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Item type:Publication, Qhali: A humanoid robot for assisting in mental health treatment(Multidisciplinary Digital Publishing Institute (MDPI), 2024-02-01)In recent years, social assistive robots have gained significant acceptance in healthcare settings, particularly for tasks such as patient care and monitoring. This paper offers a comprehensive overview of the expressive humanoid robot, Qhali, with a focus on its industrial design, essential components, and validation in a controlled environment. The industrial design phase encompasses research, ideation, design, manufacturing, and implementation. Subsequently, the mechatronic system is detailed, covering sensing, actuation, control, energy, and software interface. Qhali’s capabilities include autonomous execution of routines for mental health promotion and psychological testing. The software platform enables therapist-directed interventions, allowing the robot to convey emotional gestures through joint and head movements and simulate various facial expressions for more engaging interactions. Finally, with the robot fully operational, an initial behavioral experiment was conducted to validate Qhali’s capability to deliver telepsychological interventions. The findings from this preliminary study indicate that participants reported enhancements in their emotional well-being, along with positive outcomes in their perception of the psychological intervention conducted with the humanoid robot. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhanced leakage detection and estimation via a hybrid genetic algorithm and high-order sliding modes observer approach(Institute of Electrical and Electronics Engineers Inc., 2024-01-01)This paper introduces a hybrid approach designed for both detecting and estimating the magnitude of leaks in oil pipelines. The method integrates a High Order Sliding Mode Observer(HOSMO) with a Super Twisting Algorithm to serve as an observer for state estimation of the system. A parameterized model based on momentum and mass balance equations with discretization is used, where the parameters are the location and magnitude of the leakage. To find these parameters, it incorporates the Genetic Algorithm to solve an optimization problem that relies on a function cost related to the error norm between measurements and states estimation from HOSMO in order to measure the difference between the model with an assumed leakage and the real leakage. The solution of the minimization problem represents the leak position and magnitude. The feasibility and effectiveness of this method are evaluated using a simulation model representing a 306 km sector of the North-Peruvian Oil Pipeline. The results demonstrate its robustness against noise, showcasing a precision of ±250 m in pinpointing the location of leaks. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Alarm management approach for supervision of green hydrogen plants(Elsevier Ltd, 2024-10-20)Amid the increasing significance of renewable energy sources, Green Hydrogen Plants (GHPs) have emerged as pivotal contributors to sustainable energy solutions. However, efficient and reliable alarm management in such complex systems remains a significant challenge. This paper presents a novel methodology called V-nets-based alarm management (VBAM), designed to improve supervision by addressing the intricacies of discrete event management in industrial applications such as GHPs. VBAM offers a powerful formalism that integrates visual modeling and temporal patterns, enabling the precise detection and handling of alarms. The proposed methodology is applied to a case study in a GHP, where critical operational parameters are continuously monitored. By analyzing discrete events and their temporal patterns, V-nets facilitate early fault detection, minimize false positives, and optimize alarm response. The theoretical application of VBAM demonstrates its efficacy in improving system safety, reducing downtime, and enhancing overall operational efficiency within GHPs. The contributions of this work to the state of the art include the development of a comprehensive VBAM methodology tailored to GHPs, as well as the theoretical and practical demonstration of its potential impact on alarm management within GHP operations. The outcomes from the experiments showcase the adaptability and effectiveness of VBAM in addressing the complexities of alarm management in GHPs, thereby contributing to enhanced safety, efficiency, and resilience in the operation of GHPs. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, V-nets(Elsevier B.V., 2024-06-01)This article presents an actualization of the formalism V-nets which is a tool of supervision that deals with diagnosis problems of industrial processes, situations such as simultaneity, and false positives in sequences of discrete events. In particular, in fault detection applications, industrial processes need the dependability of their control and supervisory systems. Since the start of the investigation into automata and simulation theories, almost 50 years ago, simultaneous occurrences have been a crucial and challenging subject. This situation is a false problem that comes from the notion of "state", that is to say, that model Petri Nets, DEVS, and State Charts, among others, do not come from the concrete process under consideration. Therefore, this new formalism is based on the model of Chronicles and improved with other tools and elements that permit an increase in its functionality. V-nets are provided as a way to represent dynamic processes without using the state machine paradigm, with robustness and the capacity to distinguish discrete event sequences. An analysis of alarm management in a Green Hydrogen Plant (GHP) is presented and concludes with a comparative analysis of Time Petri Nets (TPN), Timed Automata (TA), Chronicles, and V-nets.