Architecture for Smart Buildings Based on Fuzzy Logic and the OpenFog Standard

The combination of Artificial Intelligence and IoT technologies, the so-called AIoT, is expected to contribute to the sustainability of public and private buildings, particularly in terms of energy management, indoor comfort, as well as in safety and security for the occupants. However, IoT systems deployed on modern buildings may generate big amounts of data that cannot be efficiently analyzed and stored in the Cloud. Fog computing has proven to be a suitable paradigm for distributing computing, storage control, and networking functions closer to the edge of the network along the Cloud-to-Things continuum, improving the efficiency of the IoT applications. Unfortunately, it can be complex to integrate all components to create interoperable AIoT applications. For this reason, it is necessary to introduce interoperable architectures, based on standard and universal frameworks, to distribute consistently the resources and the services of AIoT applications for smart buildings. Thus, the rationale for this study stems from the pressing need to introduce complex computing algorithms aimed at improving indoor comfort, safety, and environmental conditions while optimizing energy consumption in public and private buildings. This article proposes an open multi-layer architecture aimed at smart buildings based on a standard framework, the OpenFog Reference Architecture (IEEE 1934–2018 standard). The proposed architecture was validated experimentally at the Faculty of Engineering of Vitoria-Gasteiz to improve indoor environmental quality using Fuzzy logic. Experimental results proved the viability and scalability of the proposed architecture.The authors wish to express their gratitude to the Basque Government, through the project EKOHEGAZ II; to the Diputación Foral de Álava (DFA), through the project CONAVANTER; to the UPV/EHU, through the projects GIU20/063 and CBL 22APIN; and to the MobilityLab Foundation (CONV23/12), for supporting this work

Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin

Photovoltaic (PV) energy, representing a renewable source of energy, plays a key role in the reduction of greenhouse gas emissions and the achievement of a sustainable mix of energy generation. To achieve the maximum solar energy harvest, PV power systems require the implementation of Maximum Power Point Tracking (MPPT). Traditional MPPT controllers, such as P&O, are easy to implement, but they are by nature slow and oscillate around the MPP losing efficiency. This work presents a Reinforcement learning (RL)-based control to increase the speed and the efficiency of the controller. Deep Deterministic Policy Gradient (DDPG), the selected RL algorithm, works with continuous actions and space state to achieve a stable output at MPP. A Digital Twin (DT) enables simulation training, which accelerates the process and allows it to operate independent of weather conditions. In addition, we use the maximum power achieved in the DT to adjust the reward function, making the training more efficient. The RL control is compared with a traditional P&O controller to validate the speed and efficiency increase both in simulations and real implementations. The results show an improvement of 10.45% in total power output and a settling time 24.54 times faster in simulations. Moreover, in real-time tests, an improvement of 51.45% in total power output and a 0.25 s settling time of the DDPG compared with 4.26 s of the P&O is obtained

Model Predictive Control Design and Hardware in the Loop Validation for an Electric Vehicle Powertrain Based on Induction Motors

Electric vehicles (EV) have gained importance in recent years due to environmental pollution and the future scarcity of fossil resources. They have been the subject of study for many years, where much work has focused on batteries and the electric motor (EM). There are several types of motors in the market but the most widely used are induction motors, especially squirrel cage motors. Induction motors have also been extensively studied and, nowadays, there are several control methods used—for example, those based on vector control, such as field-oriented control (FOC) and direct torque control (DTC). Further, at a higher level, such as the speed loop, several types of controllers, such as proportional integral (PI) and model predictive control (MPC), have been tested. This paper shows a comparison between a Continuous Control Set MPC (CCS-MPC) and a conventional PI controller within the FOC method, both in simulation and hardware in the loop (HIL) tests, to control the speed of an induction motor for an EV powered by lithium-ion batteries. The comparison is composed of experiments based on the speed and quality of response and the controllers’ stability. The results are shown graphically and numerically analyzed using performance metrics such as the integral of the absolute error (IAE), where the MPC shows a 50% improvement over the PI in the speed tracking performance. The efficiency of the MPC in battery consumption is also demonstrated, with 5.07 min more driving time

Particle Swarm Optimization-Based Control for Maximum Power Point Tracking Implemented in a Real Time Photovoltaic System

Photovoltaic panels present an economical and environmentally friendly renewable energy solution, with advantages such as emission-free operation, low maintenance, and noiseless performance. However, their nonlinear power-voltage curves necessitate efficient operation at the Maximum Power Point (MPP). Various techniques, including Hill Climb algorithms, are commonly employed in the industry due to their simplicity and ease of implementation. Nonetheless, intelligent approaches like Particle Swarm Optimization (PSO) offer enhanced accuracy in tracking efficiency with reduced oscillations. The PSO algorithm, inspired by collective intelligence and animal swarm behavior, stands out as a promising solution due to its efficiency and ease of integration, relying only on standard current and voltage sensors commonly found in these systems, not like most intelligent techniques, which require additional modeling or sensoring, significantly increasing the cost of the installation. The primary contribution of this study lies in the implementation and validation of an advanced control system based on the PSO algorithm for real-time Maximum Power Point Tracking (MPPT) in a commercial photovoltaic system to assess its viability by testing it against the industry-standard controller, Perturbation and Observation (P&O), to highlight its advantages and limitations. Through rigorous experiments and comparisons with other methods, the proposed PSO-based control system’s performance and feasibility have been thoroughly evaluated. A sensitivity analysis of the algorithm’s search dynamics parameters has been conducted to identify the most effective combination for optimal real-time tracking. Notably, experimental comparisons with the P&O algorithm have revealed the PSO algorithm’s remarkable ability to significantly reduce settling time up to threefold under similar conditions, resulting in a substantial decrease in energy losses during transient states from 31.96% with P&O to 9.72% with PSO

