Predicting and Determining the Time Between Metrological Failures of Smart Systems for Precision Farming

The paper identifies twelve problems of metrological reliability of wireless sensor systems that require their solution. The most urgent is the problem of determining the main parameters of the metrological reliability of wireless sensor systems. It is closely related to solving the problem of self-calibration of optical smart sensors that are part of the smart nodes of the system. The task of self-calibration of smart sensors is solved when three basic conditions are met: 1) when creating and using structurally redundant optical sensors designed to implement methods of redundant measurements; 2) when developing LEDs or other highly stable sources of optical radiation, including those with controlled parameters, for any given spectral range of wavelengths; 3) when creating in micro-performance filters on a priori given range of the spectrum of optical sines. The article describes two methods of metrological support for BSS smart sensors, based on the introduction of information redundancy by conducting three clock cycles of measuring physical quantity with a smart sensor, followed by processing the data using an a priori derived algorithm. The disadvantages and advantages of the methods are shown. It is stated that the use of the theory of excess measurements is the only correct way to solve the problem of self-calibration of sensors and measuring instruments. Given the harsh operating conditions of optical smart sensors, it is preferable to manufacture them using high technology. Today, in Ukraine, no one is engaged in a practical solution to the problems of metrological reliability of wireless sensor systems and the solution of the problems of self-calibration of smart sensors.

Evaluation of wind power potential in Baburband (Pakistan) using Weibull distribution function

Deviation entropy-based dynamic multi-model ensemble interval prediction method for quantifying uncertainty of building cooling load

The space-terrestrial integrated network is an important research field of the future network. The dynamic and heterogeneous nature of the space-terrestrial integrated network brings challenges to the research of congestion prediction methods. In this paper, we propose a chaotic dynamic congestion prediction method under the space-terrestrial integrated network, which solves the problems of insufficient dynamics and low accuracy of the existing congestion prediction methods for the space-terrestrial integrated network. The chaotic dynamic congestion prediction method is to predict the time series by using the GRU neural network model of the improved particle swarm algorithm to optimize the parameters after wavelet analysis. The experimental results show that the prediction accuracy of the chaotic dynamic congestion prediction method is higher, and it is more suitable for the space-terrestrial integrated network.

In order to improve the dynamic characteristics of the contactor, this paper proposes a new dynamic displacement prediction method for contactors with constant air gap. The dynamic displacement process can be predicted by observing the flux linkage produced by the permanent magnet. The equivalent magnetic circuit model of electromagnetic force driving actuator (EMFA) is established. The formulas of dynamic flux linkage, displacement and current are deduced, and the displacement prediction of the contactor is completed. The curved surfaces of driving force, flux linkage, displacement and current are established by finite element method. The movement process of the contactor is dynamically simulated by interpolation method. Finally, an EMFA-driven contactor is used for the experiment. Experimental results show that the new dynamic displacement prediction method is suitable for contactors with constant air gap. On the basis of sensorless control, bounce time of the contact can be effectively reduced by adjusting the PWM duty cycle and the corresponding displacement control parameters. The research in this article can be used to guide the design of future controllers. And the simulation results are consistent with the experimental results. The experiment verifies the effectiveness of the new dynamic displacement prediction method.

Uncertainty analysis and B-basis value of tensile strength of 2D SiC/SiC composite

Session V:

In this study, wind speed data from four stations, which are located in Elazig and its close regions, are analyzed. Data are fitted to the Weibull and Rayleigh probability distribution functions. Probability distribution functions are derived from cumulative function and used to calculate the mean wind speed and variance of the actual data. The best way of representing the performance of Weibull and Rayleigh distributions is to use the statistical parameters such as the correlation coefficient (R 2), chi-square (χ2) and root mean square error analysis (RMSE). The annual mean wind speed and power are 5.66 m/s and 246.27 W/m2 for Maden, 3.86 m/s and 98.90 W/m2 for Agin, 3.14 m/s and 41.37 W/m2 for Elazig, 2.27 m/s and 15.83 W/m2 for Keban. Maden has good wind energy potential when it is compared with the other three regions. In general, wind speed is higher during the summer months, notably June and July, and is lower during some winter and spring months. However, for the whole stations, Weibull distribution function was found to be better than Rayleigh distribution function.

This paper compares two commonly used functions, the Weibull and Rayleigh distribution functions, for fitting a measured wind speed probability distribution at a given location over a certain period. The monthly and annual measured wind speed data at 84 m height for the years have been statistically analyzed for the country with a large capacity - Kitka. The analysis is made in the case of the implementation of all the predicted capacity of wind turbines and by virtue of the probability of power distribution. The Weibull and Rayleigh probability distribution functions have been determined and their parameters have been identified. The average wind speed and the wind power density have been estimated using both distribution functions and compared those estimated from the measured probability distribution function. The Weibull distribution function fits the wind speed variation better than Rayleigh distribution function. The average wind speed was found to be 4.5 m/s and the average wind power density was 114.54 W/m According to results, we can conclude that such a distribution of winds in this region yields an appropriate average value of wind power.

