Optimization of electrostatic sensor for velocity measurement based on particle swarm optimization technique

Electrostatic sensors are broadly applied to measure velocity of solid particles in many industries because controlling the velocity particles improves product quality and process efficiency. These sensors are selected due to their robust design and being economically viable. Optimization of different electrode sizes and shapes of these sensors is required to find the ideal electrodes associated with maximum spatial sensitivity and minimum statistical error. Uniform spatial sensitivity is a crucial factor because it would lead to increase similarity between the measured correlation velocity and true mean particle velocity. This thesis proposes a new method to optimize different parameters of electrodes for electrostatic sensors. This technique identified characteristics of the electrostatic sensor in a MATLAB code called Particle Swarm Optimization (PSO). A mathematical model of various electrodes to compute spatial sensitivity and statistical error was applied to extract geometric size information of electrodes to detect suitable equations. To validate the proposed method, different values of electrode designs were applied in experimental tests conducted in a laboratory to measure the velocity of solid particles. The experimental results were optimized through Response Surface Methodology (RSM), an optimization technique for experimentation. The optimized results showed that spatial sensitivity of circular-ring electrode is more uniform in comparison to the other electrodes. The optimal length of circular-ring electrode was between 0.577 cm and 0.600 cm. In addition, the best thickness for the electrodes was between 0.475 cm and 0.500 cm. A close agreement between optimization and experimentation verifies that the proposed method is feasible to optimize physical sizes of electrostatic sensor electrodes. These results provide a significant basis of the effect of geometric dimensions on the sensing characteristics of electrostatic sensors

Enhancing Software Project Outcomes: Using Machine Learning and Open Source Data to Employ Software Project Performance Determinants

Many factors can influence the ongoing management and execution of technology projects. Some of these elements are known a priori during the project planning phase. Others require real-time data gathering and analysis throughout the lifetime of a project. These real-time project data elements are often neglected, misclassified, or otherwise misinterpreted during the project execution phase resulting in increased risk of delays, quality issues, and missed business opportunities. The overarching motivation for this research endeavor is to offer reliable improvements in software technology management and delivery. The primary purpose is to discover and analyze the impact, role, and level of influence of various project related data on the ongoing management of technology projects. The study leverages open source data regarding software performance attributes. The goal is to temper the subjectivity currently used by project managers (PMs) with quantifiable measures when assessing project execution progress. Modern-day PMs who manage software development projects are charged with an arduous task. Often, they obtain their inputs from technical leads who tend to be significantly more technical. When assessing software projects, PMs perform their role subject to the limitations of their capabilities and competencies. PMs are required to contend with the stresses of the business environment, the policies, and procedures dictated by their organizations, and resource constraints. The second purpose of this research study is to propose methods by which conventional project assessment processes can be enhanced using quantitative methods that utilize real-time project execution data. Transferability of academic research to industry application is specifically addressed vis-à-vis a delivery framework to provide meaningful data to industry practitioners