THE ELABORATION OF THE MENTAL TRAINING PROGRAM AND EVALUATION OF ITS EFFECTS ON THE LEARNING OF YOUNG FOOTBALLERS

The purpose of this study is to know that the mental training through imagery allows improving the learning of individual defensive tactical principles in young footballers, and validate the program by experimental procedures that is to say, to study its effects on learning in young footballers. Twelve national footballers, aged 12 ± 1year participated in the experiment. Among this population, two groups including a group of physical training, technical, tactical (EPTT) and a group of physical training, technical, tactical and mental (EPTTM) were formed. The group (EPTTM) was subjected to 32 mental training sessions of 20 minutes spread over 4 months with two sessions per week. The compendium of measures was made by the experimenter. These measures consisted of all ratings assigned by judges; experts obtained by the players during the various competitions, tactical execution notes were identified and recorded in the form of penalty. The average individual tactical principles (marking, pressing, cover, superiority), footballers Group (EPTTM) increased significantly mannered penalty charges reduce the pre-test to post-test (1.93point vs 0.76 point p <0.05) ., while the players of the group (EPTT) increased insignificantly penalty charges decrease from pre-test to post-test (vs 1.66 1.86point point p> 0.05). The homogeneity of the groups (EPTTM) and (EPTT) during the pre-test, allows to suggest that mental training produces a better learning of the principles of individual tactics. This assessment allowed us to test the hypothesis raised. Indeed, the mental training associated with physical training, technical, tactical causes an improved learning of individual defense principles.   Article visualizations

An Improved MPPT Interleaved Boost Converter for Solar Electric Vehicle Application

International audienceAn interleaved boost dc/dc converter is developed featuring smaller input/output filters, faster dynamic response and lower device stress than conventional designs, for solar electric vehicle (SEV) applications. The converter is connected between the photovoltaic power generation and dc bus in a multisource energy storage system of a SEV. Typically, interleaved converters require a current control loop to reduce the input current ripples, the output voltage ripples, and the size of passive components with high efficiency. A Maximum Power Point Tracking (MPPT) controller for a Photovoltaic (PV) solar system associated to backup source (Battery) guarantees an uninterrupted power supply and assist the propulsion of the vehicle during transients and recover energy during regenerative braking. The design, construction, and testing of an experimental hardware p rototype is presented, with the test results included

FC/Battery Power Management for Electric Vehicle Based Interleaved DC-DC Boost Converter Topology

International audienceDue to the fact that the environmental issues have become more serious recently, interest in renewable energy systems, such as, fuel-cells (FCs) has increased steadfastly. Among many types of FCs, proton exchange membrane FC (PEMFC) is one of the most promising power sources due to its advantages, such as, low operation temperature, high power density and low emission. However, using only PEMFC for electric vehicle may not be feasible to satisfy the peak demand changes especially during accelerations and braking. So, hybridizing PEMFC and an energy storage system (ESS) decreases the FC cost and improves its performance and life. Battery (B) appears to be the most powerful candidate to hybridize with PEMFC for vehicular applications. Therefore, the performance of PEMFC/B hybridization is limited considerably by the performance of the converter. Thus, a suitable dc-dc converter topology is required. Various isolated and nonisolated converter topologies for FC applications have been proposed in literature. The objective of this study is to design and simulate a fuel cell - interleaved boost dc-dc converter (FC-IBC) for hybrid power systems in electric vehicle application, in order to decrease the FC current ripple. Therefore Energetic efficiency can also be improved. A control strategy capable of determining the desired FC power and keeps the dc voltage around its nominal value by supplying propulsion power and recuperating braking energy is designed and tested with an urbane electric vehicle model

Artificial neural network for solving the inverse kinematic model of a spatial and planar variable curvature continuum robot

In this paper, neural networks are presented to solve the inverse kinematic models of continuum robots. Firstly, the forward kinematic models are calculated for variable curvature continuum robots. Then, the forward kinematic models are implemented in the neural networks which present the position of the continuum robot’s end effector. After that, the inverse kinematic models are solved through neural networks without setting up any constraints. In the same context, to validate the utility of the developed neural networks, various types of trajectories are proposed to be followed by continuum robots. It is found that the developed neural networks are powerful tool to deal with the high complexity of the non-linear equations, in particular when it comes to solving the inverse kinematics model of variable curvature continuum robots. To have a closer look at the efficiency of the developed neural network models during the follow up of the proposed trajectories, 3D simulation examples through Matlab have been carried out with different configurations. It is noteworthy to say that the developed models are a needed tool for real time application since it does not depend on the complexity of the continuum robots' inverse kinematic models

