Live Demonstration: neuromorphic robotics, from audio to locomotion through spiking CPG on SpiNNaker.

This live demonstration presents an audio-guided neuromorphic robot: from a Neuromorphic Auditory Sensor (NAS) to locomotion using Spiking Central Pattern Generators (sCPGs). Several gaits are generated by sCPGs implemented on a SpiNNaker board. The output of these sCPGs is sent in a real-time manner to an Field Programmable Gate Array (FPGA) board using an AER-to-SpiNN interface. The control of the hexapod robot joints is performed by the FPGA board. The robot behavior can be changed in real-time by means of the NAS. The audio information is sent to the SpiNNaker board which classifies it using a Spiking Neural Network (SNN). Thus, the input sound will activate a specific gait pattern which will eventually modify the behavior of the robot.Ministerio de Economía y Competitividad TEC2016-77785-

Semi-wildlife gait patterns classification using Statistical Methods and Artificial Neural Networks

Several studies have focused on classifying behavioral patterns in wildlife and captive species to monitor their activities and so to understanding the interactions of animals and control their welfare, for biological research or commercial purposes. The use of pattern recognition techniques, statistical methods and Overall Dynamic Body Acceleration (ODBA) are well known for animal behavior recognition tasks. The reconfigurability and scalability of these methods are not trivial, since a new study has to be done when changing any of the configuration parameters. In recent years, the use of Artificial Neural Networks (ANN) has increased for this purpose due to the fact that they can be easily adapted when new animals or patterns are required. In this context, a comparative study between a theoretical research is presented, where statistical and spectral analyses were performed and an embedded implementation of an ANN on a smart collar device was placed on semi-wild animals. This system is part of a project whose main aim is to monitor wildlife in real time using a wireless sensor network infrastructure. Different classifiers were tested and compared for three different horse gaits. Experimental results in a real time scenario achieved an accuracy of up to 90.7%, proving the efficiency of the embedded ANN implementation.Junta de Andalucía P12-TIC-1300Ministerio de Economía y Competitividad TEC2016-77785-

Embedded neural network for real-time animal behavior classification

Recent biological studies have focused on understanding animal interactions and welfare. To help biolo- gists to obtain animals’ behavior information, resources like wireless sensor networks are needed. More- over, large amounts of obtained data have to be processed off-line in order to classify different behaviors.There are recent research projects focused on designing monitoring systems capable of measuring someanimals’ parameters in order to recognize and monitor their gaits or behaviors. However, network unre- liability and high power consumption have limited their applicability.In this work, we present an animal behavior recognition, classification and monitoring system based ona wireless sensor network and a smart collar device, provided with inertial sensors and an embeddedmulti-layer perceptron-based feed-forward neural network, to classify the different gaits or behaviorsbased on the collected information. In similar works, classification mechanisms are implemented in aserver (or base station). The main novelty of this work is the full implementation of a reconfigurableneural network embedded into the animal’s collar, which allows a real-time behavior classification andenables its local storage in SD memory. Moreover, this approach reduces the amount of data transmittedto the base station (and its periodicity), achieving a significantly improving battery life. The system hasbeen simulated and tested in a real scenario for three different horse gaits, using different heuristics andsensors to improve the accuracy of behavior recognition, achieving a maximum of 81%.Junta de Andalucía P12-TIC-130

Estimating cooling production and monitoring efficiency in chillers using a soft sensor

[EN] Intensive use of heating, ventilation and air conditioning systems in buildings entails monitoring their efficiency. Moreover, cooling systems are key facilities in large buildings and can account up to 44% of the energy consumption. Therefore, monitoring efficiency in chillers is crucial and, for that reason, a sensor to measure the cooling production is required. However, manufacturers rarely install it in the chiller due to its cost. In this paper, we propose a methodology to build a soft sensor that provides an estimation of cooling production and enables monitoring the chiller efficiency. The proposed soft sensor uses independent variables (internal states of the chiller and electric power) and can take advantage of current or past observations of those independent variables. Six methods (from linear approaches to deep learning ones) are proposed to develop the model for the soft sensor, capturing relevant features on the structure of data (involving time, thermodynamic and electric variables and the number of refrigeration circuits). Our approach has been tested on two different chillers (large water-cooled and smaller air-cooled chillers) installed at the Hospital of León. The methods to implement the soft sensor are assessed according to three metrics (MAE, MAPE and R²). In addition to the comparison of methods, the results also include the estimation of cooling production (and the comparison of the true and estimated values) and monitoring the COP indicator for a period of several days and for both chillers.SIMinisterio de Ciencia e InnovaciónEuropean Regional Development Fun

