Estimation of fundamental diagrams in large-scale traffic networks with scarce sensor measurements

International audienceThe macroscopic fundamental diagram (MFD) relates space-mean flow density and the speed of an entire network. We present a method for the estimation of a "normalized" MFD with the goal to compute specific Fundamental Diagram in places where loop sensors data is no available. The methodology allows using some data from different points in the city and possibly combining several kinds of information. To this aim, we tackle at least three major concerns: the data dispersion, the sparsity of the data, and the role of the link (with data) within the network. To preserve the information we decided to treat it as two-dimensional signals (images), so we based our estimation algorithm on image analysis, preserving data veracity until the last steps (instead of first matching curves that induce a first approximation). Then we use image classification and filtering tools for merging of main features and scaling. Finally, just the Floating Car Data (FCD) is used to map back the general form to the specific road where sensors are missing. We obtained a representation of the street by means of its likelihood with other links within the same network

Análise Dinâmica das Emoções através da Inteligência Artificial

Emotions have been demonstrated to be an important aspect of human intelligence and to play a significant role in human decision-making processes. Emotions are not only feelings but also processes of establishing, maintaining or disrupting the relation between the organism and the environment. In the present paper, several features of social and developmental Psychology are introduced, especially concepts that are related to Theories of Emotions and the Mathematical Tools applied in psychology (i.e., Dynamic Systems and Fuzzy Logic). Later, five models that infer emotions from a single event, in AV-Space, are presented and discussed along with the finding that fuzzy logic can measure human emotional states.Se ha comprobado que las emociones son un aspecto importante en la inteligencia humana y que desempeñan un rol significativo en el proceso humano de toma de decisiones. Las emociones no son solo sentimientos, sino también procesos de establecimiento, mantenimiento o interrupción de la relación existente entre el organismo y el ambiente. En el presente trabajo se describen algunas características de la psicología social y del desarrollo, especialmente los conceptos relacionados con las emociones y las teorías de la emoción, así como las herramientas matemáticas aplicadas en la psicología (i. e., sistemas dinámicos y lógica difusa). Luego se presentan y se discuten cinco modelos que infieren la emoción a partir de un evento, en el espacio Arousal-Valence (A-V), para encontrar que es posible usar la lógica difusa para medir los estados emocionales humanos.Se tem comprovado que as emoções são um aspeto importante na inteligência humana e que desempenha um papel significativo no processo de tomada de decisões humano. As emoções não são só sentimentos, mas também processos de estabelecimento, manutenção ou interrupção da relação existente entre o organismo e o ambiente. No presente trabalho descrevem-se algunas características da psicologia social e do desenvolvimento, especialmente os conceitos relacionados com emoções e as teorias da Emoção e as ferramentas matemáticas aplicadas na Psicologia (i.e., Sistemas dinámicos y Lógica difusa). Após, se apresentam e se discutem cinco modelos que inferem a emoção a partir de um evento, no espaço Arousal-Valence (A-V), encontrando que a lógica difusa pode usar-se para medir os estados emocionais humanos

Salud y bienestar

Se sugiere fortalecer la estrategia de adquisición de vacunas covid-19 con base en la cooperación internacional y la participación de privados en el proceso. También pone a disposición una serie de innovaciones tecnológicas recientes desarrolladas desde las universidades en materia de diagnóstico y atención de covid- 19, así como en el frente de logística de vacunación. El texto hace un análisis de las necesidades de cambio que tiene el sistema de salud colombiano y propone algunas alternativas para conseguirlo

Influencia de las técnicas somáticas en las prácticas pedagógicas y el bienestar emocional en contextos escolares

370 P.PRIMERA EDICIO

Binning application in low-dimensional metagenomic sequences: performance of Barnes-Hut t-Stochastic Neighbor Embeddings, assessment of internal cluster validity indices

Metagenomic studies aim to reconstruct the structure of microbial communities through the use of DNA sequence data of complex composition. To this end, they generally embed multidimensional data into low dimensional spaces followed by a binning process. The performance of the dimensionality reduction techniques, the clustering methods, and the internal cluster validity indices vary depending on the biological, statistical and computational features that are part of the metagenomic analysis, yet it is seldom evaluated systematically. The explained problematic was explored through an unsupervised binning of metagenomic DNA sequences, based on the Subtractive and Fuzzy c-means algorithms applied to the two- and three-dimensional metagenomic sequences obtained via the Barnes-Hut t-Stochastic Neighbor Embedding (BH-SNE) algorithm in conjunction with Principal Component Analysis (PCA), with the aim of assessing the performance of the BH-SNE including and not including a preliminary PCA, besides the assessment of four Internal Cluster Validity Indices (ICVI) that conditioned the clustering procedure. In addition, the assessment of the ICVIs demonstrated that the Silhouette index had the best performances based on the median values of the F measure. Moreover, Silhouette index was also the most consistent index obtaining the highest values of F median in two- and three-dimensional treatments. In the case of high AAI ranges, the Silhouette index had equal results compared with Calinski-Harabasz index in terms of highest values of F median in three-dimensional treatment, although there were differences between their performance in two-dimensional treatments. In particular, Dunn index generated the worst performances in the low AAI percentages, while the Davies-Bouldin index was the worst in high AAI percentages. Additionally, the Dunn and Davies-Bouldin indices were the most consistent generating the lowest F median values. Moreover, the results of this research suggest that the biology of the metagenomic sequences could have an incidence over the best ICVIs performances. Finally, it was possible to determine that the highest F median values were obtained by the ICVIs in 3D embeddings, with equal results for BH-SNE including and not including preliminary PCA. Furthermore, it was also demonstrated that there was no significance between the results that included or not included a preliminary PCA. In terms of consistency, it was not possible to determine which was the most consistent treatment (2D or 3D embedding with BH-SNE including and not including preliminary PCA) that led the ICVIs to obtaining the best and worst F median results

