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Gas turbines play a key role in generating power. It is really important that they work efficiently, safely, and reliably. However, their performance can be adversely affected by factors such as component wear, vibrations, and temperature fluctuations, often leading to abnormal patterns indicative of potential failures. As a result, anomaly detection has become an area of active research. Matrix Profile (MP) methods have emerged as a promising solution for identifying significant deviations in time series data from normal operational patterns. While most existing MP methods focus on vibration analysis of gas turbines, this paper introduces a novel approach using the outlet power signal. This modified approach, termed Cluster-based Matrix Profile (CMP) analysis, facilitates the identification of abnormal patterns and subsequent anomaly detection within the gas turbine engine system. Significantly, CMP analysis not only accelerates processing speed, but also provides user-friendly support information for operators. The experimental results on real-world gas turbines demonstrate the effectiveness of our approach in the early detection of anomalies and potential system failures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents a validation methodology for a remote system with its objective focused on a social robot. The research process starts with the customization of an application for smartphones, achieving a simple method of connection and attachment to the robot. This customization allows remote operation of the robot's movements and an additional level of autonomy for the displacements in previously known locations. One of several teleoperations methods is the direct teleoperations method, which is used in master–slave control mode via a wireless network. Next, the article focuses on proposing a validation methodology for social robot applications design. Under this approach, two tests are performed to validate the designed application. The first one seeks to find the response speed of the communication between the robot and the mobile device wherein 10 devices with different characteristics and capabilities are used. This test is critical since a delay outside the allowable range invalidates the use of the application. The second test measures the application's usability through a user survey, which allows for determining the preferences that people may have when using this type of application. This second test is essential to consider the overall acceptability of the social robot. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The development of healthcare patient digital twins in combination with machine learning technologies helps doctors in therapeutic prescription and in minimally invasive intervention procedures. The confidentiality of medical records or limited data availability in many health domains are drawbacks that can be overcome with the generation of synthetic data conformed to real data. The use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients has been previously introduced as a tool to solve this problem in the form of anonymized synthetic patients. However, generated synthetic data are mainly validated from the machine learning domain (loss functions) or expert domain (oncologists). In this paper, we propose statistical decision making as a validation tool: Is the model good enough to be used? Does the model pass rigorous hypothesis testing criteria? We show for the case at hand how loss functions and hypothesis validation are not always well aligned. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper introduces a new method for supervised discretization based on interval distances by using a novel concept of neighborhood in the target's space. The proposed method takes into consideration the order of the class attribute, when this exists, so that it can be used with ordinal discrete classes as well as continuous classes, in the case of regression problems. The method has proved to be very efficient in terms of accuracy and faster than the most commonly supervised discretization methods used in the literature. It is illustrated through several examples, and a comparison with other standard discretization methods is performed for three public data sets by using two different learning tasks: a decision tree algorithm and SVM for regression. Index Terms--Classification, ordinal regression, supervised discretization, interval distances.

The use of collaborative robots (cobots) in industrial and academic settings facilitates physical and cognitive interaction with operators. This framework is a challenge to determine how measures on concepts, such as usability, can be adapted to these new environments. Usability is a quality attribute prevalent in the field of human-computer interaction concerning the context of use and the measure of effectiveness, efficiency, and satisfaction of products and systems. In this work, the importance of the role of benchmarking usability with collaborative robots is discussed. The introduced approach is part of a general methodology for studying people and robots' performance in collaboration. It is being designed and developed on a concrete experience into a human-robot collaborative workspace. Outcomes from the study include a list of steps, resources, recommendations, and some customized questionnaires to obtain cobot-oriented usability analysis and case study results. [ABSTRACT FROM AUTHOR]

During the usual SVM biclassification learning process, the bias is chosen a posteriori as the value halfway between separating hyperplanes. A note on different approaches on the calculation of the bias when SVM is used for multiclassification is provided and empirical experimentation is carried out which shows that the accuracy rate can be improved by using bias formulations, although no single formulation stands out as providing better performance. Index Terms--Bias, multiclassification, support vector machine (SVM).

In the industrial domain, one important research activity for cognitive robotics is the development of assistant robots. In this work, we show how the use of a cognitive assistant robot can contribute to (i) improving task effectiveness and productivity, (ii) providing autonomy for the human supervisor to make decisions, providing or improving human operators' skills, and (iii) giving feedback to the human operator in the loop. Our approach is evaluated on variability reduction in a manual assembly system. The overall study and analysis are performed on a model of the assembly system obtained using the Functional Resonance Analysis Method (FRAM) and tested in a robotic simulated scenario. Results show that a cognitive assistant robot is a useful partner in the role of improving the task effectiveness of human operators and supervisors. [ABSTRACT FROM AUTHOR]

The field of human-robot interaction (HRI) is a broad community encompassing robotics, artificial intelligence (AI), human-computer interaction (HCI), psychology and social science. HRI in social robotics explores important issues in designing a robot system that works with people in daily life environments, capable of interacting with, modeling, and learning from humans. Robotic systems should improve their capabilities to not only understand humans but also to convey their intention within their actions. The present work demonstrates this behaviour is achievable through a field study conducted at a science museum. This article introduces a computer vision algorithm which is able to detect and track a leader within a group of people - the science communicator, for this particular case - and distinguish between group members and non-group members as well, all by means of a cognitive and logical behaviour analysis of their interactions on scene. The leader's direction is also computed as an attention reference for this approach. The computer vision system is supervising people within a group following a guide to prevent accidents and missing persons. This work represents one of a wide range of possible applications and future scenarios where group interactions are a key aspect for robots to understand and effectively participate in social environments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper describes an exploratory study on group interaction with a robot guide in an open large-scale busy environment. For an entire week, a humanoid robot was deployed in the popular Cosmocaixa Science Museum in Barcelona and guided hundreds of people through the museum facilities. The main goal of this experience is to study in the wild the episodes of the robot guiding visitors to a requested destination focusing on the group behavior during displacement. The walking behavior follow-me and the face-to-face communication in a populated environment are analyzed in terms of guide-visitors interaction, grouping patterns and spatial formations. Results from observational data show that the space configurations spontaneously formed by the robot guide and visitors walking together did not always meet the robot communicative and navigational requirements for successful guidance. Therefore, additional verbal and nonverbal prompts must be considered to regulate effectively the walking together and follow-me behaviors. Finally, we discuss lessons learned and recommendations for robot's spatial behavior in dense crowded scenarios. [ABSTRACT FROM AUTHOR]