Revisiting Classical Controller Design and Tuning with Genetic Programming.

This paper introduces the application of a genetic programming (GP)-based method for the automated design and tuning of process controllers, representing a noteworthy advancement in artificial intelligence (AI) within the realm of control engineering. In contrast to already existing work, our GP-based approach operates exclusively in the time domain, incorporating differential operations such as derivatives and integrals without necessitating intermediate inverse Laplace transformations. This unique feature not only simplifies the design process but also ensures the practical implementability of the generated controllers within physical systems. Notably, the GP's functional set extends beyond basic arithmetic operators to include a rich repertoire of mathematical operations, encompassing trigonometric, exponential, and logarithmic functions. This broad set of operations enhances the flexibility and adaptability of the GP-based approach in controller design. To rigorously assess the efficacy of our GP-based approach, we conducted an extensive series of tests to determine its limits and capabilities. In summary, our research establishes the GP-based approach as a promising solution for automating the controller design process, offering a transformative tool to address a spectrum of control problems across various engineering applications.

Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture.

Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today's Industry. A wide range of algorithms are being used to monitor industrial processes and predict process variables that are difficult to be measured. Maintenance operations are mandatory to tackle in all industrial equipment. It is well known that a huge amount of money is invested in operational and maintenance actions in industrial gas turbines (IGTs). In this paper, two variations of autoencoders were used to analyse the performance of an IGT after major maintenance. The data used to analyse IGT conditions were ambient factors, and measurements were performed using several sensors located along the compressor. The condition assessment of the industrial gas turbine compressor revealed significant changes in its operation point after major maintenance; thus, this indicates the need to update the internal operating models to suit the new operational mode as well as the effectiveness of autoencoder-based models in feature extraction. Even though the processing performance was not compromised, the results showed how this autoencoder approach can help to define an indicator of the compressor behaviour in long-term performance.

Technical Development of the CeCi Social Robot.

This research presents the technical considerations for implementing the CeCi (Computer Electronic Communication Interface) social robot. In this case, this robot responds to the need to achieve technological development in an emerging country with the aim of social impact and social interaction. There are two problems with the social robots currently on the market, which are the main focus of this research. First, their costs are not affordable for companies, universities, or individuals in emerging countries. The second is that their design is exclusively oriented to the functional part with a vision inherent to the engineers who create them without considering the vision, preferences, or requirements of the end users, especially for their social interaction. This last reason ends causing an aversion to the use of this type of robot. In response to the issues raised, a low-cost prototype is proposed, starting from a commercial platform for research development and using open source code. The robot design presented here is centered on the criteria and preferences of the end user, prioritizing acceptability for social interaction. This article details the selection process and hardware capabilities of the robot. Moreover, a programming section is provided to introduce the different software packages used and adapted for the social interaction, the main functions implemented, as well as the new and original part of the proposal. Finally, a list of applications currently developed with the robot and possible applications for future research are discussed.

Operational Modes Detection in Industrial Gas Turbines Using an Ensemble of Clustering Methods.

Operational modes of a process are described by a number of relevant features that are indicative of the state of the process. Hundreds of sensors continuously collect data in industrial systems, which shows how the relationship between different variables changes over time and identifies different modes of operation. Gas turbines' operational modes are usually defined regarding their expected energy production, and most research works either are focused a priori on obtaining these modes solely based on one variable, the active load, or assume a fixed number of states and build up predictive models to classify new situations as belonging to the predefined operational modes. However, in this work, we take into account all available parameters based on sensors' data because other factors can influence the system status, leading to the identification of a priori unknown operational modes. Furthermore, for gas turbine management, a key issue is to detect these modes using a real-time monitoring system. Our approach is based on using unsupervised machine learning techniques, specifically an ensemble of clusters to discover consistent clusters, which group data into similar groups, and to generate in an automatic way their description. This description, upon interpretation by experts, becomes identified and characterized as operational modes of an industrial process without any kind of a priori bias of what should be the operational modes obtained. Our proposed methodology can discover and identify unknown operational modes through data-driven models. The methodology was tested in our case study with Siemens gas turbine data. From available sensors' data, clusters descriptions were obtained in an automatic way from aggregated clusters. They improved the quality of partitions tuning one consistency parameter and excluding outlier clusters by defining filtering thresholds. Finally, operational modes and/or sub-operational modes were identified with the interpretation of the clusters description by process experts, who evaluated the results very positively.

