Computer simulation of adapting the object image selection method to video surveillance conditions

The article deals with the issues of computer modeling of methods for selecting images of objects against a non-uniform background. A test video sequence with given background and object parameters is considered, which provides imitation of one of the special cases of video surveillance conditions, namely, the convergence of the video surveillance point and the object. The issues of adaptation of the compensation selection method to the specified conditions of video surveillance are discussed. Examples of test images and graphs of dependences of the probability of correct determination of coordinates depending on the value of the local contrast of the object in relation to the background, obtained by computer simulation are given.

Monitoring and Controlling Condition of Complex Multiparametric Object

Technology is advancing ever faster in all areas of human activity, making the objects around us ever more complicated. Notably, complex objects have multiple parameters that define their condition, whether normal, near-critical, critical, over-critical, etc. If critical infrastructures are in critical condition, their collapse jeopardizes not only the infrastructures, but also their environment. This is why timely maintenance and control of condition is imperative for complex objects. This paper dwells upon a method for monitoring and controlling the condition of a complex multiparametric object; the method uses a minimum number of monitored parameters to retain full data on the condition of a complex object, to optimize the periodicity of taking readings depending on the functional phase of the object, and to determine how object parameters relate to each other and how they affect the quality of its functioning; such minimization reduces computational complexity and the bandwidth requirement. Russian Invention Patents granted to the authors certify the novelty and practical significance of the proposed solution.

SMART ALGORITHM FOR INTERVAL ESTIMATION OF ARC-QUENCHING REACTOR PARAMETERS

The purpose of the arc suppression reactor is to reduce the capacitive current of the network to a safe level where the single-phase earth fault current at the fault location does not exceed five amperes. The current reduced to a permissible level prevents open arcing at the fault location. For proper operation of this device, the arc suppression reactor control automatics needs to adjust the zero-sequence circuit to resonance, which balances the capacitive current of the mains and the inductive current of the reactor. To perform this tuning, it is not necessary to have information about the absolute values of the parameters of the zero-sequence circuit, but by determining them, the automation device is able to solve a wider range of tasks related to network diagnostics and increasing the efficiency of the arc suppression reactor. In this article we consider an approach to solving the problem of parametric identification of arc suppression reactor using the method of interval estimation of object parameters. The information about the operation modes of the arc suppression reactor is obtained by means of a simulation model of the object. Using the observed values, the object parameters are obtained by use of the inverse function to the simulation model. The dependence of the object parameters on the observed parameters is approximated using upper and lower parameter estimation models. The quantile regression method was applied to tune the estimation models. The need to increase the generalization ability of the algorithm is revealed. The method of adjustment of parameters of regularization of learning process to increase generalization ability of algorithm without increase of informativity of data in a training sample is offered. The results of algorithm performance are presented on the example of magnetization branch parameters estimation of arc suppression reactor. The boundaries of the interval of equivalent magnetic core loss resistance and magnetizing inductance are obtained. The limitations of the methods are analyzed, and recommendations for improving the quality of the algorithms are given.

RESEARCH OF THE METHOD OF INCREASING THE OBJECT DETERMINATION ACCURACY ON THE LOW-RESOLUTION VIDEO STREAM

Study subject. The article proposes and investigates a method for increasing the accuracy of determination of the distance and the obstacle geometric parameters based on object contours determination using a computer vision system that uses low-resolution sensors. The goal is the effectiveness evaluation of the proposed method. Tasks: to conduct experimental researches of the quality indicators of the method of increasing the object contours determination accuracy; evaluate the effectiveness of this method. Used methods: statistical modeling, laboratory scale tests. The obtained results: the analysis of the proposed method efficiency was carried out and the influence of this method on the determination accuracy of the distance and object geometric parameters was evaluated. Conclusions: the considered method made it possible to achieve the increasing the determination accuracy of the distance and geometric object parameters by compensating for image blur using the Lucy-Richardson deconvolution algorithm. The obtained data showed a decrease in the maximum error in determining the distance from 8% to 4% and the error in the geometric object parameters from 7.7% to 5.8%. The implementation of this approach was carried out in the Python programming language.

Wireless Monitoring of Biological Objects at Microwaves

Electromagnetic (EM) wave propagation inside and along the surface of the human body is the subject of active research in the field of biomedical applications of microwaves. This research area is the basis for wireless monitoring of biological object parameters and characteristics. Solutions to the following problems are crucial for achieving the stated goals in the area of wireless monitoring: EM wave propagation inside and on-body surface. The biological object monitoring is based on a consideration of the following problems: (i) dielectric properties of a biological issue; (ii) EM wave propagation in biological medium; (iii) propagation of EM waves across the boundary of two media (biological medium–air): wave reflection and refraction; (iv) EM wave propagation in a multilayer biological medium; (v) EM wave propagation along the plane or curved surface of biological objects.

Manipulation for self-Identification, and self-Identification for better manipulation

The process of modeling a series of hand-object parameters is crucial for precise and controllable robotic in-hand manipulation because it enables the mapping from the hand’s actuation input to the object’s motion to be obtained. Without assuming that most of these model parameters are known a priori or can be easily estimated by sensors, we focus on equipping robots with the ability to actively self-identify necessary model parameters using minimal sensing. Here, we derive algorithms, on the basis of the concept of virtual linkage-based representations (VLRs), to self-identify the underlying mechanics of hand-object systems via exploratory manipulation actions and probabilistic reasoning and, in turn, show that the self-identified VLR can enable the control of precise in-hand manipulation. To validate our framework, we instantiated the proposed system on a Yale Model O hand without joint encoders or tactile sensors. The passive adaptability of the underactuated hand greatly facilitates the self-identification process, because they naturally secure stable hand-object interactions during random exploration. Relying solely on an in-hand camera, our system can effectively self-identify the VLRs, even when some fingers are replaced with novel designs. In addition, we show in-hand manipulation applications of handwriting, marble maze playing, and cup stacking to demonstrate the effectiveness of the VLR in precise in-hand manipulation control.

RESEARCH OF THE EFFICIENCY DYNAMIC OBJECTS DETECTING ON THE VIDEO SEQUENCE FROM VIDEO SURVEILLANCE CAMERAS

In this article, indicator of the effectiveness object detection in video surveillance systems (VSS) was proposed. The results of experimental calculations, have been indicated to increase the efficiency of using VSS with video analytics functions. After modeling the obtained expressions in the software package Mathcad and using the main indicators of the conditional software and hardware, it is obvious that the probability of detection of the object (person) by the video surveillance system operator depends on a number of parameters (geometric dimensions of the observed object). , parameters of the video surveillance camera, parameters of the information display device (monitor), features of the visual system of the human operator, the level of his fatigue and level of training, etc.). Areas of further research determine the software implementation of the proposed criterion, analysis of modern models for determining the probabilities of detecting alarming events by video surveillance system operators.Keywords: probability of detection, human operator, criterion, criterion for evaluating efficiency, indicator, indicator of task success.