IoT network algorithm for production quality control

In report the algorithm for creating the IoT network for control product parameters is proposed. It includes steps: sensors measure parameters which sending in IoT platform, its processing and store in DB, applications informed farm managers about results

Image analysis algorithm in video analytics

Рассмотрен алгоритм анализа изображений в видеопотоке. Показано, что недостатком быстрого механизма обнаружения движущихся объектов на видеоизображениях, является необходимость предварительной обработки кадров с целью устранения шумов и межкадровых деформаций фона, возникающих, например, при перемещении камеры. An algorithm for analyzing images in a video stream was considered. It was shown that a drawback of the fast mechanism for detecting moving objects in video images was the need for preliminary processing of frames in order to eliminate noise and interframe deformations of the background that arise, for example, when moving the camera

Научная школа профессора А. А. Петровского

Two periods of scientific activity of Professor Alexander Alexandrovich Petrovsky, who was a member of the editorial board of the journal "Informatics" for 15 years (2004–2019), are presented. The main scientific results, his contribution to the development of the theory and to the hardware and software of the problem-oriented real-time systems and the processing of audio, speech and graphic information are shown, a list of the most significant works of the scientist is given.Представлены два периода научной деятельности профессора Александра Александровича Петровского, который на протяжении 15 лет (2004–2019) являлся членом редакционной коллегии журнала «Информатика». Показаны основные научные результаты, его вклад в области разработки теории и аппаратно-программных средств проблемно-ориентированных систем реального времени и обработки звуковой, речевой, графической информации, приведен перечень наиболее значимых трудов ученого

ИСПОЛЬЗОВАНИЕ НЕЙРОННЫХ СЕТЕЙ ДЛЯ ОБНАРУЖЕНИЯ И РАСПОЗНАВАНИЯ АНОМАЛИЙ В КОРПОРАТИВНОЙ ИНФОРМАЦИОННОЙ СИСТЕМЕ ПРЕДПРИЯТИЯ

The neural networks structure using for task solving of information defense are discussed. The choice of attribute and metadata of executing files with two states (clean and with viruses) which used for multilevel perseptron teaching are built. The teaching was realized within SPSS Statistics - program of IBM Company. After the teaching of neural network the efficiently its working with the control choice of executing files was determined. The relative value of files classification was 5 %, that it is the good result. The file choice must be greater and viruses more variables within the use such approach for large enterprise corporative information systems.Рассмотрены нейросетевые структуры, используемые для решения задачи защиты информации. Построена выборка атрибутов и метаданных исполняемых файлов, которая использовалась для обучения многослойного персептрона. Обучение проводилось в программе SPSS Statistics. После обучения нейронной сети эффективность ее работы была определена с помощью контрольной выборки исполняемых файлов. Относительная погрешность классификации файлов составила 5 %

IOT components for production quality monitoring

To automate the creation of IoT systems, design tools are used in the form of IoT platforms. The structure of the stack in the IoT network is considered. The connection of sensors with means of primary processing, including protocols and data structure, is described. A generalized algorithm for creating a network using the IoT plat-form Bluemix from IBM is presented. The forms of the developed user interface are described

Scientific school of professor A. A. Petrovsky

Two periods of scientific activity of Professor Alexander Alexandrovich Petrovsky, who was a member of the editorial board of the journal "Informatics" for 15 years (2004–2019), are presented. The main scientific results, his contribution to the development of the theory and to the hardware and software of the problem-oriented real-time systems and the processing of audio, speech and graphic information are shown, a list of the most significant works of the scientist is given

USE OF NEURAL NETWORKS FOR DETECTION AND RECOGNITION OF THE ANOMALIES IN ENTERPRISE CORPORATIVE INFORMATION SYSTEM

The neural networks structure using for task solving of information defense are discussed. The choice of attribute and metadata of executing files with two states (clean and with viruses) which used for multilevel perseptron teaching are built. The teaching was realized within SPSS Statistics - program of IBM Company. After the teaching of neural network the efficiently its working with the control choice of executing files was determined. The relative value of files classification was 5 %, that it is the good result. The file choice must be greater and viruses more variables within the use such approach for large enterprise corporative information systems

Organization of management and structure in local networks internet of things

Internet of Things (IoT) symbolic formula is given. The analysis of management technologies both in the network structures of infocommunications, based on the NSMP, and on local networks of the IoT. Two approaches for implementing the management process in infocommunication networks are shown: one is based on creating special software tools, the second is based on the working with data describing the network device. The basic operations of SNMP are given. Four levels of IoT in local network structure are described: smart sensors, network vehicles, services, and applications. Structure of local network of IoT which includes smart sensors, transport environment, services and applications information representation in network use semantic web are considered. The structure of multi-agent system (MAS) of milk farms analyzing in Libyan (MASMFA) for monitoring of production quality. MASMFA structure has many agents such as quality milk sensors, agents of communications, data base, analysis of the information received from sensor agents, decision-making. This system implements the functions to ensure the required class of milk quality and based on IoT local network construction. The information algorithm processing in such IoT is proposed. Milk sensor shell be periodically queried, their values will be recorded in the server database. The decision-making subsystem will issue data on milk quality to the farm administrator on a mobile device. The server structure will be implemented using a cloud service. Implementation this Internet of things network is being developed using LTE technology

Structure and database of the internet of things network for milk quality control

The data structures of the removed milk quality indicators are presented for relational representation in the database for their further display on the site pages. The solver generates messages if the milk quality indicators go beyond the control limits. The principles of forming database tables for the main controlled indicators of milk quality are considered

Модель и структура сети интернет вещей для мониторинга качества молока

The quality of milk is evaluated by a number of control points, which include a number of indicators, such as fat content, protein, lactose, density, etc. It is proposed to use the Internet of things (IoT) technology to control the quality of milk from distributed dairy farms. A multi-agent model of IoT network and the structure of such an IoT network for monitoring the quality of milk from different farms is presented. The model is represented by a variety of agents: milk analyzers, converter, storage of quality indicators, their processing, decision-making, monitoring of milk quality indicators. The structure of the IoT network includes milk analyzers, gateways-converters, a cloud platform, and mobile devices. The cloud platform rents a server that hosts knowledge and data bases, special software (solver) for processing and making decisions on milk quality, and a farm website. The database of the cloud structure server stores milk quality characteristics, and the knowledge base stores the rules for processing them. The solver outputs deviations from the current milk quality indicators from the standards. The site is used for communication of specialists in milk quality control. Monitoring of milk quality characteristics is implemented from mobile devices of specialists with access to the site components. The 4th generation LTE network using NB-IoT technology was chosen as the network for transmitting information from dairy farms to the cloud. The review of milk analyzers of both domestic and foreign companies is carried out. A gateway solution for querying milk analyzers and transmitting parameters to the cloud infrastructure is presented. Popular cloud platforms for building a network of IoT are presented