A Temperature Compensation Method for Piezo-Resistive Pressure Sensor Utilizing Chaotic Ions Motion Algorithm Optimized Hybrid Kernel LSSVM

A piezo-resistive pressure sensor is made of silicon, the nature of which is considerably influenced by ambient temperature. The effect of temperature should be eliminated during the working period in expectation of linear output. To deal with this issue, an approach consists of a hybrid kernel Least Squares Support Vector Machine (LSSVM) optimized by a chaotic ions motion algorithm presented. To achieve the learning and generalization for excellent performance, a hybrid kernel function, constructed by a local kernel as Radial Basis Function (RBF) kernel, and a global kernel as polynomial kernel is incorporated into the Least Squares Support Vector Machine. The chaotic ions motion algorithm is introduced to find the best hyper-parameters of the Least Squares Support Vector Machine. The temperature data from a calibration experiment is conducted to validate the proposed method. With attention on algorithm robustness and engineering applications, the compensation result shows the proposed scheme outperforms other compared methods on several performance measures as maximum absolute relative error, minimum absolute relative error mean and variance of the averaged value on fifty runs. Furthermore, the proposed temperature compensation approach lays a foundation for more extensive research

Plug and play monitoring: developing novel solutions for marine observations using divers as citizen scientists

There is a lack of depth-resolved sea temperature data, especially in coastal areas, which are often frequently dived by SCUBA divers. Marine citizen science is a growing phenomenon, but projects involving collection of physical parameters are underrepresented. The aim of this thesis is to explore the potential for SCUBA diver citizen scientists as a novel source of marine measurements, with a focus on temperature data collected from dive computers. Current knowledge does not quantify bias, response to temperature change, or within and between model differences across models and styles of dive computer, a shortcoming this thesis addresses. The response time (time constant), accuracy and precision of water temperature measurement in 28 dive computers from 11 models, plus three underwater cameras of the same model are assessed. In addition, using a case study of a dataset of dive computer temperature from recreational divers in the Red Sea, we ascertain bias from satellite derived sea surface temperature and depth-resolved in situ data. We do so to quantify responses, and better understand the limitations and potential uses for data collected in this way. Time constant by device ranged from (17 ± 6) s to (341 ± 69) s, with significant between model differences found. When compared with baseline mean temperature from CTDs, mean bias by model ranged from (0.0 ± 0.5) °C to (-1.4 ± 2.1) °C, with 9 of the 12 models found to have accuracy ≤ 0.5 °C overall. We show that seasonal patterns comparable with regional climatologies are observable at annual, monthly and weekly resolutions in data from anonymous online dive computer logs. Interannual variation, south-north cooling trends and data biases consistent with seasonal mixed layer depths proposed in the literature are also seen. We also develop an interactive citizen science website Dive into Science (diveintoscience.org) using the Shiny package in R, detailing the development process, design decisions and key factors involved. We conclude that, with sufficient data points, temperature data from dive computers could form part of an integrated monitoring system, and there is potential for SCUBA divers to act as citizen scientists in the collection of other oceanographic parameters