Devising a method for improving the efficiency of artillery shooting based on the Markov model

This paper reports a method for improving the firing efficiency of an artillery unit that results in enhanced effectiveness. Given the modern use of artillery for counter-battery warfare, the effectiveness of shooting is not enough assessed by accuracy only. It is also necessary to take into consideration and minimize the time spent by the unit in the firing position and the consumption of shells to hit the target. It has been shown that in order to assess the effectiveness of an artillery shot due to the initial velocity of the projectile, the most rapid and simple means is to classify the quality of the shot by the acoustic field. A procedure for categorizing the shot has been improved by applying an automatic classifier with training based on a machine of support vectors with the least squares. It is established that the error in the classification of the effectiveness of the second shot does not exceed 0.05. The concept of the effectiveness of a single artillery shot was introduced. Under the conditions of intense shooting, there may be accidental disturbances in each shot due to the wear of the charging chamber of the gun, its barrel, and incomplete information about the powder charge. When firing involves disturbances, the firing of an artillery unit can be described by a model of a discrete Markov chain. Based on the Markov model, a method for improving the efficiency of artillery fire has been devised. The method is based on the identification of guns that produce ineffective shots. The fire control phase of the unit has been introduced. In the process of controlling the fire of the unit, such guns are excluded from further firing. A generalized criterion for the effectiveness of artillery firing of a unit, based on the convolution of criteria, has been introduced. It is shown that the devised method significantly improves the effectiveness of shooting according to the generalized criterion.

Analysis and Design of a Wireless Sensor Network Based on the Residual Energy of the Nodes and the Harvested Energy from Mint Plants

Nowadays, the use of sensor nodes for the IoT is widespread; nodes that compose these networks must possess self-organizing capabilities and communication protocols that require less energy consumption during communication procedures. In this work, we propose the design and analysis of an energy harvesting system using bioelectricity harvested from mint plants that aids in powering a particular design of a wireless sensor operating in a continuous monitoring mode. The system is based on randomly turning nodes ON (active nodes) and OFF (inactive nodes) to avoid their energy depletion. While a node is in an inactive state, it is allowed to harvest energy from the surroundings. However, while the node is harvesting energy from its surroundings, it is unable to report data. As such, a clear compromise is established between the amount of information reported and the lifetime of the network. To finely tune the system’s parameters and offer an adequate operation, we derive a mathematical model based on a discrete Markov chain that describes the main dynamics of the system. We observe that with the use of mint plants, the harvested energy is of the order of a few Joules; nonetheless, such small energy values can sustain a wireless transmission if correctly adapted to drive a wireless sensor. If we consider the lowest mean harvested energy obtained from mint plants, such energy can be used to transmit up to 259,564 bits or can also be used to receive up to 301,036 bits. On the other hand, if we consider the greatest mean harvested energy, this energy can be used to transmit up to 2,394,737 bits or can also be used to receive up to 2,777,349 bits.

Quality control of tea products by variable sampling

Based on the technological process of obtaining tea products, the factors determining its quality are considered. The analysis of these factors and their classification. The optimization of labor costs and time to perform quality control of raw materials using the methods of probability theory. Based on a simple discrete Markov chain, proposed a system of selective quality control of tea products by variable samples.

Covid-19 Prediction in USA using modified SIR derived model

The Covid-19 pandemic is rapidly extended into the extraordinary crisis. Based on the SIR model and published datasets the Covid-19 spread is assessed and predicted in USA in terms of susceptible, recovered and infected in the communities is focused on this study. For modelling the USA pandemic prediction several variants have been utilized. The SIR model splits the whole population into three components such as Susceptible (S), Infected (I) and Recovered or Removed (R). A collection of differential equations have been utilized to propagate the model and resolve the disease dynamics. In the proposed study, the prediction of covid-19 based on time is performed using the modified SIR derived model SIR-D with discrete markov chain. This proposed technique analyse and forecasting the covid-19 spread in 19 states of USA. The performance analysis of the proposed Analytical results revealed that though the probable uncertainty of the proposed model provides prediction, it becomes difficult to determine the death cases in future.

Return Time Probability Distribution of a Finite Markov Chain

In this paper we have considered a finite discrete Markov chain and derived a recurrence relation for the calculating the return time probability distribution. The mean recurrence time is also calculated. Return time distribution helps to identify the most frequently visited states. Return time distribution plays a vital role in the classification of Markov chain. These concepts are illustrated through an example.

Complex Probability and Markov Stochastic Process

This note discusses the existence of "complex probability" in the real world sensible problems. By defining a measure more general than the conventional definition of probability, the transition probability matrix of discrete Markov chain is broken to the periods shorter than a complete step of the transition. In this regard, the complex probability is implied.

Cost-Optimal Maintenance Planning for Defects on Wind Turbine Blades

Due to the considerable increase in clean energy demand, there is a significant trend of increased wind turbine sizes, resulting in much higher loads on the blades. The high loads can cause significant out-of-plane deformations of the blades, especially in the area nearby the maximum chord. This paper briefly presents a discrete Markov chain model as a simplified probabilistic model for damages in wind turbine blades, based on a six-level damage categorization scheme applied by the wind industry, with the aim of providing decision makers with cost-optimal inspection intervals and maintenance strategies for the aforementioned challenges facing wind turbine blades. The in-history inspection information extracted from a database with inspection information was used to calibrate transition probabilities in the discrete Markov chain model. With the calibrated transition probabilities, the damage evolution can be statistically simulated. The classical Bayesian pre-posterior decision theory, as well as condition-based maintenance strategy, was used as a basis for the decision-making. An illustrative example with transverse cracks is presented using a reference wind turbine.