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One hundred development and certification ground tests must be performed before the A400M aircraft's first flight. Ground testing is an essential but also expensive process performed by the Airbus Defense and Space (Airbus DS) Company in the manufacturing cycle of an aircraft. This process involves the repetition of a large group of tests due to failures (known in the terminology of the company as “incidences”) in the testing. One or more incidences in a test imply that it will be repeated, which requires a significant investment of resources and time by the company's engineers. In this article, an innovative decision support environment to manage the ground testing sequence is presented and a data mining analysis of the testing time and the trend of test incidences is included. The core application, developed in R language, is supported by an easy-to-use customer web application using the Shiny environment. The environment was used to analyze real-world cases of tests to be performed by Airbus DS, producing a useful decision tool for company experts to evaluate the ground testing sequence. It is currently in the last stage of testing by the Airbus DS ground test staff using real-world data.

Ground testing is an essential process in the manufacturing cycle of an aircraft. Testing requires a large investment of resources and time on the part of the engineers of the company. It is common for failures to occur during the different tests during this process. Our group at the Electronic Technology Department of the University of Seville (Spain) worked with the Airbus Defense and Space Company (Airbus DS) to develop a framework for the prediction of failures (known in the terminology of the company as “incidences”) generated in the ground testing of the A400M aircraft. The prediction models are generated through a data mining process with the simulated data registered by the company on its testing process of the previous aircraft. In addition, the framework not only predicts incidences but also suggests changes in the test parameters so that these incidences will not occur again in future aircraft. A battery of tests on real cases was carried out to validate the different models. The success rate of the models was 87.5%, which is considered a good result by the company. In addition, the changes suggested by the framework to ensure that the incidences did not reoccur were studied and executed by Airbus DS in the tests. Currently, based on the framework developed with simulated data, Airbus is using the framework with the actual data of its aircraft.

Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption–generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy.

[EN] Many current system-on-chip (SoC) devices are composed of low-power multicore processors combined with a small graphics accelerator (or GPU) offering a trade-off between computational capacity and low-power consumption. In this context, spatial audio methods such as wave field synthesis (WFS) can benefit from a distributed system composed of several SoCs that collaborate to tackle the high computational cost of rendering virtual sound sources. This paper aims at evaluating important aspects dealing with a distributed WFS implementation that runs over a network of Jetson Nano boards composed of embedded GPU-based SoCs: computational performance, energy efficiency, and synchronization issues. Our results show that the maximum efficiency is obtained when the WFS system operates the GPU frequency at 691.2 MHz, achieving 11 sources-per-Watt. Synchronization experiments using the NTP protocol show that the maximum initial delay of 10 ms between nodes does not prevent us from achieving high spatial sound quality., This work has been supported by the Spanish Government through TIN2017-82972-R, ESP2015-68245-C4-1-P, the Valencian Regional Government through PROMETEO/2019/109 and the Universitat Jaume I through UJI-B2019-36.

Engineering studies are usually based on mathematical, scientific and technical concepts. The use of these concepts in a practical way helps students to secure and assimilate them. The work presented in this paper includes the development of an economic training equipment for its use in the practical sessions of Automatic Control Systems and Industrial Automation subjects of the Undergraduate Degree in Industrial Electronic Engineering of the authors' university. The training equipment is based on a motorized linear axis. It can be controlled by a PC or a PLC. The equipment is very flexible and can be used for modelling or control training, allowing the emulation of different physical systems.

Digital Signal Processor is a useful tool for learning and practice about filters for digital signals. The practices of the subject “Procesado Digital de Sen ales” made the students learn how to use it, and how to run some algorithms. But the hardware and laboratory restrictions and the complexity of subject provoked some availability problems of digital signal processor platforms. An effective solution is the creation of a virtual laboratory which connects to the real hardware, as explained in this document.

The discrepancy between the stiffness of commercially pure titanium and cortical bone tissue compromises its success as a biomaterial. The use of porous titanium has been widely studied, however, it is still challenging to obtain materials able to replicate the porous structure of the bones (content, size, morphology and distribution). In this work, the freeze-casting technique is used to manufacture cylinders with elongated porosity, using a home-made and economical device. The relationship between the processing parameters (diameter and material of the mold, temperature gradient), microstructural features and mechanical properties is established and discussed, in terms of ensuring biomechanical and biofunctional balance. The cylinders have a gradient porosity suitable for use in dentistry, presenting higher Young&rsquo, s modulus at the bottom, near the cold spot and, therefore better mechanical resistance (it would be in contact with a prosthetic crown), while the opposite side, the hot spot, has bigger, elongated pores and walls.

