The inclusion of intelligent systems in the modern industry is demanding the development of the automatic monitoring and continuous analysis of the data related to entire processes, this is a challenge of the industry 4.0 for the energy management. In this regard, this chapter proposes a novelty detection methodology based on Self-Organizing Maps (SOM) for Power Quality Monitoring. The contribution and originality of this proposed method consider the characterization of synthetic electric power signals by estimating a meaningful set of statistical time-domain based features. Subsequently, the modeling of the data distribution through a collaborative SOM’s neuron grid models facilitates the detection of novel events related to the occurrence of power disturbances. The performance of the proposed method is validated by analyzing and assessing four different conditions such as normal, sag, swell, and fluctuations. The obtained results make the proposed method suitable for being implemented in embedded systems for online monitoring.
Part of the book: Artificial Intelligence
In Vitro culture is a technique commonly used for plant research. Nevertheless, it is more expensive than traditional methods of production, due to the use of the culture medium gelling agent called agar. Recent studies have been searching for alternative substances in raw materials with the same characteristics but which can be extracted easier than agar. The dietary fiber of the nopal cactus (Opuntia) is a rich source of hydrocolloids (pectin and mucilage). These hydrocolloids have the ability to gel in combination with the indicated solution. In this chapter, we will focus on the study of the hydrocolloids from nopal cactus to replace agar partially and/or totally as a gelling agent using in vitro culture media benefiting from the molecular structure and mechanical properties of the compounds.
Part of the book: Recent Research and Advances in Soilless Culture