The Level of Community Cohesiveness Under Psychological Pressure and Control Center for Emerging Football Players U17

The objective of this study is to know the level the group cohesiveness and the type of relationship between it and the psychological pressure and control center for emerging footballers under the 17 years. The descriptive approach of the study was based on a sample of 70 young footballers between the ages of 15 – 17 years of the professional Algerian championship who are still studying. The measurement of cohesiveness of the group, consisting of 34 phrases divided into four dimensions, was used on 5 dimensions, and on the scale of the center control the internal and external consists of 20 phrases. We found an average level of group cohesiveness in all dimensions and in the total score of the group cohesiveness scale with mean and standard deviation estimated at (118.5 ± 13.13), high level of psychological stress and total degree of psychological stress with mean and standard deviation of 163.38 ± 10.67). The average level in the remote control center and high in the external control center, there is a statistically significant correlation between group cohesiveness and psychological stress, and a statistically significant correlation between the cohesiveness of the group and both the internal control center and the external control center. The psychological pressures negatively affect the cohesiveness of the group while the control center with its both sides the internal and external does not affect the cohesiveness of the group

Une approche pour la navigation optimale dans des situations d'incertitude de localisation

The basic functions of an autonomous vehicle typically involve navigating from one point to another in the world by following a reference path and analyzing the traversability along this path to avoid potential obstacles. What happens when the vehicle is subject to uncertainties in its localization? All its capabilities, whether path following or obstacle avoidance, are affected by this uncertainty, and stopping the vehicle becomes the safest solution. In this work, we propose a framework that optimally combines path following and obstacle avoidance while keeping these two objectives independent, ensuring that the limitations of one do not affect the other. Absolute localization uncertainty only has an impact on path following, and in no way affects obstacle avoidance, which is performed in the robot’s local reference frame. Therefore, it is possible to navigate with or without prior information, without being affected by position uncertainty during obstacle avoidance maneuvers. We conducted tests on an EZ10 shuttle in the PAVIN experimental platform to validate our approach. These experimental results show that our approach achieves satisfactory performance, making it a promising solution for collision-free navigation applications for mobile robots even when localization is not accurate

Active Disturbance Rejection Control of an Interleaved High Gain DC-DC Boost Converter for Fuel Cell Applications

In this paper, a simplified and robust control strategy of an interleaved high gain DC/DC boost converter (IHGBC) is proposed in order to enhance DC bus voltage regulation in proton exchange membrane fuel cell (PEMFC) applications. The fluctuation of the energy source voltage and external load, and the change in system parameters lead to the instability of output voltage. Based on the creation of an average state space model of the DC/DC boost converter, the proposed controller is designed based on a linear active disturbance rejection control (LADRC), which has an external voltage loop and an internal current loop to meet the output voltage requirements under parameters uncertainties and disturbances. The effectiveness of the proposed approach strategy and its superiority were examined under different operating conditions and scenarios. Simulation and experiment results showed the efficiency and robustness of the suggested approach and the great effectiveness in the reference tracking and disturbance rejection

Dual occupancy and knowledge maps management for optimal traversability risk analysis

International audienceIn a context of autonomous driving, perception of the surrounding is a crucial task. It characterizes the vehicle's ability to simultaneously model its surroundings accurately and maintain its position in the environment. In this article, a new framework of mobile robot perception and risk assessment is proposed. Our approach aims to leverage the simultaneous combination of the standard occupancy grid map with a new map that we have called "knowledge map". This proposal was motivated by the fact that risk arises not only from obstacles but also from the lack of knowledge. Using this framework, we are able to assess the risk, mainly of collision, over a given path P and therefore compute an optimal navigation control of the robot. Thanks to the proposed Bayesian framework the paper also shows how we can combine both local measurements and existing map (eg. OpenStreetMap) and also take account of the robot's localization errors