Wireless Technologies for Industry 4.0 Applications

Wireless technologies are increasingly used in industrial applications. These technologies reduce cabling, which is costly and troublesome, and introduce several benefits for their application in terms of flexibility to modify the layout of the nodes and scaling of the number of connected devices. They may also introduce new functionalities since they ease the connections to mobile devices or parts. Although they have some drawbacks, they are increasingly accepted in industrial applications, especially for monitoring and supervision tasks. Recently, they are starting to be accepted even for time-critical tasks, for example, in closed-loop control systems involving slow dynamic processes. However, wireless technologies have been evolving very quickly during the last few years, since several relevant technologies are available in the market. For this reason, it may become difficult to select the best alternative. This perspective article intends to guide application designers to choose the most appropriate technology in each case. For this purpose, this article discusses the most relevant wireless technologies in the industry and shows different examples of applications

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control

Piezoelectric actuators (PEA) are high-precision devices used in applications requiring micrometric displacements. However, PEAs present non-linearity phenomena that introduce drawbacks at high precision applications. One of these phenomena is hysteresis, which considerably reduces their performance. The introduction of appropriate control strategies may improve the accuracy of the PEAs. This paper presents a high precision control scheme to be used at PEAs based on the model-based predictive control (MPC) scheme. In this work, the model used to feed the MPC controller has been achieved by means of artificial neural networks (ANN). This approach simplifies the obtaining of the model, since the achievement of a precise mathematical model that reproduces the dynamics of the PEA is a complex task. The presented approach has been embedded over the dSPACE control platform and has been tested over a commercial PEA, supplied by Thorlabs, conducting experiments to demonstrate improvements of the MPC. In addition, the results of the MPC controller have been compared with a proportional-integral-derivative (PID) controller. The experimental results show that the MPC control strategy achieves higher accuracy at high precision PEA applications such as tracking periodic reference signals and sudden reference change

A DIVA vaccine strain lacking RpoS and the secondary messenger c-di-GMP for protection against salmonellosis in pigs

International audienceAbstractSalmonellosis is the second most common food-borne zoonosis in the European Union, with pigs being a major reservoir of this pathogen. Salmonella control in pig production requires multiple measures amongst which vaccination may be used to reduce subclinical carriage and shedding of prevalent serovars, such as Salmonella enterica serovar Typhimurium. Live attenuated vaccine strains offer advantages in terms of enhancing cell mediated immunity and allowing inoculation by the oral route. However, main failures of these vaccines are the limited cross-protection achieved against heterologous serovars and interference with serological monitoring for infection. We have recently shown that an attenuated S. Enteritidis strain (ΔXIII) is protective against S. Typhimurium in a murine infection model. ΔXIII strain harbours 13 chromosomal deletions that make it unable to produce the sigma factor RpoS and synthesize cyclic-di-GMP (c-di-GMP). In this study, our objectives were to test the protective effects of ΔXIII strain in swine and to investigate if the use of ΔXIII permits the discrimination of vaccinated from infected pigs. Results show that oral vaccination of pre-weaned piglets with ΔXIII cross-protected against a challenge with S. Typhimurium by reducing faecal shedding and ileocaecal lymph nodes colonization, both at the time of weaning and slaughter. Vaccinated pigs showed neither faecal shedding nor tissue persistence of the vaccine strain at weaning, ensuring the absence of ΔXIII strain by the time of slaughter. Moreover, lack of the SEN4316 protein in ΔXIII strain allowed the development of a serological test that enabled the differentiation of infected from vaccinated animals (DIVA)

Tubers from patients with tuberous sclerosis complex are characterized by changes in microtubule biology through ROCK2 signalling

Most patients with tuberous sclerosis complex (TSC) develop cortical tubers that cause severe neurological disabilities. It has been suggested that defects in neuronal differentiation and/or migration underlie the appearance of tubers. However, the precise molecular alterations remain largely unknown. Here, by combining cytological and immunohistochemical analyses of tubers from nine TSC patients (four of them diagnosed with TSC2 germline mutations), we show that alteration of microtubule biology through ROCK2 signalling contributes to TSC neuropathology. All tubers showed a larger number of binucleated neurons than expected relative to control cortex. An excess of normal and altered cytokinetic figures was also commonly observed. Analysis of centrosomal markers suggested increased microtubule nucleation capacity, which was supported by the analysis of an expression dataset from cortical tubers and control cortex, and subsequently linked to under-expression of Rho-associated coiled-coil containing kinase 2 (ROCK2). Thus, augmented microtubule nucleation capacity was observed in mouse embryonic fibroblasts and human fibroblasts deficient in the Tsc2/TSC2 gene product, tuberin. Consistent with ROCK2 under-expression, microtubule acetylation was found to be increased with tuberin deficiency; this alteration was abrogated by rapamycin treatment and mimicked by HDAC6 inhibition. Together, the results of this study support the hypothesis that loss of TSC2 expression can alter microtubule organization and dynamics, which, in turn, deregulate cell division and potentially impair neuronal differentiation. Copyrigh

Innovaciones y mejoras en el proyecto tutoría entre compañeros. Curso 2015-2016

Memoria ID-0137. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2015-2016