No abstract

No abstract

A methodology has been developed for assessing public transport passenger traffic in the city. A mathematical model based on fuzzy logic is presented. The main criteria for assessing the attractiveness of passenger traffic are: the interval between vehicles, technical condition of the vehicle, route length, time of day. In the mathematical model, all input linguistic variables and output variable, their terms and membership functions are described. A fragment of a fuzzy knowledge base presented in the form of production rules is presented. At the exit, the dispatcher receives an output variable – the degree of confidence in the attractiveness of the route. Based on this assessment, the dispatcher can make a number of necessary changes to improve the functioning of the route. The software is implemented as a web service. This software will be convenient for dispatchers to use for planning public transport routes. Fifteen selected routes were taken for research, which are the most popular in the city. These routes were proposed for evaluation by three controllers. The results obtained from dispatchers were compared with the results of the fuzzy inference implemented in the software. The main advantage of using this software product is the ability to build a dynamic schedule based on the analysis of the dispatcher. This, in turn, will allow passengers to receive a better transportation service within the city

No abstract

The paper proposes a solution to improve the efficiency of recognition of speech defects in children by processing the sound data of the spectrogram based on convolutional neural network models. For a successful existence in society, a person needs the most important skill - the ability to communicate with other people. The main part of the information a person transmits through speech. The normal development of children necessarily includes the mastery of coherent speech. Speech is not an innate skill for people, and children learn it on their own. Speech defects can cause the development of complexes in a child. Therefore, it is very important to eliminate them at an early age. So, the problem of determining speech defects in children today is a very urgent problem for parents, speech therapists and psychologists. Modern information technologies can help in solving this problem. The paper provides an analysis of the literature, which showed that models of CNN can be successfully used for this. But the results that are available today have not been applied to speech in Ukrainian. Therefore, it is important to develop and study models and methods of convolutional neural networks to identify violations in the speech of children. The paper describes a mathematical model of oral speech disorders in children, the structure of a convolutional neural network and the results of experiments. The results obtained in the work allow to establish one of the speech defects: dyslexia, stuttering, difsonia or dyslalia with recognition results of 77-79%.