Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering

In this paper, we demonstrate how the differential Earth Mover's Distance (EMD) may be used for visual tracking in synergy with Gaussian mixtures models (GMM). According to our model, motion between adjacent frames results in variations of the mixing proportions of the Gaussian components representing the object to be tracked. These variations are computed in closed form by minimizing the differential EMD between Gaussian mixtures, yielding a very fast algorithm with high accuracy, without recurring to the EM algorithm in each frame. Moreover, we also propose a framework to handle occlusions, where the prediction for the object's location is forwarded to an adaptive Kalman filter whose parameters are estimated on line by the motion model already observed. Experimental results show significant improvement in tracking performance in the presence of occlusion.     

CAD-based simulations and design of experiments for determining thrust force in drilling operations

Drilling thrust force calculations require a large amount of experimental work, which can be greatly reduced, since an extensively validated CAD-based approach, using the DRILL3D software application, has become available. DRILL3D calculates the thrust force of both the cutting areas of the tool (main edges and chisel edge) simultaneously, which means that every simulation can substitute two separate lab experiments. Nevertheless, as the number of parameters involved is increasing, the amount of the necessary simulations becomes substantial. This is the reason that led to the combined use of the DRILL3D and the design of experiments methodology, which reduces the amount of the necessary digital experiments to an impressive degree. The main factors affecting the current analysis are the tool diameter, the web to diameter ratio, the feed rate and the cutting speed used. Using an L16 Taguchi table, a function of the developed thrust force can be calculated using the response surface methodology. This statistical modeling tool employs the regression analysis to establish the relationship between various process parameters and response.     

How student’s personality traits affect Computer Based Assessment Acceptance: Integrating BFI with CBAAM

Researchers in the Information Systems (IS) field have put considerable effort on identifying how personality affects technology acceptance. This study is a further step towards this direction within the context of Computer Based Assessment (CBA). Particularly, it investigates how the five personality factors affect the most important variables regarding CBA’s acceptance. For this purpose, 117 participants were required to complete a survey questionnaire. The questionnaire included the Big Five Inventory (BFI) questions in order to measure the five personality traits, and 23 items regarding student’s perceptions. Partial Least Squares (PLS) was used to test the measurement and the structural model. Results underline that Neuroticism has significant negative effect on Perceived Usefulness and on Goal Expectancy, Agreeableness determines Social Influence and Perceived Ease of Use, Conscientiousness defines Perceived Ease of Use, while Extroversion and Openness explain Perceived Importance. Important implications of these results are discussed.     

Palaeoenvironmental Conditions of the Mavropigi Lignites,Ptolemais Basin,Greece: A Petrological Study

The aim of this study is to interpret the palaeoenvironmental conditions established during the formation of several lignite seams at the Mavropigi deposit, Ptolemais, Greece (corehole MAK-48). In nine representative lignite samples, ash contents, as well as the contents of the elements C, H, N, O, and S were determined. Polished block sections from the same samples were examined under the microscope. The ash contents (750°C) of the studied lignite seams range between 10–29% (on dry basis). The contents of C, H, N, and O display values between 36–55%, 2.4–4.6%, 0.7–2%, and 21–37.5%, respectively, while S contents do not exceed 1.4%. Huminite is the prevailing maceral group (87–95%). All the samples, except No. 35, display a distinct prevalence in detrohuminite maceral subgroup (up to 69 vol%, mmf). Liptinite and inertinite maceral groups show low contents, which do not exceed 9% and 7%, respectively. The Mavropigi lignites are medium to low grade coals and can be regarded as peat to lignite in terms of thermal maturity. The studied lignite seams formed in fens, possibly from herbaceous plants under limnotelmatic regime. During peat deposition, conditions were very moist and intense reducing with increased bacterial activity. The ratio of plant growth and peat accumulation versus rise of water table due to the subsidence rate was not well balanced. As a result, the petrographical composition of the Mavropigi lignites is related either to a long residence time of the organic matter in the acrotelm or to a herbaceous vegetation origin.     

