On operational search support of special operations

The problem of operational-search support of special operations with the help of a complex of operational-search measures and other auxiliary tactical methods of actions that contribute to the effective achievement of goals and the solution of special operations tasks is considered. Based on the definitions of the concepts «special operation», «operational-search operation» and the opinions of researchers, the author proposed a definition of an operational-search support of special operations, tasks and stages of its implementation are formulated. It is noted that the entire range of tasks can be solved only with the widespread use of the undercover method, the use of an operational game, which implies the use of an organizational and tactical form - an operational search operation. If it is necessary to urgently conduct a special operation in conditions of extremely limited operational illumination, only certain measures of operational-search support are possible, which do not include an operational-search operation. Analyzing the experience of the practical activities of the internal affairs bodies of Russia, the author concludes about the consideration of special and operational-search operations in their dialectical interaction, when one type of operation by tactical methods of action can solve problems inherent in it, and at the same time it can ensure the actions of another type of operation. When analyzing the operational-search support, the author determines its most effective organizational and tactical form – the operational-search operation.

Discovering access holes in disaster rubble with functional and photometric attributes

The collapse of buildings often result in human victims becoming trapped within rubble. This environment is dangerous for emergency first responders tasked with locating and extricating victims. Recent work in scene mapping using photometric colour and metric depth (RGB-D) data suggest the possibility of automatically identifying potential access holes into rubble interior. This capability would improve search operation by directing limited resources to be concentrated on areas where access holes might exist. This thesis presents an approach to automatically identify access holes in rubble. The investigation begins by defining access holes in terms of their functional utility, that allow for their algorithmic identification. From this definition, a set of hole-related features extracted from RGB-D imagery are proposed for detection. Experiments were conducted using data collected over a real-world disaster training facility. Empirical evaluation indicates the efficacy of the proposed system for successfully identifying potential access holes in disaster rubble scenes.

Discovering access holes in disaster rubble with functional and photometric attributes

The collapse of buildings often result in human victims becoming trapped within rubble. This environment is dangerous for emergency first responders tasked with locating and extricating victims. Recent work in scene mapping using photometric colour and metric depth (RGB-D) data suggest the possibility of automatically identifying potential access holes into rubble interior. This capability would improve search operation by directing limited resources to be concentrated on areas where access holes might exist. This thesis presents an approach to automatically identify access holes in rubble. The investigation begins by defining access holes in terms of their functional utility, that allow for their algorithmic identification. From this definition, a set of hole-related features extracted from RGB-D imagery are proposed for detection. Experiments were conducted using data collected over a real-world disaster training facility. Empirical evaluation indicates the efficacy of the proposed system for successfully identifying potential access holes in disaster rubble scenes.

Feasibility of Longest Prefix Matching using Learned Index Structures

This paper revisits longest prefix matching in IP packet forwarding because an emerging data structure, learned index, is recently presented. A learned index uses machine learning to associate key-value pairs in a key-value store. The fundamental idea to apply a learned index to an FIB is to simplify the complex longest prefix matching operation to a nearest address search operation. The size of the proposed FIB is less than half of an existing trie-based FIB while it achieves the computation speed nearly equal to the trie-based FIB. Moreover, the computation speed of the proposal is independent of the length of IP prefixes, unlike trie-based FIBs.

Difference-Based Mutation Operation for Neuroevolution of Augmented Topologies

In this paper, a novel search operation is proposed for the neuroevolution of augmented topologies, namely the difference-based mutation. This operator uses the differences between individuals in the population to perform more efficient search for optimal weights and structure of the model. The difference is determined according to the innovation numbers assigned to each node and connection, allowing tracking the changes. The implemented neuroevolution algorithm allows backward connections and loops in the topology, and uses a set of mutation operators, including connections merging and deletion. The algorithm is tested on a set of classification problems and the rotary inverted pendulum control problem. The comparison is performed between the basic approach and modified versions. The sensitivity to parameter values is examined. The experimental results prove that the newly developed operator delivers significant improvements to the classification quality in several cases, and allow finding better control algorithms.

The “self-learning query” algorithm for searching scientific publications across specialized search engines

Typically, a search operation on Big Data is complicated task due to different formats and different nature of the data. To simplify it the user may implement special algorithms to process different data for uncertain criteria. The “self-learning query” algorithm presented in this work allows to search in Big Data either for certain or uncertain search criteria’s with minimum attention from the data programmer. It uses accumulated search statistics as the basis to make the result set more precisely according to the search criteria, so as long the user work with the system as more precise will be the results. The algorithm presented in this work allows data scientists to search for uncertain data and potentially discover results faster by offloading the burdens of data management and provenance to the expert system.

Legal regulation of the activities of persons cooperating with the subjects of search activities

Although the Law of the Republic of Azerbaijan on “Operational-Search Activity” of October 28, 1999 does not provide a list of entities inspecting and supervising operational-search activities, various articles of the law reflect this opinion. The state bodies and their officials, as well as the courts entrusted with the function of supervising the implementation of the laws of the Republic of Azerbaijan by the legislative bodies of the Republic of Azerbaijan, the General Prosecutor's Office of the Republic of Azerbaijan. prosecutors authorized by him. Key words: subject, legal regulation, activity, search operation, regulation

Software Package for Searching People and Vehicles Using Unmanned Aerial Vehicles

The paper considers the relevance of using a single software package for processing images made by unmanned aerial vehicles and coordinating the actions of search participants during operations to search for people and vehicles in the natural environment. An approach to the creation of such a package is proposed, its architecture is developed, the technologies used in the development and the main technical solutions are described. An algorithm for processing data for a search operation is developed and implemented. The package core, which recognizes search objects on aerial photographs, is built on a convolutional artificial neural network YOLOv5. The characteristics of the network operation depending on the structure of the training sample and the network parameters are studied. The experimental operation of the package by one division of the crisis management center of the Ministry of Emergency Situations of Russia in Bryansk region is described.

Automatic Control of Ship Motion Conducting Search in Open Waters

In this work a search operation support system is presented, which is an additional autopilot function block. Its task is to lead a vessel included in the search by the SAR services to the datum position on the basis of the data entered by the operator into the system and then automatically to search the indicated area according to the newly defined search pattern. The set goal can be achieved thanks to the autopilot that guides the ship along a given trajectory consisting of straight lines and arcs. High control accuracy is provided by the IMC control system using a relatively simple non-linear ship model. The simulation tests of the tanker model confirmed that the indicated search area can be precisely checked in a shorter time than when using the expanding square search pattern.