Bioinspired Spike-Based Hippocampus and Posterior Parietal Cortex Models for Robot Navigation and Environment Pseudomapping

The brain has great capacity for computation and efficient resolution of complex problems, far surpassing modern computers. Neuromorphic engineering seeks to mimic the basic principles of the brain to develop systems capable of achieving such capabilities. In the neuromorphic field, navigation systems are of great interest due to their potential applicability to robotics, although these systems are still a challenge to be solved. This work proposes a spike-based robotic navigation and environment pseudomapping system formed by a bioinspired hippocampal memory model connected to a posterior parietal cortex (PPC) model. The hippocampus is in charge of maintaining a representation of an environment state map, and the PPC is in charge of local decision-making. This system is implemented on the SpiNNaker hardware platform using spiking neural networks. A set of real-time experiments are applied to demonstrate the correct functioning of the system in virtual and physical environments on a robotic platform. The system is able to navigate through the environment to reach a goal position starting from an initial position, avoiding obstacles and mapping the environment. To the best of the authors’ knowledge, this is the first implementation of an environment pseudomapping system with dynamic learning based on a bioinspired hippocampal memory. © 2023 The Authors. Advanced Intelligent Systems published by Wiley-VCH GmbH.Ministerio de Educación, Cultura y Deporte (MECD). España PID2019‐105556GB‐C33Horizonte 2020 (Unión Europea) CHIST‐ERA‐18‐ACAI‐004Horizonte 2020 (Unión Europea) PCI2019‐111841‐2/AEI/10.13039/501100011033Ministerio de Ciencia e Innovación (MCIN) España AEI/10.13039/50110001103

Virtual sensor for probabilistic estimation of the evaporation in cooling towers

16th AIAI (Artificial Intelligence Applications and Innovations) Joint International Conference[EN] Global natural resources are affected by several causes such as climate change effects or unsustainable management strategies. Indeed, the use of water has been intensified in urban buildings because of the proliferation of HVAC (Heating, Ventilating and Air Conditioning) systems, for instance cooling towers, where an abundant amount of water is lost during the evaporation process. The measurement of the evaporation is challenging, so a virtual sensor could be used to tackle it, allowing to monitor and manage the water consumption in different scenarios and helping to plan efficient operation strategies which reduce the use of fresh water. In this paper, a deep generative approach is proposed for developing a virtual sensor for probabilistic estimation of the evaporation in cooling towers, given the surrounding conditions. It is based on a conditioned generative adversarial network (cGAN), whose generator includes a recurrent layer (GRU) that models the temporal information by learning from previous states and a densely connected layer that models the fluctuations of the conditions. The proposed deep generative approach is not only able to yield the estimated evaporation value but it also produces a whole probability distribution, considering any operating scenario, so it is possible to know the confidence interval in which the estimation is likely found. This deep generative approach is assessed and compared with other probabilistic state-of-the-art methods according to several metrics (CRPS, MAPE and RMSE) and using real data from a cooling tower located at a hospital building. The results obtained show that, to the best of our knowledge, our proposal is a noteworthy method to develop a virtual sensor, taking as input the current and last samples, since it provides an accurate estimation of the evaporation with wide enough confidence intervals, contemplating potential fluctuations of the conditions.S

Design of Platforms for Experimentation in Industrial Cybersecurity

[EN] The connectivity advances in industrial control systems have also increased the possibility of cyberattacks in industry. Thus, security becomes crucial in critical infrastructures, whose services are considered essential in fields such as manufacturing, energy or public health. Although theoretical and formal approaches are often proposed to advance in the field of industrial cybersecurity, more experimental efforts in realistic scenarios are needed to understand the impact of incidents, assess security technologies or provide training. In this paper, an approach for cybersecurity experimentation is proposed for several industrial areas. Aiming at a high degree of flexibility, the Critical Infrastructure Cybersecurity Laboratory (CICLab) is designed to integrate both real physical equipment with computing and networking infrastructure. It provides a platform for performing security experiments in control systems of diverse sectors such as industry, energy and building management. They allow researchers to perform security experimentation in realistic environments using a wide variety of technologies that are common in these control systems, as well as in the protection or security analysis of industrial networks. Furthermore, educational developments can be made to meet the growing demand of security-related professionals.SIMinisterio de Economía y Competitividad Spain UNLE13-3E-157

