COVID-19 fever symptom detection based on IoT cloud

This paper presents a new method of detection COVID-19 fever symptoms depending on IoT cloud services to solve the higher time delay of checking the crowded clients that enter public or private agencies which can lead to a dangerous field to spread the disease. An automatically checking process is suggested using a practical experiment is developed using (ESP8266 Node MCU, Ultrasonic (SR-04), RFID (RC522), human body temperature (MAX30205) sensors, and ThingSpeak platform). Where Node MCU is open-source hardware used to transmit the received data (human temperature sensor) from the (MAX30205) to the cloud platform (ThingSpeak) then alert the monitoring manager user when the collected data reached a critical value that specified previously and automatically take action to solve this situation. At the same time, the cloud platform will provide a graphical representation of the received data to display it using different monitoring devices such as (computers, mobiles, and others)

An improved swarm intelligence algorithms-based nonlinear fractional order-PID controller for a trajectory tracking of underwater vehicles

This paper presents a nonlinear fractional order proportional integral derivative (NL-FOPID) for autonomous underwater vehicle (AUV) to solve the path tracking problem under the unknown disturbances (model uncertainty or external disturbances). The considered controller schemes are tuned by two improved swarm intelligence optimization algorithms, the first on is the hybrid grey wolf optimization with simulated annealing (HGWO-SA) algorithm and an improved whale optimization algorithm (IWOA). The developed algorithms are assessed using a set of benchmark function (unimodal, multimodal, and fixed dimension multimodal functions) to guarantee the effectiveness of both proposed swarm algorithms. The HGWO-SA algorithm is used as a tuning method for the AUV system controlled by NL-FOPID scheme, and the IWOA is used as a tuning algorithm to obtain the PID controller’s parameters. The evaluation results show that the HGWO-SA algorithm improved the minimal point of the tested benchmark functions by 1-200 order, while the IWOA improved the minimum point by (1-50) order. Finally, the obtained simulation results from the system operated with NL-FOPID shows the competence in terms of the path tracking by 1-15% as compared to the PID method

Disturbance rejection controller design based on nonlinear with fuzzy approximation technique for a tidal turbine system

In this work, a novel disturbance rejection controller based on a nonlinear fractional order proportional integral derivative with fuzzy (NLFOPIDF) approximation and an adaptive law technique for a 160 kW two horizontal axis parts tidal turbine model is proposed. The tidal turbine system encounters gigantic unknown uncertainties with external and internal disturbances induced by fatigue forces and non-uniform operative thrust and turbulence deviations caused by the effect of wind and wave movements. The main working purpose of the tidal turbine system is to extract the maximum power generated by obtaining and tracking the optimal turbine speed. To track the optimal speed turbine, two controller loops are synthesized for the tidal turbine dynamics (outer loop) and current dynamics in the q-axis component (inner loop). The stability and convergence are verified for the outer and inner loops using a candidate Lyapunov function. An approximation fuzzy function is proposed to estimate the nonlinear dynamics of the tidal turbine system, and an adaptive technique is applied to adapt the tidal turbine system against the variation in nonlinear dynamics. The results demonstrate that the NLFOPIDF controller is superior to other works in optimal power generation and optimal turbine speed tracking. Moreover, this controller can be used to achieve the maximum power coefficients to get the optimal power generation

An Adaptive Nonlinear PID Design for 6-DOF Underwater Robotic Vehicle

An Adaptive Nonlinear PID (ANLPID) controller for six Degrees of Freedom (6-DOF) Under- water Robotic Vehicle (URV) model is proposed to solve the path tracking problem. The path tracking problem is mainly caused by external environmental disturbances and the unknown uncertainties of the URV model. The ANLPID controller is used to estimate both the exter- nal disturbances and the unknown URV uncertainties. The performance of the ANLPID controller was eval- uated by comparing the ANLPID controller with other existing works that are Nonlinear PID (NLPID) con- troller and Nonlinear Fractional PID (NLFOPID) con- troller. The system stability is proved by utilizing the Lyapunov function. At the end, the results obtained show the proficiency of the ANLPID controller, where the ANLPID controller improved the performance of the URV by 41.4185 % compared to the NLPID con- troller and by 54.6479 % compared to the NLFOPID controller

The quest for indigenous aquafeed ingredients: a review

The growing demand for fish food products worldwide heightens the pressure on the current availability of feed ingredients, especially marine resources such as fishmeal and fish oil, for aquaculture feed production. To address this concern, the potential of novel ingredients for use in feed formulations needs to be tapped. This paper highlights recent researches undertaken concerning the dietary inclusion of various indigenous ingredients classified as seaweeds, leaf meals, oilseed meals, vegetable oils and medicinal herbs, and their effects on growth performance, feed intake, feed utilization efficiency, nutrient retention, disease resistance and other physiological activities among consumer species. Moreover, this paper presents information regarding their nutritive values, optimum inclusion levels and recommended protocols that can improve their potential as feed additives or fishmeal replacements