Javier Gamez Garcia

This work presents a Digital Twin aimed at incorporating proactive quality control capabilities into industrial vision systems applied to the manufacturing of automotive components. The proposed approach combines an existing industrial inspectionsystem with a multi-agent platform developed using SPADE and deep learning-based models for the automatic identification of surface defects. The Digital Twin enables the synchronization of information generated by the physical inspection system witha digital representation oriented toward defect analysis and production-process traceability. For automatic defect identification, models based on the YOLO architecture are trained using images acquired from the industrial environment. In addition, atraceability analysis mechanism is proposed to associate specific defects with different stages of the manufacturing process. The proposed architecture has been defined and functionally validated in an industrial environment for headlamp lens inspection,providing a basis for future supervision and proactive quality control strategies in manufacturing.

Laboratory work is essential in most applied science and engineering specific coursers. The continuous advances in Information and Communication Technologies be able the use of online laboratories (OLs). Their use is increased due to the advantages they offer compared to conventional laboratories, as unfortunately verified during Covid-19 pandemic. OLs and Learning Management Systems (LMSs) are key technologies in the learning process field. Normally, the integration of OLs with LMSs is carried out using proprietary or ad-hoc solutions. Furthermore, there are standards that can be used for this integration, such as: LTI, IEEE P92741.1 (xAPI), SCORM and IEEE1876. This work elaborates a survey which has been provided to the experts of online laboratories for them to complete. Gathered information allowed authors to diagnose the level of knowledge and use of the main standards for creating and integrating OLs. In fact, the analysis of this information confirms that there is a lack of criteria for selecting one standard against the others and how use it in the online laboratory development. This work gives guidelines for selecting the most appropriate standard according to desired characteristics of the online laboratory under development.

The ripeness and sanitary state of olive fruits are key factors in the final quality of the virgin olive oil (VOO) obtained. Since even a small number of damaged fruits may significantly impact the final quality of the produced VOO, the olive inspection in the oil mill reception area or in the first stages of the productive process is of great interest. This paper proposes and validates an automatic defect detection system that utilizes infrared images, acquired under regular operating conditions of an olive oil mill, for the detection of defects on individual fruits. First, the image processing algorithm extracts the fruits based on the iterative application of the active contour technique assisted with mathematical morphology operations. Second, the defect detection is performed on the segmented olives using a decision tree based on region descriptors. The final assessment of the algorithm suggests that it works effectively with a high detection rate, which makes it suitable for the VOO industry.

This work dials with the integration of online laboratories into Learning Management Systems. In fact, this work emphasizes the pedagogical advantages that could be achieved with this integration as well as the involved actors, being mainly: students, lecturers and laboratory owners. It also makes an analysis of each actor's requirements and how they can be satisfied by the integration. To achieve this, the use of standards is essential. Authors use an example of online laboratory and integrate it into LMSs by different paths but using the more widespread standards. Finally, the achieved advantages are discussed.

The presence of minor compounds in virgin olive oils has been proven to play multiple positive roles in health protection, encouraging its production. The key factors that influence the oil quality are ripening stages and the state of health of the fruit. For this reason, at the oil mill’s reception yard, fruits are visually inspected and separated according to their external appearance. In this way, the process parameters can be better adjusted to improve the quantity and/or quality of olive oil. This paper presents a proposal to automatically determine the oil quality before being produced from a previous inspection of the incoming fruits. Expert assessment of the fruit conditions guided the image processing. The proposal has been validated through the analysis of 74 batches of olives coming from an oil mill. Best correlation results between the image processing and the analytical data were found in the acidity index, peroxide values, ethyl ester, polyphenols, chlorophylls, and carotenoids.