Abstract
The practical application of knowledge acquired during undergraduate studies is crucial for students to address real-world problems and seek solutions. The SMAR PD3 didactic plant provides a conducive environment for experiments in systems such as level and flow, common in various industrial sectors. Cascade control, an approach that sequentially uses two or more controllers, stands out as a promising strategy to enhance precision and stability in industrial processes. This work proposes a study on cascade control in flow and level systems, demonstrating its application in the didactic plant. The process involved system identification, tuning of conventional and cascade PI and PID controllers, followed by the implementation of the Successive Loop Closure technique. Results, in line with specialized literature, indicate that the implementation of cascade controllers in the industry can improve specific processes affected by disturbances or changes in variables, directly impacting the overall functioning of the process.

Abstract
This paper describes the development of a complete controller for the FANUC S-420FD 6-axis industrial robot. The original controller of the robot presented failures that made it impossible to operate and that negatively impacted the academic and research activities. To solve this problem, it was proposed the development of a new open-technology controller and also the design of an intuitive and functional graphical interface, allowing the programming, control and monitoring of the robot parameters. The developed interface offers advanced features such as trajectory programming, custom parameter configuration, and real-time visualization of the robot's state. This work highlights the importance of efficient and affordable solutions for the maintenance of industrial robots in university environments, encouraging scientific and technological advancement in these areas of study.

Abstract
This study compares two computer vision methods to detect yellow sticky traps using unmanned autonomous vehicles in olive tree cultivation. The traps aim to combat and monitor the density of the Bactrocera oleae, an important pest that damages olive fruit, leading to substantial economic losses annually. The evaluation encompassed two distinct methods: firstly, an algorithm employing conventional segmentation techniques like thresholding and contour localization, and secondly, a contemporary artificial intelligence approach utilizing YOLOv8, a state-of-the-art technology. A specific dataset was created to train and adjust the two algorithms. At the end of the study, both were able to locate the trap precisely. The segmentation algorithm demonstrated superior performance at proximal distances (50 cm), outperforming the outcomes achieved by YOLOv8. In contrast, YOLOv8 exhibited sustained precision, irrespective of the distance under examination. These findings affirm the versatility of both algorithms, highlighting their adaptability to various contexts based on distinct application demands. Consideration of trade-offs between accuracy and processing speed is essential in determining the most appropriate algorithm for a given application.

Abstract

Abstract
It is known that electrical energy consumption is higher during the day than at night.This is a challenge to balance the consumption levels because when the consumption is high at night, it does not have energy production to supply and the tariff usage is cheaper. Aspiring to avoid the users consuming too much electrical energy and work on this usage control during the night, the present work aims to develop smart plug modules that could self-manage power in residence utilizing the minimum of grid energy. In this sense, the modules may use the overproduction of energy coming from generator systems (such as photovoltaic panels), eliminating the necessity of battery usage. Sometimes, the power supply could provide different values of current, consequently, the use of this electric energy needs to adapt according to the production. Therefore, the final objective is to build an intelligent electrical management system that works on energy efficiency.

Abstract
In the current energy context, alternatives are sought that provide a more conscious use of energy and the development of technology aimed at efficiently meeting the needs of energy consumers and the utility company. In this scenario, smart systems for monitoring and controlling the energy consumption of residential loads stand out. In [1], the authors worked on a system from which the user could monitor their energy consumption in real time. Through a website, the consumer accessed their information using visualizations in graphics, for example. Consumption data was obtained by a smart plug. Furthermore, the option to remotely turn devices on and off has been included in the system so that the user has the ease of controlling their devices.