Machine Learning for Handwriting Recognition

With the knowledge of current data about particular subject, machine learning tries to extract hidden information that lies in the data. By applying some mathematical functions and concepts to extract hidden information, machine learning can be achieved and we can predict output for unknown data. Pattern recognition is one of the main application of ML. Patterns are usually recognized with the help of large image data-set. Handwriting recognition is an application of pattern recognition through image. By using such concepts, we can train computers to read letters and numbers belonging to any language present in an image. There exists several methods by which we can recognize hand-written characters. We will be discussing some of the methods in this paper

Simulation de la couche limite hypersonique autour d’un corps émoussé du type sphère-cylindre

Une des contraintes majeures intervenant dans la conception de la protection thermique d’engin spatial hypersonique est liée à l’échauffement de la paroi. Elle est du principalement à la présence de chocs forts au sein de l’écoulement, à la dissipation visqueuse dans les couches limites et aux effets de thermochimie. L’importance de la différence de température entre la paroi et l’écoulement est source de flux de chaleur internes, transmis en partie à la paroi à travers la couche limite. Un des outils numériques qui permet une telle prédiction et simule la couche de choc complète est constitué par les méthodes de résolution des couches limites hypersoniques à la base des schémas à propriété TVD. Cette recherche concerne la simulation numérique d’écoulements hypersoniques visqueux et réactifs autour de configuration du type sphère – cylindre. Le but est d’étudier les variations du flux de chaleur, de la pression, des contraintes et des concentrations de certaines espèces ; et d’analyser la sensibilité de la couche limite hypersonique à la température de la paroi, aux différents modèles thermodynamiques et chimiques.Mots clés : fluide dynamique; écoulement réactif hypersonique; ondes de choc; thermochimique

Phénomène de pincement de l'écoulement secondaire entre deux sphères contrarotatives

Cette étude numérique concerne l'évolution de l'écoulement de Couette sphérique, entre deux sphères contrarotatives, avec l'augmentation du nombre de Reynolds. Le but de l'étude est la détermination de l'apparition et de l'évolution du phénomène de pincement de l'écoulement secondaire dans le plan méridien avec l'augmentation du nombre de Reynolds. L'écoulement est modélisé par les équations de Navier-Stokes, avec des conditions initiales et aux limites appropriées. La forme adimensionnelle du modèle fait apparaître trois paramètres de contrôle: le rapport d'aspect et le nombre de Rossby, arbitrairement fixés à 0.5 et -0.5, respectivement, et le nombre de Reynolds est varié entre 100 et 500. Les équations modélisantes sont résolues avec la méthode des volumes finis. Pour le nombre de Reynolds initial, l'écoulement secondaire, dans le plan méridien, se manifeste sous la forme de deux cellules d'Eckman dans chaque hémisphère. Avec l'augmentation du nombre de Reynolds, les deux cellules près de la sphère intérieure sont déformées par un phénomène de pincement prés de l'équateur. Le pincement apparaît faiblement avec le nombre de Reynolds 200; mais se resserre continuellement avec l'augmentation du nombre de Reynolds entre 200 et 500.Mots clés: contrarotative; sphères; pincement; écoulement; simulation; numérique. This numerical study concerns the evolution of the spherical Couette flow between two counter-rotating spheres with the increase of the Reynolds number. The purpose of the study is the determination of the appearance and the evolution of the pinching phenomenon of the secondary flow in the meridian plane with the increase of the Reynolds number. The flow is modeled by the Navier-Stokes equations, with appropriate initial and boundary conditions. The non dimensional form of the model shows three control parameters: the aspect ratio and the Rossby numbers arbitrarily set equal to 0.5 and -0.5, respectively, and the Reynolds number is varied between 100 and 500. The model equations are solved with the finite volume method. For the initial Reynolds number, the secondary flow in the meridian plane is manifested in the form of two Eckman cells in each hemisphere. With the increase of the Reynolds number, the two cells near the inner sphere are deformed by a pinch phenomenon near the equator. The pinch appears weakly with the low Reynolds number 200; but tightens continuously with the increase of the Reynolds number between 200 and 500.Key words: counter-rotating; spheres; pinched; flow; simulation; numerical

Development of a Secondary Crash Identification Algorithm and occurrence pattern determination in large scale multi-facility transportation network

Secondary crash (SC) occurrences are non-recurrent in nature and lead to significant increase in traffic delay and reduced safety. National, state, and local agencies are investing substantial amount of resources to identify and mitigate secondary crashes in order to reduce congestion, related fatalities, injuries, and property damages. Though a relatively small portion of all crashes are secondary, their identification along with the primary contributing factors is imperative. The objective of this study is to develop a procedure to identify SCs using a static and a dynamic approach in a large-scale multimodal transportation networks. The static approach is based on pre-specified spatiotemporal thresholds while the dynamic approach is based on shockwave principles. A Secondary Crash Identification Algorithm (SCIA) was developed to identify SCs on networks. SCIA was applied on freeways using both the static and the dynamic approach while only static approach was used for arterials due to lack of disaggregated traffic flow data and signal-timing information. SCIA was validated by comparison to observed data with acceptable results from the regression analysis. SCIA was applied in the State of Tennessee and results showed that the dynamic approach can identify SCs with better accuracy and consistency. The methodological framework and processes proposed in this paper can be used by agencies for SC identification on networks with minimal data requirements and acceptable computational time

Prediction of secondary crash frequency on highway networks

Secondary crash (SC) occurrences are major contributors to traffic delay and reduced safety, particularly in urban areas. National, state, and local agencies are investing substantial amount of resources to identify and mitigate secondary crashes to reduce congestion, related fatalities, injuries, and property damages. Though a relatively small portion of all crashes are secondary, determining the primary contributing factors for their occurrence is crucial. The non-recurring nature of SCs makes it imperative to predict their occurrences for effective incident management. In this context, the objective of this study is to develop prediction models to better understand causal factors inducing SCs. Given the count nature of secondary crash frequency data, the authors used count modeling methods including the standard Poisson and Negative Binomial (NB) models and their generalized variants to analyze secondary crash occurrences. Specifically, Generalized Ordered Response Probit (GORP) framework that subsumes standard count models as special cases and provides additional flexibility thus improving predictive accuracy were used in this study. The models developed account for possible effects of geometric design features, traffic composition and exposure, land use and other segment related attributes on frequency of SCs on freeways. The models were estimated using data from Shelby County, TN and results show that annual average daily traffic (AADT), traffic composition, land use, number of lanes, right side shoulder width, posted speed limits and ramp indicator are among key variables that effect SC occurrences. Also, the elasticity effects of these different factors were also computed to quantify their magnitude of impact

Truck Parking utilization analysis using GPS data

Unavailability of sufficient parking spaces during various periods at rest areas results in illegal and unsafe parking at on/off-ramps and other unauthorized areas, which may lead to traffic safety hazards. In this research, the authors attempt to understand truck parking utilization by developing econometric models by using truck global positioning system (GPS) data to predict truck parking utilization at rest areas to improve truck parking management and ensure proper use of parking spaces. Count models, including Poisson and negative binomial models, were developed in addition to generalized ordered-response probit (GORP) models to understand the factors that affect truck parking utilization. The GORP model that subsumes the Poisson model as a special case was found to provide the best data fit. The model results suggest that several factors positively contribute to truck parking utilization (e.g., truck volume on the adjacent roadway, number of lanes) at rest areas, whereas factors such as on/off-ramp violations decrease truck parking utilization. In addition, parking utilization was found to vary considerably throughout the day