This paper presents a study of the most suitable ground test equipment for the Airbus A400M aircraft aimed at optimizing a design that combines portability, flexibility, and configurability. The study is based on current trends in the configuration of the different parts that are involved in the process of designing Aircraft Interface Modules (AIMs). Researches have been conducted facing the real problems of data acquisition and control in the assembly, maintenance, and repair processes of aircraft. In this sense, the number of signals used simultaneously in historical tests has been determined to be a parameter that plays an important role in the analysis. This work is within the framework of a project that our working group of the Department of Electronic Technology has carried out together with the company Airbus Defense and Space (Airbus DS). Thus, all the analyses and researches for the different configurations of the test equipment have been validated for the company. Some state-of-the-art systems have been considered in the process of designing the structure of the test system while analyzing the main advantages and drawbacks. Every hardware or software component was identified and justified according to its importance with respect to the use case. A comparison of how every approach meets the requirements was taken into account to define a methodology for designing a portable and configurable ground testing system. Thus, the conclusions of our study are being used by Airbus DS for the design of a prototype for the next generation of AIMs in A400M.

The complexity of power systems is rising mainly due to the expansion of renewable energy generation. Due to the enormous variability and uncertainty associated with these types of resources, they require sophisticated planning tools so that they can be used appropriately. In this sense, several tools for the simulation of renewable energy assets have been proposed. However, they are traditionally focused on the simulation of the generation process, leaving the operation of these systems in the background. Conversely, more expert SCADA operators for the management of renewable power plants are required, but their training is not an easy task. SCADA operation is usually complex, due to the wide set of information available. In this sense, simulation or co-simulation tools can clearly help to reduce the learning curve and improve their skills. Therefore, this paper proposes a useful simulator based on a JavaScript engine that can be easily connected to any renewable SCADAs, making it possible to perform different simulated scenarios for novel operator training, as if it were a real facility. Using this tool, the administrators can easily program those scenarios allowing them to sort out the lack of support found in setting up facilities and training of novel operator tasks. Additionally, different renewable energy generation models that can be implemented in the proposed simulator are described. Later, as a use example of this tool, a study case is also performed. It proposes three different wind farm generation facility models, based on different turbine models: one with the essential generation turbine function obtained from the manufacturer curve, another with an empirical model using monotonic splines, and the last one adding the most important operational states, making it possible to demonstrate the usefulness of the proposed simulation tool.

One of the fundamental tasks of electric distribution utilities is guaranteeing a continuous supply of electricity to their customers. The primary distribution network is a critical part of these facilities because a fault in it could affect thousands of customers. However, the complexity of this network has been increased with the irruption of distributed generation, typical in a Smart Grid and which has significantly complicated some of the analyses, making it impossible to apply traditional techniques. This problem is intensified in underground lines where access is limited. As a possible solution, this paper proposes to make a deployment of a distributed sensor network along the power lines. This network proposes taking advantage of its distributed character to support new approaches of these analyses. In this sense, this paper describes the aquiculture of the proposed network (adapted to the power grid) based on nodes that use power line communication and energy harvesting techniques. In this sense, it also describes the implementation of a real prototype that has been used in some experiments to validate this technological adaptation. Additionally, beyond a simple use for monitoring, this paper also proposes the use of this approach to solve two typical distribution system operator problems, such as: fault location and failure forecasting in power cables.

The acquisition of data in protected natural environments is subordinated to actions that do not stress the life-forms present in that environment. This is why researchers face two conflicting interests: autonomous and robust systems that minimize the physical interaction with sensors once installed, and complex enough ones to capture and process higher volumes of data. On the basis of this situation, this paper analyses the current state-of-the-art of wireless multimedia sensor networks, identifying the limitations and needs of these solutions. In this sense, in order to improve the trade-off between autonomous and computational capabilities, this paper proposes a heterogeneous multiprocessor sensor platform, consisting of an ultra-low power microcontroller and a high-performance processor, which transfers control between processors as needed. This architecture allows the shutdown of idle systems and fail-safe remote reprogramming. The sensor equipment can be adapted to the needs of the project. The deployed equipment incorporates, in addition to environmental meteorological variables, a microphone input and two cameras (visible and thermal) to capture multimedia data. In addition to the hardware description, the paper provides a brief description of how long-range (LoRa) can be used for sending large messages (such as an image or a new firmware), an economic analysis of the platform, and a study on energy consumption of the platform according to different use cases.

Environmental audio monitoring is a huge area of interest for biologists all over the world. This is why some audio monitoring system have been proposed in the literature, which can be classified into two different approaches: acquirement and compression of all audio patterns in order to send them as raw data to a main server; or specific recognition systems based on audio patterns. The first approach presents the drawback of a high amount of information to be stored in a main server. Moreover, this information requires a considerable amount of effort to be analyzed. The second approach has the drawback of its lack of scalability when new patterns need to be detected. To overcome these limitations, this paper proposes an environmental Wireless Acoustic Sensor Network architecture focused on use of generic descriptors based on an MPEG-7 standard. These descriptors demonstrate it to be suitable to be used in the recognition of different patterns, allowing a high scalability. The proposed parameters have been tested to recognize different behaviors of two anuran species that live in Spanish natural parks; the Epidalea calamita and the Alytes obstetricans toads, demonstrating to have a high classification performance.