Environment for Education on Industry 4.0

[EN] A new industrial production model based on digitalization, system interconnection, virtualization and data exploitation, has emerged. Upgrade of production processes towards this Industry 4.0 model is one of the critical challenges for the industrial sector and, consequently, the training of students and professionals has to address these new demands. To carry out this task, it is essential to develop educational tools that allow students to interact with real equipment that implements, in an integrated way, new enabling technologies, such as connectivity with standard protocols, storage and data processing in the cloud, machine learning, digital twins and industrial cybersecurity measures. For that reason, in this work, we present an educational environment on Industry 4.0 that incorporates these technologies reproducing realistic industrial conditions. This environment includes cutting-edge industrial control system technologies, such as an industrial firewall and a virtual private network (VPN) to strengthen cybersecurity, an Industrial Internet of Things (IIoT) gateway to transfer process information to the cloud, where it can be stored and analyzed, and a digital twin that virtually reproduces the system. A set of hands-on tasks for an introductory automation course have been proposed, so that students acquire a practical understanding of the enabling technologies of Industry 4.0 and of its function in a real automation. This course has been taught in a master’s degree and students have assessed its usefulness by means of an anonymous survey. The results of the educational experience have been useful both from the students’ and faculty’s viewpoint.SIAgencia estatal de investigación MCIN/AEI/ 10.13039/501100011033Comité español de Automática y Siemens a través del premio ‘Automatización y Digitalización. Industria 4.0

Efficacy of a strength-based exercise program in patients with chronic tension type headache: a randomized controlled trial

BackgroundStrength-based exercise is widely used to treat tension-type headache, but the evidence of its benefit is unclear. This study aims to analyze the efficacy of a strength-based exercise program in patients with chronic tension-type headaches.MethodsA randomized controlled trial with a 12-week strength-based exercise program, with chronic tension-type headache. The headache characteristics (which were the primary outcomes: frequency, duration, and intensity), cervical muscle thickness at rest or contraction of multifidus and longus-colli muscle, cervical range of motion, pain pressure threshold of temporalis, upper trapezius, masseter, tibialis muscle and median nerve, and cervical craniocervical flexion test were assessed at baseline and 12-weeks of follow-up in the intervention group (n = 20) and the control group (n = 20) was performed on 40 patients (85% women, aged 37.0 ± 13.3 years).ResultsBetween baseline and week-12 of follow-up the intervention group showed statistically significant differences compared to control group in the following primary outcomes: duration and intensity of headaches. In addition, the intervention group improved the thickness of deep cervical muscles, reduced the peripheral sensitization, and improved the strength of deep cervical flexors.ConclusionA 12-week strength training of neck and shoulder region induced changes in pain intensity and duration, and physical-related factors in patients with TTH. Future interventions are needed to investigate if normalization of pain characteristics and physical factors can lead to an increase of headache-related impact

Armario para la formación en automatización y control de subestaciones eléctricas de tracción

Publicado por: Comité Español de Automática Universidad de La Rioja[ES] En este trabajo, se propone el diseño de un armario para la formación en automatización y control de subestaciones eléctricas de tracción mediante el estándar IEC 61850. Este armario incorpora diversos dispositivos electrónicos inteligentes, comunicados mediante protocolos como MMS y GOOSE, con el propósito de supervisar y controlar de forma local y remota las maniobras, así como el estado de las líneas de entrada y salida de la subestación. Los equipos son configurados para comunicarse en una red redundante, demostrando ser capaces de realizar las distintas maniobras en la subestación y asegurando en todo momento la alimentación a la catenaria, si se produce un fallo en cualquiera de las líneas. Además, se proponen un conjunto de tareas prácticas para la formación en el ámbito de la automatización y control de subestaciones de tracción, que los alumnos pueden realizar con el armario propuesto.SIMCIN/AEI/10.13039/501100011033/ y el proyecto UNLE15-EE-2943 financiado por MINECO