Human Factors While Using Head-Up-Display in Low Visibility Flying Conditions

Flying an aircraft in low visibility is still a challenging task for the pilot.It requires precise and accurate situational awareness(SA)in real-time.A Head-up Display(HUD)is used to project collimated internal and externalflight information on a transparent screen in the pilot’s forwardfield of view,which eliminates the change of eye position between Head-Down-Display(HDD)instru-ments and outer view through the windshield.Implementation of HUD increases the SA and reduces the workload for the pilot.But to provide a betterflying capability for the pilot,projecting extensive information on HUD causes human factor issues that reduce pilot performance and lead to accidents in low visibility conditions.The literature shows that human error is the leading cause of more than 70%of aviation accidents.In this study,the ability of the pilot able to read background and symbology information of HUD at a different level of back-ground seen complexity,such as symbology brightness,transition time,amount of Symbology,size etc.,in low visibility conditions is discussed.The result shows that increased complexity on the HUD causes more detection errors.

Hybrid Energy Storage to Control and Optimize Electric Propulsion Systems

Today,ship development has concentrated on electrifying ships in commercial and military applications to improve efficiency,support highpower missile systems and reduce emissions.However,the electric propulsion of the shipboard system experiences torque fluctuation,thrust,and power due to the rotation of the propeller shaft and the motion of waves.In order tomeet these challenges,a new solution is needed.This paper explores hybrid energy management systems using the battery and ultracapacitor to control and optimize the electric propulsion system.The battery type and ultracapacitor are ZEBRA and MAXWELL,respectively.The 3-,4-and 5-blade propellers are considered to produce power and move rapidly.The loss factor has been reduced,and the sea states have been found through the Elephant Herding Optimization algorithm.The efficiency of the proposed system is greatly enhanced through torque,thrust and power.The model predictive controller control strategy is activated to reduce load torque and drive system Root Average Square(RMS)error.The implementations are conducted under the MATLAB platform.The values for torque,current,power,and error are measured and plotted.Finally,the performance of the proposed methodology is compared with other available algorithms such as BAT and Dragonfly(DF).The simulation results show that the results of the proposed method are superior to those of various techniques and algorithms such as BAT and Dragonfly.

Controller Placement in Software Defined Internet of Things Using Optimization Algorithm

The current and future status of the internet is represented by the upcoming Internet of Things(IoT).The internet can connect the huge amount of data,which contains lot of processing operations and efforts to transfer the pieces of information.The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics,sensors and network connectivity.Nowadays,there are two trending technologies that take the platform i.e.,Software Defined Network(SDN)and IoT(SD-IoT).The main aim of the IoT network is to connect and organize different objects with the internet,which is managed with the control panel and data panel in the SD network.The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers.It is more significant for wide area networks,because of the large packet propagation latency and the controller placement problem is more important in every network.In the proposed work,IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization(ESFO)algorithm and Pareto Optimal Controller placement tool(POCO)for the placement problem of the controller.In order to prove the efficiency of the proposed system,it is compared with other existing methods like PASIN,hybrid SD and PSO in terms of load balance,reduced number of controllers and average latency and delay.With 2 controllers,the proposed method obtains 400 miles as average latency,which is 22.2%smaller than PSO,76.9%lesser than hybrid SD and 91.89%lesser than PASIN.