Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability of the UAV to plan and track the path with minimum energy and time consumption is possible by using the flight parameters. This study performs the optimum path planning and tracking using Harris hawk optimization (HHO)–grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold–release mission avoiding obstacles. In the study, the hybrid HHO–GWO algorithm, which stands out with its avoidance of local minima and speed convergence, is used to successfully obtain the feasible and effective path. In addition, the effect of the mass change uncertainty of the UAV on optimal path planning and tracking performance is determined. The effectiveness of the proposed approach is tested by comparing it with the metaheuristic swarm optimization algorithms such as particle swarm optimization (PSO) and GWO. The experimental results obtained indicate that the proposed algorithm generates a fast and safe optimal path without becoming stuck with local minima, and the quadcopter tracks the generated path with minimum energy and time consumption.

In this study, Bitcoin prediction is performed with Linear Regression (LR) and Support Vector Machine (SVM) from machine learning methods by using time series consisting of daily Bitcoin closing prices between 2012-2018. The prediction model with include the least error is obtained by testing with different parameter combinations such as SVM with including linear and polynomial kernel functions. Filters with different weight coefficients are used for different window lengths. For different window lengths, Bitcoin price prediction is made using filters with different weight coefficients. 10-fold cross-validation method in training phase is used in order to construct a model with high performance independent of the data set. The performance of the obtained model is measured by means of statistical indicators such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Pearson Correlation. It is seen that the price prediction performance of the proposed SVM model for Bitcoin data set is higher than that of the LR model.

The control of equipment such as camera gimbal, Vertical Take-Off and Landing (VTOL) and Load Transporting System (LTS) on Unmanned Aerial Vehicle (UAV) with its own flight control directly affects the performance of the mission in tasks such as tracking the target along the specified path and leaving payloads on the targets specified in the dangerous areas. In this study, neural network based real-time control of a hexarotor UAV is performed so that the payloads on the targets determined by path tracking can be left with minimum error. The Nonlinear AutoRegressive eXogenous (NARX) model of the UAV is obtained after the flight data are passed through the pre-processing, feature extraction and feature selection stages. The obtained neural network model is embedded in the flight control card to realize real time path tracking of the UAV. The three payloads in the cubic structure are both transported by the originally designed LTS and left with the help of LTS to targets on the path. Environmental testing is conducted taking into account the limitations of the physical properties of the LTS and specified path tracking on the autonomously moving UAV, and the impact on proposed NARX control algorithm's mission performance is examined.

In this study, the wind speed prediction model is created that gives a minimum error for different hidden layer neuron numbers and delay step numbers. Using the one-minute time series, the prediction of the next wind speed is performed with the NAR neural network model. The predicted values of wind speed obtained are compared with predicted values of wind speed obtained with filter methods. For different window functions and lengths, wind speed prediction is made using filters with different weight coefficients. For the number of hidden layer neurons is 14 and the number of delay steps is 10, MAE, MSE and RMSE values are calculated as 0.0315, 0.0019, 0.0445, respectively, with NAR neural network. It is seen that the proposed method for the wind speed dataset has a higher prediction performance than thefilter methods.

Renal autoregulation (AR) mechanisms provide the primary protection against transmission of systemic pressures and hypertensive renal damage. However, the relative merits of the “step” change vs. “dynamic” methods for the assessment of AR capacity remain controversial. The effects of 48–72 h of orally administered amlodipine (L-type) and mibefradil (T-type) calcium channel blockers (CCBs) on step and dynamic AR in Sprague-Dawley rats were compared. Both CCBs significantly impaired “steady-state step” AR (autoregulatory indexes = ∼0.5 vs. ∼0.1 in controls, P < 0.05; n = 9–10/group). By contrast, dynamic AR compensation in separate conscious rats (n = 12) was not significantly altered by either amlodipine (n = 10) or mibefradil (n = 6; fractional gain in admittance ∼0.4–0.5 in all groups at frequencies in the range of 0.0025–0.025 Hz). However, both CCBs tended to attenuate the myogenic resonance peak along with shifting it to a significantly slower frequency (P < 0.001) during dynamic AR, but no consistent effects were observed on the tubuloglomerular feedback resonance peak. While the reasons for the insensitivity of dynamic vs. steady-state step AR capacity estimates to CCBs remain to be established, the present data indicate that dynamic AR methods may have a limited utility for assessing AR capacity but may provide potentially important insights into the operational characteristics of AR control mechanisms. A strong correlation was also observed between the average conductance and the admittance gain at the heart beat frequency (r = 0.77, P < 0.001), suggesting that such parameters may provide additional and possibly more meaningful indexes of BP transmission in conscious animals during dynamic AR.