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A mathematical model for the determination of X-ray scattering kernels’ shapes based on incomplete simulation or measurement data was introduced and tested using a mathematical phantom. The model is originally intended for low-dose X-ray imaging without anti-scatter grids. The proposed model fits different kinds of symmetrical and asymmetrical scattering kernels in different tissues well enough for practical applications. Kernels asymmetry is mostly caused by irradiation of the object near the boundaries of different tissues. The model describes a variety of asymmetrical kernels by proposed “sectoral” members. Application of the proposed model in scattering compensation procedure reduces resulting error up to 50 % for “wide” scattering kernels.

The 21st century aviation and aerospace technologies have evolved and become more complex and technical. Turbofan jet engines as well as their cousins, the rocket engines (liquid/solid) have gone through several design upgrades and enhancements during the course of their design and exploitation. These technological upgrades have made engines very complex and expensive machines which need constant monitoring during their working phase. As the demand and use of such engines is growing steadily, both in the civilian and military sectors, it becomes necessary to monitor and predict the behavior of parametric data generated by these complex engines during their working phases. In this paper flight parameters such as Exhaust Gas Temperature (EGT), Engine Fan Speeds (N1 and N2), Fuel Flow (FF), Oil Temperature (OT), Oil Pressure (OP), Vibration and others where used to determine engine fault. All turbo fan engines go through several distinctly different working phases: Take-off phase, Cruise phase and Landing phase. Recording generated parametric data during these different phases leads to a massive amount of in-flight data and maintenance reports, which makes the task of designing and developing a fault diagnostic system highly challenging. It becomes imperative to use modern techniques in data analysis that can handle large volumes of generated data and provide clear visual results for determining the technical status of the engine under investigation/monitoring. These modern techniques should be able to give clear and objective assessment of the object under investigation. Cluster analysis methods based on Neural Networks such as c-means, k-means, self-organizing maps and DBSCAN algorithm have been used to build clusters. Differences in cluster groupings/patterns between healthy engine and engine with degraded performance are compared and used as the bases for defining faults. Fault diagnosis plays a crucial role in aircraft engine management. Timely and accurate detection of faults is the foundation on which maintenance turnaround times, operational costs and flight safety are based. The data used in this paper for analysis was obtained from flight data recorder during one flight cycle. The final decision on a fault is taken by an engineer.

In order to resist network malicious attacks, this paper briefly introduced the network intrusion detection model and K-means clustering analysis algorithm, improved them, and made a simulation analysis on two clustering analysis algorithms on MATLAB software. The results showed that the improved K-means algorithm could achieve central convergence faster in training, and the mean square deviation of clustering center was smaller than the traditional one in convergence. In the detection of normal and abnormal data, the improved K-means algorithm had higher accuracy and lower false alarm rate and missing report rate. In summary, the improved K-means algorithm can be applied to network intrusion detection.

The results of statistical estimation of the life quality of Central Federal Districtpopulation are shown. Methods of clustering and correlation analysis were used.Integral estimation of the life quality wascarried out.

Problems of control over subdivisions of a branch network are regarded; an algorithm of the implementation of a system of control over subdivisions is suggested, as well as implementation variations of modern methods of recording of interactions between the head office and the branch network, such as transfer pricing and costs allocation.An algorithm of system construction of effect monitoring of subdivisions of the branch network is described; in its terms a set of key performance indicators, supplemented by the use of clustering analysis in order to reduce the number of analyzed objects and reveal general trends of the development of subdivision groups, is offered. The described algorithms and methods may be implemented in organizations with a branch network in order to improve the efficiency of subdivisions management of the branch network and sustainability of the organization.