Abstract
Control and monitoring systems capabilities are unavoidable to improve product quality, reduce production time, and to the rapid adaptation to changes in production. Therefore, it is advantageous to develop control and monitoring systems, and maximize the use of the resources already available in machine tools, without high additional costs and hard implementations. The use of Programmable Logic Controllers (PLC) on industries is growing. This sort of controller has been an election tool to attend present day requirements. It has the necessary resources to acquire and manage the information and control of the machines, and easily interacts with SCADA systems. The objective of the study described in this paper was to develop a solution for monitoring a metal casting machine, which could replace an outdated system installed on it, and find a solution to control a metal pouring process. All programs that allow the PLC to control the machine movements, and also perform the casting process, were developed during the project implementation. The results show that the developed solution is able to control the machine without the need to invest in a PC, which is a more expensive solution and with a more limited life time. © Springer International Publishing Switzerland 2017.

Abstract
Heart sounds are difficult to interpret because a) they are composed by several different sounds, all contained in very tight time windows; b) they vary from physiognomy even if the show similar characteristics; c) human ears are not naturally trained to recognize heart sounds. Computer assisted decision systems may help but they require robust signal processing algorithms. In this paper, we use a real life dataset in order to compare the performance of a hidden Markov model and several hidden semi Markov models that used the Poisson, Gaussian, Gamma distributions, as well as a non-parametric probability mass function to model the sojourn time. Using a subject dependent approach, a model that uses the Poisson distribution as an approximation for the sojourn time is shown to outperform all other models. This model was able to recreate the "true" state sequence with a positive predictability per state of 96%. Finally, we used a conditional distribution in order to compute the confidence of our classifications. By using the proposed confidence metric, we were able to identify wrong classifications and boost our system (in average) from an approximate to 83% up to approximate to 90% of positive predictability per sample.

Abstract
Credit-card fraud leads to billions of dollars in losses for online merchants. With the development of machine learning algorithms, researchers have been finding increasingly sophisticated ways to detect fraud, but practical implementations are rarely reported. We describe the development and deployment of a fraud detection system in a large e-tail merchant. The paper explores the combination of manual and automatic classification, gives insights into the complete development process and compares different machine learning methods. The paper can thus help researchers and practitioners to design and implement data mining based systems for fraud detection or similar problems. This project has contributed not only with an automatic system, but also with insights to the fraud analysts for improving their manual revision process, which resulted in an overall superior performance.

Abstract
Semi-autonomous systems are capable of sensing their environment and perform their tasks autonomously, but they may also be supervised by humans. The shared manual/automatic control makes the dynamics of such systems more complex, and undesirable and hardly predictable behaviours can arise from human-machine interaction. When these systems are used in critical applications, such as autonomous driving or robotic surgery, the identification of conditions that may lead the system to violate safety requirements is of main concern, since people actually entrust their life on them. In this paper, we extend an FMI-based co-simulation framework for cyber-physical systems with the possibility of modelling semi-autonomous robots. Co-simulation can be used to gain more insights on the system under analysis at early stages of system development, and to highlight the impact of human interaction on safety. This approach is applied to the Line Follower Robot case study, available in the INTO-CPS project. © Springer International Publishing AG 2018.

Abstract

Abstract
Current mobile devices allow the execution of sophisticated applications with the capacity for identifying the user situation, which can be helpful in treatments of mental disorders. In this paper, we present SituMan, a solution that provides situation awareness to MoodBuster, an ecological momentary assessment and intervention mobile application used to request self-assessments from patients in depression treatments. SituMan has a fuzzy inference engine to identify patient situations using context data gathered from the sensors embedded in mobile devices. Situations are specified jointly by the patient and mental health professional, and they can represent the patient's daily routine (e.g., "studying", "at work", "working out"). MoodBuster requests mental status self-assessments from patients at adequate moments using situation awareness. In addition, SituMan saves and displays patient situations in a summary, delivering them for consultation by mental health professionals. A first experimental evaluation was performed to assess the user satisfaction with the approaches to define and identify situations. This experiment showed that SituMan was well evaluated in both criteria. A second experiment was performed to assess the accuracy of the fuzzy engine to infer situations. Results from the second experiment showed that the fuzzy inference engine has a good accuracy to identify situations.