Processing video streams in real-time is one of the most difficult areas of machine intellectualization. Intelligent systems of processing and analyzing video streams are more and more intensively used in various fields of human activity. They are most widely used when dealing with such tasks as securing various facilities, development of traffic management systems, etc. One of the areas of developing systems for processing and analyzing video streams is the development of systems for license plate recognition.Today, the market offers quite a number of plate recognition systems, but there is no system produced in Kazakhstan. After analyzing the known plate recognition systems the following was revealed: firstly, many systems give unsatisfactory results for images of poor quality; secondly, they do not work with complex (diverse) images, since it is difficult to locate the required recognition area in them; and thirdly, they are all aimed at well-defined conditions (lighting, camera angle, brightness, etc.). Their high cost does not allow for mass introduction.The peculiarity of developed system is that depending on the image quality and technical characteristics of the equipment the data processing is performed by automatically selecting a particular model and algorithms which in turn provides the system's rate of
This paper presents technique for recognizing license plates structured characters of the Republic of Kazakhstan. This technique includes methods for converting the geometric-topological characteristics of license plates and the method for classifying alphanumeric characters by using cluster analysis. Developed modified algorithm for character recognition based on methods of contour analysis and template method with the addition of proposed transformations. A. Tlebaldinova et al.
Assessment of seismic vulnerability of urban infrastructure is an actual problem, since the damage caused by earthquakes is quite significant. Despite the complexity of such tasks, today's machine learning methods allow the use of "fast" methods for assessing seismic vulnerability. The article proposes a methodology for assessing the characteristics of typical urban objects that affect their seismic resistance; using classification and clustering methods. For the analysis, we use kmeans and hkmeans clustering methods, where the Euclidean distance is used as a measure of proximity. The optimal number of clusters is determined using the Elbow method. A decision-making model on the seismic resistance of an urban object is presented, also the most important variables that have the greatest impact on the seismic resistance of an urban object are identified. The study shows that the results of clustering coincide with expert estimates, and the characteristic of typical urban objects can be determined as a result of data modeling using clustering algorithms.
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