Estimation of fundamental diagrams in large-scale traffic networks with scarce sensor measurements

International audienceThe macroscopic fundamental diagram (MFD) relates space-mean flow density and the speed of an entire network. We present a method for the estimation of a "normalized" MFD with the goal to compute specific Fundamental Diagram in places where loop sensors data is no available. The methodology allows using some data from different points in the city and possibly combining several kinds of information. To this aim, we tackle at least three major concerns: the data dispersion, the sparsity of the data, and the role of the link (with data) within the network. To preserve the information we decided to treat it as two-dimensional signals (images), so we based our estimation algorithm on image analysis, preserving data veracity until the last steps (instead of first matching curves that induce a first approximation). Then we use image classification and filtering tools for merging of main features and scaling. Finally, just the Floating Car Data (FCD) is used to map back the general form to the specific road where sensors are missing. We obtained a representation of the street by means of its likelihood with other links within the same network

Application of the continuous wavelet transform in the extraction of directional data on rtm imaging condition wavefields

Low-frequency artifacts in reverse time migration result from unwanted cross-correlation of the source and receiver wavefields at non-reflecting points along ray-paths. These artifacts can hide important details in migrated models and increase poor interpretation risk. Some methods have been proposed to avoid or reduce the number of these artifacts, preserving reflections, and improving model quality, implementing other strategies such as modification of the wave equation, proposing other imaging conditions, and using image filtering techniques. One of these methods uses wavefield decomposition, correlating components of the wavefields that propagate in opposite directions. We propose a method for extracting directional information from the RTM imaging condition wavefields to obtain characteristics allowing for better, more refined imaging. The method works by separating directional information about the wavefields based on the continuous wavelet transform (CWT), and the analysis of the main changes on the frequency content revealed within the scalogram obtained by a Gaussian wavelet family. Through numerical applications, we demonstrate that this method can effectively remove undesired artifacts in migrated images. In addition, we use the Laguerre-Gauss filtering to improve the results obtained with the proposed method.Los artefactos de baja frecuencia en la migración de tiempo reverso resultan de la no deseada correlación cruzada de campos de onda de fuentes y receptores en puntos no reflejantes a lo largo de la trayectoria de los rayos. Esos artefactos pueden ocultar detalles importantes en modelos migrados y pueden incrementar el riesgo de mala interpretación. Algunos métodos han sido propuestos para evitar o reducir esos artefactos, preservando reflexiones, y mejorando la calidad del modelo, implementando otras estrategias como las modificaciones de la ecuación de onda, proponiendo otras condiciones de representación y usando técnicas de filtrado de imágenes. Uno de esos métodos usa descomposición del campo de onda, correlacionando componentes de campos de ondas que se propagan en direcciones opuestas. Proponemos un método de extracción de información de campos de ondas para obtener características que permitan una mejor y más refinada representación de modelos de estructuras del subsuelo. El método trabaja a través de separación de información de campos de ondas basados en la transformada continua de ondícula (TCW) y análisis de cambios en el contenido frecuencial, revelado dentro del escalograma obtenido a través de una familia de ondículas gaussianas. A través de aplicaciones numéricas, demostramos que este método puede remover efectivamente artefactos indeseados en modelos migrados. Además, usamos filtrado de Laguerre-Gauss para mejorar resultados finales obtenidos con el método propuesto

Estimation of fundamental diagrams in large-scale traffic networks with scarce sensor measurements

International audienceThe macroscopic fundamental diagram (MFD) relates space-mean flow density and the speed of an entire network. We present a method for the estimation of a "normalized" MFD with the goal to compute specific Fundamental Diagram in places where loop sensors data is no available. The methodology allows using some data from different points in the city and possibly combining several kinds of information. To this aim, we tackle at least three major concerns: the data dispersion, the sparsity of the data, and the role of the link (with data) within the network. To preserve the information we decided to treat it as two-dimensional signals (images), so we based our estimation algorithm on image analysis, preserving data veracity until the last steps (instead of first matching curves that induce a first approximation). Then we use image classification and filtering tools for merging of main features and scaling. Finally, just the Floating Car Data (FCD) is used to map back the general form to the specific road where sensors are missing. We obtained a representation of the street by means of its likelihood with other links within the same network