Stage Of Preliminary Training Research Articles

Методика обучения штрафному броску детей на этап предварительной подготовки в баскетболе

The purpose of the study is to describe the technique of a free throw with one hand based on the biokinematic scheme of this movement; to develop and justify the methodology for performing the free throw technique with one hand; to determine the methods and means that will contribute to the development and improvement of this technical element. In the article, we consider the conditions for the need to teach children a free throw at the stage of preliminary preparation. First, the number of free throws hit is a significant part of the total score of the game. Secondly, the movements of the hands and feet performed during the free throw process are the basis for the formation of the skill of throwing the ball into the basket from different positions. Third, the effectiveness of hitting a free throw is a way of psychological impact on the opposing team. The scientific novelty lies in the development and justification of a methodology for teaching one-handed free throw techniques with the help of selected methods and means of children at the stage of preliminary training, which will contribute to the development of this element.

General questions of building the stage of pre-competition training

Success in competition is ensured by long-term preparation, but direct pre-competition training (DPT) is of fundamental importance, with the help of which athletes must bring themselves to a state of high or the highest working capacity.
 Modern views on the essence and construction of preliminary training stage most intensively began to be formed since the 1960s. The reason for this was the appearance of such fundamental projects, such as "Periodization of sports training" by L.P. Matveva, "The current system of sports training" by N.G. Ozolina, "The coach of athletics trainer" ed. L.S. Khomenkovoi.
 The success in competitions is provided by long-term training, but fundamental importance has a direct pre-competition training (DPT), by which athletes should lead themselves to a state of high or the highest efficiency. If the goal of competition is only of training or control nature and athlete is not eager to achieve a high result, then DPT is not necessary before the starts. Such a combination of the process of training and participation in competitions may occur in the first competitions at the beginning of the competitive period. However, when there is a participating in high-level competitions or even more in the culminating competitions, in such an event, DPT is essential.
 Mistakes in the building of the phase of the DPT are often the reason not only for an unsuccessful speech in the main competition, but it is also the reason for a slowed down growth of achievements and even deterioration of them. All training of an athlete can be considered successful if he shows the best result in the crucial competition with a significant progress in his accomplishments.

МОДЕЛИРОВАНИЕ СИСТЕМЫ ПОДГОТОВКИ ДОШКОЛЬНИКОВ ПО СПОРТИВНОЙ ГИМНАСТИКЕ НА СПОРТИВНО-ОЗДОРОВИТЕЛЬНОМ ЭТАПЕ

МОДЕЛИРОВАНИЕ СИСТЕМЫ ПОДГОТОВКИ ДОШКОЛЬНИКОВ ПО СПОРТИВНОЙ ГИМНАСТИКЕ НА СПОРТИВНО-ОЗДОРОВИТЕЛЬНОМ ЭТАПЕ

A dynamic behavioural traffic assignment model with strategic agents

Foresee traffic conditions and demand is a major issue nowadays that is very often approached using simulation tools. The aim of this work is to propose an innovative strategy to tackle such problem, relying on the presentation and analysis of a behavioural dynamic traffic assignment.The proposal relies on the assumption that travellers take routing policies rather than paths, leading us to introduce the possibility for each simulated agent to apply, in real time, a strategy allowing him to possibly re-route his path depending on the perceived local traffic conditions, jam and/or time already spent in his journey.The re-routing process allows the agents to directly react to any change in the road network. For the sake of simplicity, the agents’ strategy is modelled with a simple neural network whose parameters are determined during a preliminary training stage. The inputs of such neural network read the local information about the route network and the output gives the action to undertake: stay on the same path or modify it. As the agents use only local information, the overall network topology does not really matter, thus the strategy is able to cope with large and not previously explored networks.Numerical experiments are performed on various scenarios containing different proportions of trained strategic agents, agents with random strategies and non strategic agents, to test the robustness and adaptability to new environments and varying network conditions. The methodology is also compared against existing approaches and real world data. The outcome of the experiments suggest that this work-in-progress already produces encouraging results in terms of accuracy and computational efficiency. This indicates that the proposed approach has the potential to provide better tools to investigate and forecast drivers’ choice behaviours. Eventually these tools can improve the delivery and efficiency of traffic information to the drivers.

