6] CNB were also found to be very high. Furthermore, the dispersive interactions of the same small molecules adsorbed inside [6, 6] CNB were significantly stronger than those adsorbed outside. In [6, 6] CNB dimers, dispersion played a major role in the mutual attraction of molecules, accounting for 70% of the total attraction

Classification, identification, and reservoir characteristics of intermediate mafic lava flows: a case study in Dongling area, Songliao Basin

Intermediate mafic lava is a special oil and gas reservoir. While its internal structure is an important factor affecting the reservoir properties, the identification of facies and understanding of the relationship between facies architecture and reservoir are limited. This study evaluated the intermediate mafic lava flows of the Yingcheng Formation in the Dongling area of Songliao Basin by analyzing drilling cores, corresponding thin sections, and scanning electron microscope (SEM) images, as well as well-logging and seismic attributes. We also performed helium gas experiments and high-pressure mercury intrusion (HPMI) analysis to assess the physical properties and pore structure of the reservoir, respectively. The results showed that intermediate mafic lava flows develop tabular lava flow, compound lava flow, and hyaloclastite. Three facies showed present diverse well-logging and seismic responses. The intermediate mafic lava facies architecture was divided into crater-proximal facies (CF-PF), medial facies (MF), and distal facies (DF), which were characterized by their vesicles and joints and could be identified through their seismic attributes. The reservoir spaces including vesicles, amygdale inner pores, joint fissures, and dissolution pores predominantly showed oil and gas accumulation. The results of the tests of the reservoir’s physical properties showed that the reservoir quality was best in the CF-PF, which is the main target of oil and gas exploration

Histogram of oriented gradient based plantar pressure image feature extraction and classification employing fuzzy support vector machine

To identify and design comfortable footwear for patients with diabetic conditions (e.g. high blood pressure), an experimental design was proposed to collect plantar pressure data-set using a RSscan sensor system. The data-set acquired by the pressure sensor was reformed into images to allow for image analysis technologies to be applied. In this paper, image features were extracted including color of Hue-Saturation-Value (HSV), gray difference based features (mean, entropy and auto-correlation function), gray-level co-occurrence matrix based features (energy and correlation) and Histogram of Oriented Gradient (HOG). The features were normalized into a high dimensional vector applied to a Fuzzy Support Vector Machine (FSVM), and finally, the FSVM was trained and used for prediction of diabetic plantar pressure images. Normal features and HOG were compared in different classifiers including SVM, LSVM and FSVM. HOG with normal features of image for FSVM performed with a higher accuracy classification effectiveness (84.3%) than the current state of the art. The proposed methods have clear applications in revealing the key zone of foot plantar of diabetics and offer a new direction in producing comfortable diabetic footwear