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• THE SOFTWARE APPROACHES FOR SOLVING HYDROACOUSTIC COMMUNICATION PROBLEMS IN MARINE INTERNET OF THINGS SYSTEMS

  К. G. Кеbkal, А. А. Kabanov, V.V. Alchakov, V.А. Kramar, М. E. Dimin

  2024-04-16

  Abstract ▼

  When several hydroacoustic modems operate simultaneously in an area of mutual coverage, collisions
  of data packets received from several sources may occur, which leads to the loss of some or all
  information. With the increase in the number of simultaneously operating hydroacoustic modems, physical
  layer algorithms do not provide stable data transmission and the likelihood of collisions increases,
  which makes the operation of modems ineffective or even impossible. To ensure effective operation in a
  hydroacoustic signal propagation environment and to reduce or eliminate collisions during the exchange
  and delivery of data between two modems that do not have the ability to operate synchronously,
  as well as to reduce the access time to the signal propagation environment, methods of the medium access
  control layer are required using link layer protocols. Typically, this problem is solved using code
  separation of hydroacoustic channels. Modems communicate as if at different frequencies, which does
  not create collisions, this allows subscribers of the underwater network to communicate in a point-topoint
  format, or in multicast mode, that is, everyone separately, however, in case it is necessary to make
  a transmission over the network , this option is no longer suitable, since network transmission involves
  working on the basis of “broadcast” messages. In practical use, it is convenient to place these protocols
  into a software development environment (framework) for specific user applications for solving network
  communication problems. Such a framework is usually called a software framework; it allows for user
  modification of the network algorithms available in the framework, as well as the inclusion of new network
  hydroacoustic communication algorithms by the user. To build a predictive model, the DACAP, TLohi,
  Flooding and ICRP protocols were used in the work. The algorithms were implemented in Erlang.
  The paper presents algorithms for implementing these protocols. A comparative analysis of network
  operation with and without protocols is provided. Efficiency and speed of work were assessed. Recommendations
  for further development of the software framework are given

• THE MACHINE LEARNING TECHNIQUE FOR FORECASTING THE SEASONAL TIME SERIES

  V.V. Alchakov, V.А. Kramar

  2023-06-07

  Abstract ▼

  Time series with seasonal variability is widely used to describe processes in various
  fields, such as trade, analysis of financial markets, forecasting of passenger air transportation,
  and description of climatic changes. Recently, this approach has been widely used to describe
  technological processes as well. In this regard, applying predictive models in control systems of
  complex technical objects has become possible. Machine learning methods can be effectively
  used to build predictive models of series of this type. In this case, only historical data accumulated
  over several periods of seasonal observation is used as input data for constructing the
  forecast. Knowledge of other parameters, as a rule, is not required. The article considers creating
  a predictive time series model with seasonal variability, describing a technological process,
  the inlet flow of a wastewater treatment plant being chosen as a model. The general methodology
  of model building, requirements for the input data sets, and algorithms of preprocessing to
  form samples used for model training and testing are described. Classical methods (SARIMA,
  Holt-Winters Exponential Smoothing, ETS), as well as new algorithms (Facebook Prophet,
  XGBoost, Long Short Term Memory), were used to build the predictive model. The implementation
  of the algorithms is done in the Python language, and recommendations for the use of existing
  libraries and functions of this language are given in the work. The comparative analysis of
  the accuracy of the obtained models is given on the calculation of a set of statistical metri cs.
  Analysis of methods performance is also carried out since the time it takes to create a model
  and get a forecast plays an important role when running the model in real production conditions.
  The best method for solving the set task for application in real-time control systems was
  chosen based on the sum of estimates. In conclusion, recommendations for improving forecast
  accuracy were given, and future research directions were outlined.