3D model based map building

Comunicación presentada en el IX Workshop de Agentes Físicos (WAF'2008), Vigo, 11-12 septiembre 2008.

A conceptual map about alternating current circuits

Teachers are deeply concerned on how to be more effective in our task of teaching. We must organize the contents of our specific area providing them with a logical configuration, for which we must know the mental structure of the students that we have in the classroom. We must shape this mental structure, in a progressive manner, so that they can assimilate the contents that we are trying to transfer, to make the learning as meaningful as possible. In the generative learning model, the links before the stimulus delivered by the teacher and the information stored in the mind of the learner requires an important effort by the student, who should build new conceptual meanings. That effort, which is extremely necessary for a good learning, sometimes is the missing ingredient so that the teaching-learning process can be properly assimilated. In electrical circuits, which we know are perfectly controlled and described by Ohm's law and Kirchhoff's two rules, there are two concepts that correspond to the following physical quantities: voltage and electrical resistance. These two concepts are integrated and linked when the concept of current is presented. This concept is not subordinated to the previous ones, it has the same degree of inclusiveness and gives rise to substantial relations between the three concepts, materializing it into a law: The Ohm, which allows us to relate and to calculate any of the three physical magnitudes, two of them known. The alternate current, in which both the voltage and the current are reversed dozens of times per second, plays an important role in many aspects of our modern life, because it is universally used. Its main feature is that its maximum voltage is easily modifiable through the use of transformers, which greatly facilitates its transfer with very few losses. In this paper, we present a conceptual map so that it is used as a new tool to analyze in a logical manner the underlying structure in the alternate current circuits, with the objective of providing the students from Sciences and Engineering majors with another option to try, amongst all, to achieve a significant learning of this important part of physics.

Constrained self-organizing feature map to preserve feature extraction topology

In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-dimensional constrained self-organizing map preserving the input topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high-performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example has been included to focus on validate the nD-SOM-PINT proposal in other domain than the individual trajectory. Results confirm the high accuracy of the nD-SOM-PINT outperforming previous methods aimed to classify the same datasets.