This paper compares between two commonly used functions, the Weibull and Rayleigh distribution functions, for fitting a measured wind speed probability distribution at a given location over a certain period. Hourly measured wind speed data at 10 m height for the years 2003 to 2005 have been statistically analyzed for the Kingdom of Bahrain. The Weibull and Rayleigh probability distribution functions have been determined and their parameters have been identified. The average wind speed and the wind power density have been estimated using both distribution functions and compared those estimated from the measured probability distribution function. The Weibull distribution function fits the wind speed variation better than Rayleigh distribution function. The average wind speed was found to be 4.5 m/s and the average wind power density was 114.54 W/m2

Predictive energy management (PEM) strategy has shown great advantages in improving fuel economy for plug-in hybrid electric vehicles (PHEVs). A key technology in PEM is velocity prediction and its accuracy greatly affects the effectiveness of a PEM strategy. This paper proposes a novel dynamic competitive velocity prediction method based on Markov state space (SS) reconstruction. The basic Markov model is introduced and its performance is fully evaluated. The Markov SS is designed by the K-Means++ clustering method to support online reconstruction. The transition probability matrix (TPM) is updated to adapt to the actual driving scenario. The dynamic competitive prediction method combines the basic and the reconstructed Markov models to achieve better performance. The velocity prediction performance is validated through repetitive complex driving conditions. Simulation result shows that the proposed method has superior performance in both prediction accuracy and computing time. For the complex driving condition scenario, the proposed method can reduce prediction error by 5.7%–9.1% comparing to the basic Markov model and its computing time is about 1% of that of LSTM when the prediction horizon is 5 s.

An improved mistuning identification and dynamic strain prediction method for rotating blades with application of blade tip timing technology

This paper focuses the behaviour of wind speed pattern in some selected locations of Bangladesh in order to forecast the wind energy potential of Bangladesh. In our study, the wind speed data for Cox's Bazar, Kuakata, Swandip, Hatiya and Sydpur between 2004 and 2007 have collected from several organizations i.e. Bangladesh Meteorological Department (BMD), Local Government Engineering Development (LGED) and Infrastructure Development Company Ltd (IDCOL) and processed on daily, monthly and hourly basis. The data have been examined by Weibull distribution function using Weibull Graphical method in order to find the shape parameter (k) and scale parameter (c) and thus compared between the Weibull and Rayleigh distribution functions to deduce the wind availability of the selected locations. Moreover, statistically Correlation Coefficient (R2), Root Mean Square Error (RMSE), and Coefficient of Efficiency (COE) methods have also employed based on the data in order to evaluate the performance of the Weibull probability density function.

Identification of time spectrum is one of the core issues of the time-domain induced polarization (IP) method, which can be considered as one of the bases for distinguishing various polarized rocks. Fitting the IP data based on the spectrum forward model and obtaining optimal solution of the model parameters is a key step in spectrum identification. However, the suitable forward model should consider the observation conditions, such as the charging time and the time window. The time spectrum identification may be difficult to implement stably due to the lack of objective reference for the optimal solution. Therefore, our purpose is to improve the forward model and implement the spectrum identification for IP data. First, using the Weibull (WB) distribution function as the basis, a time spectrum forward model considering the charging time and observation time window is provided according to the typical measurement mode. Then, based on the WB spectrum model and Barzilai-Borwein gradient optimization, a method for solution of apparent spectral model parameters for spectrum identification is developed. Finally, this method is used to process the IP data from a mine in which the anomalies related to ore-bearing beds are identified based on the processing results. Results obtained demonstrate that the spectrum forward model based on the WB function is feasible in describing the IP data. The limited charging time and a wide observed time window should be considered to realize accurate simulations and description of the time-domain IP data. The essence of the time spectrum identification is to comprehensively reflect the time-varying state of the whole time channel through the spectral model parameters, whereas the decay field ratio of the adjacent time channel can be used as an objective reference. The parameters of the spectrum model characterizing the time-varying state are independent of polarization and resistivity and thus can be directly used for identification of IP anomalies.

Today, the use of wind energy by wind turbines has grown significantly, and this development is due to the production of required energy and tourism attraction of wind turbines. But according to the standard technologies used in this industry, the operating costs are very high. For this reason, before the construction of wind farms, potential measurements should be done along with economic analysis. Therefore, in this research, a statistical analysis of wind farms has been done. In the statistical analysis, Weibull and Rayleigh distribution functions were used to predict the wind speed of the studied area. MATLAB software is used to model prediction functions. Among the important results of wind speed prediction by the Weibull distribution function, it can be mentioned that the wind speed is variable between 0.6 and 7 m/s in the studied area. The total power density and wind energy in the 10 years are equivalent to 28 W/m2 and 810/0534 kWh/m2 at the height of 10 m calculated.