Morelva Saeteros

Bio: Morelva Saeteros is an academic researcher from Politecnica Salesiana University. The author has contributed to research in topics: Augmented reality & PID controller. The author has an hindex of 2, co-authored 7 publications receiving 44 citations.

Real-Time Augmented Reality Application for Visualizing Variables in Industrial Processes

Abstract: In recent years there has been an increase in the use of virtual tools in different areas such as: education, tourism or industry. The augmented reality (AR) enables the correlation of the real world with virtual objects so that users have additional information of the surrounding environment supplementing the learning and experience of the user. The present work shows the development of an AR application, which characterizes and sends information of the industrial sensors in real time; the recognition of objects utilized a three-dimensional analysis due to the holographic geometry of the MPS PA Compact Workstation, the mobile application combines various development platforms such as: Visual Studio, Android Studio Unity and Vuforia. The data of the sensors arrive to a public URL address which enables visualizing its status in real time, the Android application accesses the particle cloud server via WiFi enabling visualizing the information of the sensors in the smartphone.

Development and Analysis of a PID Controller and a Fuzzy PID

Abstract: This document presents the development of a Proportional Integral Derivative (PID) and a Fuzzy-PID, for the control of the level of the MPS PA plant, which is constituted by industrial sensors and actuators. The mathematical model and the parameters of the system were obtained using the software the MATLAB environment using a STM32F4 card. The experimental results show the robustness of the Fuzzy PID, because it improves the response of the system and its parameters are tuned automatically according to the state of the process, thus enhancing the performance of the system. Besides, the conventional PID controller requires an adjustment of its parameters to operate in an optimal for each change of variable, and it does not respond efficiently to disturbances.

Development of an IEC-61499 PID Control for Industrial Processes Applications

Abstract: In this paper we present novel procedures for deploying closed-loop controllers, to be employed in industrial processes, using free software. Using C++ and Framework for Distributed Industrial Automation and Control (4DIAC) software we implemented a Proportional-Integral-Derivative (PID) control, under IEC-61499 standard which focusses on the portability, interoperability, reuse and configuration of applications. In our research we use as tested the FESTO's MPS® PA module, which offers a flexible set of industrial sensors, and a Raspberry Pi board. Results show the effective performance and reduced cost an IEC-61499 PID may have and the possibilities it offers for deploying smart-control-systems.

Braille Grade 1 Learning and Monitoring System

Abstract: Braille is a standard code for people with visual disabilities that consists of an array of high points. This paper explains the implementation of a low-cost prototype for reading, writing, and audio-assisted evaluation. This didactic and user-friendly system aims to improve the learning of disabled people by providing teachers new and modern alternatives to lead and monitor the student’s activities. For designing the prototype, the researchers followed the suggestions provided by teachers of the “Mariana de Jesus” institute for blind and deaf children. The Braille Grade 1 system consists of the following four stages: (a) writing, by the use of 10 buttons placed in matrix form, (b) reading, through 10 push-pull solenoids, (c) memorization, and (d) evaluation by using a touch display where the generated letters are displayed and assisted by audio messages. The implementation was carried out using a Raspberry Pi 3B board due to its high performance and low cost. The device improves and eases learning of Braille grade 1, allowing the monitoring of the progress reached, with and an acceptance of 91% obtained from the teachers of the institute.

Joint optimization of energy consumption and time delay in IoT-fog-cloud computing environments using NSGA-II metaheuristic algorithm

Abstract: Today, there exists a growing demand for Internet of Things (IoT) services in the form of vehicle networks, smart cities, augmented reality, virtual reality, positioning systems, and so on. Due to the considerable distance between the IoT devices and the central cloud, using this option may no longer be a suitable solution for delay-constraint tasks. To overcome these drawbacks, a complementary solution called fog computing, also known as the cloud at the edge is used. In this solution, nodes at the edge of the network provide resources for IoT applications. Although offloading tasks on the fog nodes save energy on IoT devices, it increases task response time. Therefore, making a trade-off between energy consumption and latency is crucial for IoT devices. Because offloading falls into the category of NP-hard knapsack problems, metaheuristic methods have been widely used in recent years. In this paper, we formulate the problem of joint optimization of energy consumption and latency in the form of a multi-objective problem and solve it using the non-dominant sorting genetic algorithm (NSGA-II) and Bees algorithm (BA). Also, to improve the quality of solutions, we combine each of these methods with a robust type of differential evolution approach called minimax differential evolution (MMDE). This combination moves the solutions to better areas and increases the convergence speed. The simulation results show that NSGA-based methods have remarkable robustness compared to BA-based methods in terms of significant criteria such as energy consumption, time delay, and so on. Our statistical analysis shows that both NSGA-based and BA-based metaheuristic methods not only do not significantly increase energy consumption but also drastically reduce response time.

Fog-Based Intelligent Machine Malfunction Monitoring System for Industry 4.0

Abstract: There is an exponential increase in the use of Industrial Internet of Things (IIoT) devices for controlling and monitoring the machines in an automated manufacturing industry. Different temperature sensors, pressure sensors, audio sensors, and camera devices are used as IIoT devices for pipeline monitoring and machine operation control in the industrial environment. But, monitoring and identifying the machine malfunction in an industrial environment is a challenging task. In this article, we consider machines fault diagnosis based on their operating sound using the fog computing architecture in the industrial environment. The different computing units, such as industrial controller units or micro data center are used as the fog server in the industrial environment to analyze and classify the machine sounds as normal and abnormal. The linear prediction coefficients and Mel-frequency cepstral coefficients are extracted from the machine sound to develop and deploy supervised machine learning (ML) models on the fog server to monitor and identify the malfunctioning machines based on the operating sound. The experimental results show the performance of ML models for the machines sound recorded with different signal-to-noise ratio levels for normal and abnormal operations.