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AbstractAnomaly detection addresses the problem of finding unexpected values in data sets. Often, these anomalies, also known as outliers, discordant values, or exceptions, describe patterns in the behavior of the data. Anomaly detection is important because it frequently involves significant and critical information in many application domains. In the case of nuclear fusion, there is a wide variety of anomalies that could be related to plasma behaviors, such as disruptions or low-high (L-H) transitions. In this context, there are known and unknown anomalies, where unknown anomalies represent the largest proportion of the total that can be found in nuclear fusion. This paper presents a study of the application of deep learning and architecture called Autoencoder to detect anomalies predicting (encode-decode) in a discharge.

This paper presents a virtual and remote laboratory of the ball and plate system with augmented reality. The ball and plate is a non-linear, multi-variable and open-loop unstable system. Due to its inherent complexity, presents challenging problems, such as: 1) point stabilization control, to carry the ball to a specific position and hold it there, and 2) trajectory tracking control, in which the goal is to make the ball follows a predefined geometric trajectory (square and circle) minimizing the tracking error. The laboratory is composed by two parts: 1) a virtual laboratory developed in Easy Java Simulations (EJS), which is a 3D interactive simulation of the system; 2) a remote laboratory (developed with EJS and LabVIEW) to connect via Internet to a pilot plant of the system, situated in the laboratory. This laboratory is used in the Systems and Control Engineering Master Program offered by Universidad Nacional de Educacion a Distancia and Universidad Complutense de Madrid.

This paper presents the architecture and the implementation of a remote laboratory for mobile robot applications. The implementation is based on Matlab and Easy Java Simulations (EJS). The aim of the remote laboratory is that students perform – via the Internet – experiments on a group of non-holonomic mobile robots. The robot application presented in this paper is leader-follower formation control using image processing.