FORENSIC EXAMINATION OF THE ESTABLISHMENT OF CAUSES OF FAULTS IN THE “ACTIVE CURVE SYSTEM” STABILIZATION SYSTEM OF MERCEDES-BENZ GLS500 4MATIC

Establishing of causes and circumstances that could affect failure of both parts individually and mechanisms generally of the “Active curve system” active stabilization system in Mercedes-benz GLS500 4MATIC cars is considered. The relevance of this scientific paper is due to novelty of installing an active stabilizer on vehicles which ensures minimal car body roll. Design, a principle of work and forensic expert research while inspection and study of the “Active curve system” active stabilization system of Mercedes-benz GLS500 4MATIC using modern equipment and research methods are presented. Measures to prevent failure of structural parts and systems are considered, which will ensure reliable and safe work of the “Active curve system” active stabilization system in the process of operation. The system allows you to keep a car within the path set by a driver in various driving modes (acceleration, braking, moving along a straight line, while road curves and free rolling). Determination of occurrence of a traffic collision is carried out by comparing actions of a driver and parameters of a car. In case when a driver’s actions (desired driving parameters) differ from the actual vehicle motion parameters, the “Active curve system” active stabilization system recognizes the situation as uncontrolled and starts working. In case of understeer, the “Active curve system” active stabilization system prevents a car from slipping out of turning trajectory by slowing down a rear inner wheel and changing an engine torque. In case of oversteer, skidding of a car during turns is prevented by slowing down a front outer wheel and changing an engine torque. Operation in this case is of a cyclical nature: pressure increase, pressure containment and pressure relief in the “Active curve system” active stabilization system. The system that combines the system of electronic stability control, steering and car suspension is called vehicle dynamics management integrated system.

Compartmentalized mathematical model to predict future number of active cases and deaths of COVID-19

In December 2019, China reported a series of atypical pneumonia cases caused by a new Coronavirus, called COVID-19. In response to the rapid global dissemination of the virus, on the 11th of Mars, the World Health Organization (WHO) has declared the outbreak a pandemic. In light of this situation, this paper intends to analyze and improve the current SEIR models to better represent the behavior of the COVID-19 and accurately predict the outcome of the pandemic in a given social, economic and political scenario. We present a novel generalized Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model and test it using a global optimization algorithm with data collected from the WHO. Our main results were: (a) our model was able to accurately fit the data of all countries tested (b) it is possible to predict values for one week ahead with errors in the order of 15% for the number of cases and 30% in the number of deaths for all countries; (c) predictions are better for countries where the active cases curve already reached the maximum; the error being in the order of 10% in the number of cases and 20% in the number of deaths; (d) for countries where the active curve is still growing, different optimization solutions can be found that fit the data; so, to predict future behavior in this scenarios some of the model coefficients should be estimated from outside sources or based on generalized results from other countries according to their health policies of social distance, quarantining and case test and tracing.

Model-Free, Occlusion Accommodating Active Contour Tracking

This study investigates tracking in monocular image sequences by a model-free, occlusion accommodating active contour method. The objective functional contains a model-free shape tracking term to constrain the active curve in a frame to have a shape which approximates as closely as possible the shape of the active curve in the preceding frame. It complements a kernel photometric tracking term which constrains the active curve in a frame to enclose an intensity profile that matches as closely as possible the profile within the curve in the preceding frame. This data term is in kernel form so as to forgo image modeling. The method, which is exclusively driven by the curve/level set evolution equations derived from the objective functional Euler-Lagrange conditions, can track several objects independently. Experimental validation includes examples with infrared imaging, occlusion, clutter, and articulated motion.