International Conference on Research in Robotics and Automation Engineering

This study presents the outcomes of a project-oriented bootcamp designed to promote a high-performance learning and development environment, allowing sharing of ideas and knowledge in practical and active fashioning. The bootcamp foundations are based on project development and management technologies (Scrum, Kanban) coupled with artificial intelligence and systemic constellation, which we call the Digital Constellation® framework. The study was carried out with 11 students from a High School and Technical Institution located in the municipality of Florianópolis, Brazil. The participants were divided into three groups and challenged to develop a digital application from ideation to deployment over four months. By applying the proposed framework, it was possible to analyze the perception of the participants regarding their expectations, their experience with the working team, and, with the applications being developed, serving as an input for continuous improvement. An improvement in collaborative attitude and co-responsibility indicators was observed, represented by the quadratic growth model (r² = 0.80). Also, a remarkable reduction in anxiety-related terms (Pearson's coefficient ≈ 0.85) was noticed along with the bootcamp. The results indicate the potential of the framework to foster innovative professionals, providing a collaborative and psychologically safe environment, exchange of experience and knowledge, and empowerment through technological tools.

Virtual flow measurement is a soft sensing that provides feasible and economical alternatives to costly physical flow measurement instrument. It uses information available from other measurements and process parameters to calculate the flow. Current practice in calculating flow is to install differential pressure transmitter with orifice plate. By doing so, piping modification is required as well as shutting down facility fire water system, that will lead to lost production. The soft measurement logic is developed in the Distributed Control System (DCS) which is capable to manipulate process data and calculate the volume of water pumped from water supply well. The logic capitalizes on liquid volumetric change as well as discharge flow rate of water tank and pump characteristics. This is to log the water consumption in the plant in order optimize and preserve the environmental resources. This water is utilized for the utilities, oil processing and fire water system inside the Gas Oil Separation Plant (GOSP).

With the advent of the Information Technology (IT) and the Machine Learning (ML), we have stepped in to the age of automation. More specifically with the introduction of the Deep Learning which is an advanced stage of Machine Learning to allow computers to learn from experience and understand the world in terms of hierarchy of concepts using the supervised and unsupervised learning methods. This enables educators to personalized education and to improve the outcomes for the students which ultimately improves the education experience where data can accompany the students throughout their education journey. The purpose of the research is to investigate the use of the Machine Learning (ML) and Deep Learning (DL) in the UK higher education sector and to investigate the impact of these practices on the automation practices in the higher education. We are investigating how some UK Higher Education institutions are using machine learning and deep learning tools such as the IBM Watson Enlight to achieve automation and to achieve a data driven cognitive technology in terms of the curated, personalized learning contents and to align with individual students need.

A simulator has been developed to reproduce exhaust gases containing particles in a flue gas and to perform various tests using simulated flue gases. One of the applications of the simulator is to evaluate the performance of automatic dust concentration measuring instruments (dust monitors). Dust monitors are widely used in thermal power stations and waste incinerators, as they can constantly monitor the dust concentration. With the development of automatic dust concentration measurement technology, installation and operation is becoming more common. In Europe, certification systems have been established through MCERTS in the UK and TÜV in Germany, and are legally recognised as continuous monitoring systems (CMS). In Japan, these developments were delayed, but a series of JIS standards were finally established in 2020 and a certification system is currently being developed. However, only gravimetric methods are still permitted by Air Pollution Control Act in Japan for measuring dust concentration in exhaust gases, and not dust monitors. One of the reasons is the lack of scientific evidence on those dust detection characteristic performance. In this report, the actual performance of a newly developed simulator is evaluated to confirm compliance with JIS standards, and useful findings such as validity and operational problems are reported. The simulator made it possible to evaluate the correspondence between the measured values of the dust monitor and by gravimetric methods. In the future, evidence on the performance of the dust monitor will be accumulated and improvements will be made to enable testing at lower concentrations.

Foreign objects (FOD) that can be found on runways represent a great danger for aircraft on take-off or landing; FOD cleaning systems for runways fulfill two basic tasks: ''Detection'' and ''Cleaning''; which have been developed in different works and researches in the last years, generally treating both tasks as one. Over time, a great advance in detection systems was obtained, starting at first from the traditional method, commonly called ''FOD Walk'', employing visual inspection by human personnel, to the use of radars and cameras that employ complex detection algorithms. On the other hand, in the cleaning task, the progress is not the same, since in many airports and air bases around the world the inefficient FOD Walk is still used, however, in some places there are automated cleaning systems that become efficient, using robots or vehicles that clean the FOD. In this paper we will review all these methods, classifying them and analyzing their operation to show their advantages, disadvantages, the quality with which they perform the work, and the opportunities they offer when using a specific method. In the end, a discussion and conclusions are offered that will help future researchers on the subject.