3-Dimensional kinematics simulation of face milling

Face milling is currently the most effective and productive manufacturing method for roughing and finishing large surfaces of metallic parts. Milling data, such as surface topomorphy, surface roughness, non-deformed chip dimensions, cutting force components and dynamic cutting behavior, are very helpful, especially if they can be accurately produced by means of a simulation program. This paper presents a novel simulation model which has been developed and embedded in a commercial CAD environment. The model simulates the true tool kinematics using the exact geometry of the cutting tool thus accurately forecasting the resulting roughness. The accuracy of the simulation model has been thoroughly verified, with the aid of a wide variety of cutting experiments. The proposed model has proved to be suitable for determining optimal cutting conditions for face milling. The software can be easily integrated into various CAD–CAM systems.     

A regenerative multiple zone model for HCCI combustion

A new conserved scalar approach, the so-called regenerative multiple zone (RMZ) model, is introduced to simulate combustion in homogeneous charge compression ignition (HCCI) engines with significant products of combustion. In this approach, two conserved scalars are introduced, the mixture fraction Z and the initial exhaust gas fraction J, to determine uniquely the state of the reactive system as a function of the two conserved scalars and time. For the numerical solution of the HCCI combustion, the conserved scalar plane is divided into different zones, which represent homogeneous reactors with constant initial exhaust gas level. Particularly, the zones are created based on the distribution of the initial exhaust gases and are mixed and regenerated at every time step during combustion in order to account for the history effects which are due to the finite rate chemistry. A proper methodology to create and initialize the new zones during the combustion, the so-called zone creation strategy (ZCS), is also proposed. For validation, the RMZ model is implemented in the 2DRD code, which is a computational fluid dynamics code that solves the governing equations for a two-dimensional reaction-diffusion problem. Initially, the consistency of the new model is validated in a one-dimensional reaction-diffusion (RD) case. Subsequently, the necessity for a proper zone creation strategy is demonstrated by a two-dimensional RD case. Next, a parametric study is performed to investigate the sensitivity of the new model on the maximum number of zones that is used. Finally, the limitations as well as the advantages of the RMZ model are discussed.     

Machine Learning Utilization for Bed Load Transport in Gravel-Bed Rivers

Three data-driven techniques, namely artificial neural networks, adaptive-network-based fuzzy inference system, and symbolic regression based on genetic programming, are employed for the prediction of bed load transport rates in gravel-bed steep mountainous streams and rivers in Idaho (U.S.A.), and the potential of several input variables is investigated. The input combinations that were tested are based, mainly, on unit stream power, stream power, and shear stress, and exhibited similarly good performance, with respect to the machine learning technique used, accentuating the importance of the regression model. The derived models are robust, generalize very well in unseen data, and generated results superior to those of some of the widely used bed load formulae, without the need to set a threshold for the initiation of motion, and consequently avoid predicting erroneous zero transport rates.     

An extended nonparametric homogeneously weighted moving average sign control chart

Parametric (or traditional) control charts are based on the assumption that the quality characteristic of interest follows a specific distribution. However, in many applications, there is a lack of knowledge about the underlying distribution. To this end, nonparametric (or distribution-free) control charts have been developed in recent years. In this article, a nonparametric double homogeneously weighted moving average (DHWMA) control chart based on the sign statistic is proposed for monitoring the location parameter of an unknown and continuous distribution. The performance of the proposed chart is measured through the run-length distribution and its associated characteristics by performing Monte Carlo simulations. The DHWMA sign chart is compared with other nonparametric sign charts, such as the homogeneously weighted moving average, generally weighted moving average (GWMA), double GWMA, and triple exponentially weighted moving average sign charts, as well as the traditional DHWMA chart. The results indicate that the proposed chart performs just as well as and in some cases better than its competitors, especially for small shifts. Finally, two examples are provided to show the application and implementation of the proposed chart.