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Broadband dielectric measurement methods based on vector network analyzer coupled with coaxial transmission line cell (CC) and open-ended coaxial probe (OC) are simply reviewed, by which the dielectric behaviors in the frequency range of 1 MHz to 3 GHz of two practical geomaterials are investigated. Kaolin after modified compaction with different water contents is measured by using CC. The results are consistent with previous study on standardized compacted kaolin and suggest that the dielectric properties at frequencies below 100 MHz are not only a function of water content but also functions of other soil state parameters including dry density. The hydration process of a commercial grout is monitored in real time by using OC. It is found that the time dependent dielectric properties can accurately reveal the different stages of the hydration process. These measurement results demonstrate the practicability of the introduced methods in determining dielectric properties of soft geomaterials.
Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification
(2020)
Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.
The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. %, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. %, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6% for ethyl ester and 3.1% for methyl ester, compared with those for the experimental data.
Water substantially affects nearly all physical, chemical and biological processes on the Earth. Recent Mars observations as well as laboratory investigations suggest that water is a key factor of current physical and chemical processes on the Martian surface, e.g. rheological phenomena. Therefore it is of particular interest to get information about the liquid-like state of water on Martian analogue soils for temperatures below 0 °C. To this end, a parallel plate capacitor has been developed to obtain isothermal dielectric spectra of fine-grained soils in the frequency range from 10 Hz to 1.1 MHz at Martian-like temperatures down to −70 °C. Two Martian analogue soils have been investigated: a Ca-bentonite (specific surface of 237 m2 g−1, up to 9.4% w / w gravimetric water content) and JSC Mars 1, a volcanic ash (specific surface of 146 m2 g−1, up to 7.4% w / w). Three soil-specific relaxation processes are observed in the investigated frequency–temperature range: two weak high-frequency processes (bound or hydrated water as well as ice) and a strong low-frequency process due to counter-ion relaxation and the Maxwell–Wagner effect. To characterize the dielectric relaxation behaviour, a generalized fractional dielectric relaxation model was applied assuming three active relaxation processes with relaxation time of the ith process modelled with an Eyring equation. The real part of effective complex soil permittivity at 350 kHz was used to determine ice and liquid-like water content by means of the Birchak or CRIM equation. There are evidence that bentonite down to −70 °C has a liquid-like water content of 1.17 monolayers and JSC Mars 1 a liquid-like water content of 1.96 monolayers.