Condition Assessment of Industrial Gas Turbine Compressor Using a Drift Soft Sensor Based in Autoencoder.

Maintenance is the process of preserving the good condition of a system to ensure its reliability and availability to perform specific operations. The way maintenance is nowadays performed in industry is changing thanks to the increasing availability of data and condition assessment methods. Soft sensors have been widely used over last years to monitor industrial processes and to predict process variables that are difficult to measured. The main objective of this study is to monitor and evaluate the condition of the compressor in a particular industrial gas turbine by developing a soft sensor following an autoencoder architecture. The data used to monitor and analyze its condition were captured by several sensors located along the compressor for around five years. The condition assessment of an industrial gas turbine compressor reveals significant changes over time, as well as a drift in its performance. These results lead to a qualitative indicator of the compressor behavior in long-term performance.

Children's Imaginaries of Human-Robot Interaction in Healthcare.

This paper analyzes children’s imaginaries of Human-Robots Interaction (HRI) in the context of social robots in healthcare, and it explores ethical and social issues when designing a social robot for a children’s hospital. Based on approaches that emphasize the reciprocal relationship between society and technology, the analytical force of imaginaries lies in their capacity to be embedded in practices and interactions as well as to affect the construction and applications of surrounding technologies. The study is based on a participatory process carried out with six-year-old children for the design of a robot. Imaginaries of HRI are analyzed from a care-centered approach focusing on children’s values and practices as related to their representation of care. The conceptualization of HRI as an assemblage of interactions, the prospective bidirectional care relationships with robots, and the engagement with the robot as an entity of multiple potential robots are the major findings of this study. The study shows the potential of studying imaginaries of HRI, and it concludes that their integration in the final design of robots is a way of including ethical values in it.

Externalising moods and psychological states in a cloud based system to enhance a pet-robot and child's interaction.

BACKGROUND: This PATRICIA research project is about using pet robots to reduce pain and anxiety in hospitalized children. The study began 2 years ago and it is believed that the advances made in this project are significant. Patients, parents, nurses, psychologists, and engineers have adopted the Pleo robot, a baby dinosaur robotic pet, which works in different ways to assist children during hospitalization. METHODS: Focus is spent on creating a wireless communication system with the Pleo in order to help the coordinator, who conducts therapy with the child, monitor, understand, and control Pleo's behavior at any moment. This article reports how this technological function is being developed and tested. RESULTS: Wireless communication between the Pleo and an Android device is achieved. The developed Android app allows the user to obtain any state of the robot without stopping its interaction with the patient. Moreover, information is sent to a cloud, so that robot moods, states and interactions can be shared among different robots. CONCLUSIONS: Pleo attachment was successful for more than 1 month, working with children in therapy, which makes the investment capable of positive therapeutic possibilities. This technical improvement in the Pleo addresses two key issues in social robotics: needing an enhanced response to maintain the attention and engagement of the child, and using the system as a platform to collect the states of the child's progress for clinical purposes.

A survey on model based approaches for 2D and 3D visual human pose recovery.

Human Pose Recovery has been studied in the field of Computer Vision for the last 40 years. Several approaches have been reported, and significant improvements have been obtained in both data representation and model design. However, the problem of Human Pose Recovery in uncontrolled environments is far from being solved. In this paper, we define a general taxonomy to group model based approaches for Human Pose Recovery, which is composed of five main modules: appearance, viewpoint, spatial relations, temporal consistence, and behavior. Subsequently, a methodological comparison is performed following the proposed taxonomy, evaluating current SoA approaches in the aforementioned five group categories. As a result of this comparison, we discuss the main advantages and drawbacks of the reviewed literature.