An adaptive agent-based approach to traffic simulation

Abstract The aim of this work is to present the initial exploration of a behavioural Dynamic Traffic Assignment model, particularly suitable to be used and implemented in agent-based micro-simulations. The proposal relies on the assumption that travellers take routing policies rather than paths, leading us to introduce the possibility for each simulated agent to apply, in real time, a strategy allowing him to possibly re-route his path depending on the perceived local traffic conditions, jam and/or time spent. The re-routing process allows the agents to directly react to any change in the road network. For the sake of simplicity, the agents’ strategy is modelled with a simple neural network whose parameters are determined during a preliminary training stage. The inputs of such neural network read the local information about the route network and the output gives the action to undertake: stay on the same path or modify it. As the agents use only local information, the overall network topology does not really matter, thus the strategy is able to cope with large networks. Numerical experiments are performed on various scenarios containing different proportions of trained strategic agents, agents with random strategies and non-strategic agents, to test the robustness and adaptability to new environments and varying network conditions. The methodology is also compared against MATSim and real world data. The outcome of the experiments suggest that this work-in-progress already produces encouraging results.

Training image-correlation systems by optimizing their attribute representations

A new method has been developed for representing the key points of images by vectors in attribute space. The method is based on an algorithm for the adaptive construction of new attributes on the basis of descriptions obtained ahead of time by using the “speeded-up robust features” method for a series of images recorded indoors from different aspects. Experimental studies of the model thus developed on various series of images shows that this method can be used in practice to solve the image-correlation problem, provided that a preliminary training stage of the given model is carried out.

Training Methods for Image Noise Level Estimation on Wavelet Components

The estimation of the standard deviation of noise contaminating an image is a fundamental step in wavelet-based noise reduction techniques. The method widely used is based on the mean absolute deviation (MAD). This model-based method assumes specific characteristics of the noise-contaminated image component. Three novel and alternative methods for estimating the noise standard deviation are proposed in this work and compared with the MAD method. Two of these methods rely on a preliminary training stage in order to extract parameters which are then used in the application stage. The sets used for training and testing, 13 and 5 images, respectively, are fully disjoint. The third method assumes specific statistical distributions for image and noise components. Results showed the prevalence of the training-based methods for the images and the range of noise levels considered.

Fuzzy neural network models for liquefaction prediction

Integrated fuzzy neural network models are developed for the assessment of liquefaction potential of a site. The models are trained with large databases of liquefaction case histories. A two-stage training algorithm is used to develop a fuzzy neural network model. In the preliminary training stage, the training case histories are used to determine initial network parameters. In the final training stage, the training case histories are processed one by one to develop membership functions for the network parameters. During the testing phase, input variables are described in linguistic terms such as ‘high’ and ‘low’. The prediction is made in terms of a liquefaction index representing the degree of liquefaction described in fuzzy terms such as ‘highly likely’, ‘likely’, or ‘unlikely’. The results from the model are compared with actual field observations and misclassified cases are identified. The models are found to have good predictive ability and are expected to be very useful for a preliminary evaluation of liquefaction potential of a site for which the input parameters are not well defined.

Gaze control in cats achieved by rewarding tongue push movements during the flickering of a light-emitting diode

A method is described for achieving gaze control in cats, by rewarding them for pushing against a lever with their tongue, only when a light-emitting diode (LED) was flickering. Since the state of the LED was changed sequentially from OFF to ON to flickering and the duration of flickering was short, the cats were required to gaze continuously at the LED during its ON period, in order to detect the flickering. After the preliminary training stage, 2–3 weeks were required before the cats performed the task at a higher level than could be predicted by chance. The electro-oculogram indicated that the eyes fixated during the ON period of the LED.