The algorithm of the program is done in so many different ways, some of the methods of writing the algorithm of a program includes the using the flow charts and using the pseudocodes . In this case, the use of pseudocode is employed in the process, (García, Cano, and Herrera 2008). The pseudocodes is one of the informal high level operating principle description of the computer program which makes the use of statement description of the operation of the program. The statements flows in a logical manner from the start of the program to the last step which is exiting or closing up.
As a viable technique, a calculation can be communicated inside a limited measure of room and time and in an all around characterized formal dialect for computing a capacity, (Anderson and Mohan 2012). Beginning from an underlying state and starting info the guidelines portray a calculation that, when executed, continues through a limited number of very much characterized progressive states, in the end creating “yield” and ending at a last closure state. The progress starting with one state then onto the next isn’t really deterministic; a few calculations, known as randomized calculations, consolidate arbitrary information, (Cacciari 2008).
The below is the algorithm which is written in pseudocode that shows the automatic visual checking for the passengers which are boarding the plane.
Pseudocode
Open passenger checking point door
Face recognition camera
If face is not recognized
Prompt with a message display on screen
Else if recognized
Readjust the focus to completely detect the passenger face
Capture the passenger face
Store the captured photo
Open the passport insertion slot
Prompt passenger to insert the passport
If passport not inserted
Prompt three times for passenger to insert passport
If password not inserted
Exit the camera focus and close the system check point
Open the check point door
Else if passport is inserted
Continue
Else if passport is inserted
Close the passport insertion slot
Check the passport serial number
If serial number does not exist in the ministry database
Reject the passport
Open the passport insertion slot
Eject the passport out
Display message of unauthentic passport
Close the detection system
Open the checking point door
If serial number exists in the ministry database
Detect the location of the eye in the passport
Detect the location of the hair in the passport
Take the eye from the passport and store in an array
Take the hair in the face in the passport and store in an array
Detect the location of the eye in the captured photo
Detect the location of the hair in the captured photo
Take the eye from the captured photo and store in an array
Take the hair in the face and store in an array
Check if the arrays are empty
If the array is empty
Take the hair in the face in the passport and store in an array
Take the eye from the captured photo and store in an array
Take the hair in the face and store in an array
Take the eye from the passport and store in an array
If the array is not empty
Detect the color of the eye from the passport
Detect the color of the eye from the captured photo
Detect the color of the hair from the passport
Detect the color of the hair from the captured photo
Compare the color of the two eyes
If color is of the two eyes are not same
Compare the color of the two hair
If the color of the two hair are not the same
Display the message of mismatch
Open the passport insertion slot
Eject the passport from system
Close the recognition system
Open the check point door
If the color of the two hair are the same
Display the message of success
Open the success passenger door
Open the take off passport slot
Eject the passport from take off
Close the recognition system
Open the checkpoint door
If the color of the two eyes are the same
Display the message of success
Open the success passenger door
Open the take off passport slot
Eject the passport from take off
Close the recognition system
Open the checkpoint door
The defining diagram
|
Inputs |
Processing |
Output |
|
Passport Passenger’s face Passport serial number Passenger’s eye Passport eye Passenger’s hair Passport hair |
Read passenger’s face and passport Read passport serial number Read passenger’s and passport eye Read passenger’s and passport hair |
DetectedFace DetectPassport ValidatedSerialNumber MatchingEye MatchingHair Success |
The algorithm below shows the drone which is taking off from the landing strip automatically
Draft
Since the speed is measured in knot which is equal to the one nautical mile, then the conversion of the knot to kilometer per hour is required.
I knot is exactly 1.852 km / h
The taking off plane takes of in 160 knots
Converting the knots to km / h = 160 knots * 1.852 km / h = 296.32 km / h
Then the taking off speed is 296 km / h
The length of the of the landing strip is exactly 1km
The length of the plane is 73 m which is 0.073km
So the maximum distance that the plane can go before landing is less than 0.927 km since the length of the plane is 0.073 km
The algorithm which is implemented using the Pseudocode is as below;
Check plane speed
If the speed of the plane is equal to 296 km / h
Initiate the take off process
Open up the air bags
Initiate connection between the pilot and the watch tower centre
Increase the propeller speed to double the speed
Close up all the open windows in the plane
Inflate the engine of the plane to Turbo engine
Consume the tyres
Check the altitude of the plane
If the altitude is equal to 1km
Initiate the forward mechanism of the plane
Check the peed of the plane
Close the take off auto system
If the speed is more than 296 km / h
Check the distance covered
If distance is more than 500m
Close the engine
Close the push mechanism
Initiate the stopping mechanism
If the speed is less than 296 km / h
Check the speed and distance covered
Repeat the process till 296 km / h is achieved
Landing plane algorithm
Draft
Since the speed is measured in knot which is equal to the one nautical mile, then the conversion of the knot to kilometer per hour is required.
I knot is exactly 1.852 km / h
The landing plane is always at 180 knots speed while at landing
Converting the knots to km / h = 180 knots * 1.852 km / h = 333.36 km / h
Then the taking off speed is 333.36 km / h
The length of the of the landing strip is exactly 1km
The length of the plane is 73 m which is 0.073km
So the maximum distance that the plane can go before landing is less than 0.927 km since the length of the plane is 0.073 km
The algorithm which is implemented using the Pseudocode is as below;
Check plane speed
If the speed of the plane is equal to 333.36km / h
Initiate the landing process
Close up the air bags
Keep the connection between the pilot and the watch tower centre
Reduce the propeller speed to half the speed
Open up all the closed safety windows in the plane
Deflate the engine of the plane to Rolls engine
Start up the tyres for landing
Check the altitude of the plane
If the altitude is equal to or less than 50m
Initiate the forward landing mechanism of the plane
Check the speed of the plane
Open the landing auto system
If the speed is more than 333.36 km / h
Check the distance covered
If distance is more than 500m
Initiate the shut down of the engine
Close the push mechanism
Initiate the stopping mechanism
If the speed is less than 333.36km / h
Check the speed and distance covered
Repeat the process till 0 km / h is achieved
Conclusion
Its is very much possible to automate the processes of the plane which in this case includes the checking and validating of the passenger and the passports, the automation of the taking off of the plane and the landing process of the plane too. By using the above algorithm, the whole processes of the plane will be more than 90% automated hence advancing the technology in the aerospace world.
References
García, S., Cano, J.R. and Herrera, F., 2008. A memetic algorithm for evolutionary prototype selection: A scaling up approach. Pattern Recognition, 41(8), pp.2693-2709.
Anderson, J.B. and Mohan, S., 2012. Source and channel coding: an algorithmic approach (Vol. 150). Springer Science & Business Media.
Cacciari, M., 2008. The anti-kt jet clustering algorithm. Journal of High Energy Physics, 2008(04), p.063.
Beck, A. and Teboulle, M., 2009. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM journal on imaging sciences, 2(1), pp.183-202.
Cacciari, M., Salam, G.P. and Soyez, G., 2008. The anti-kt jet clustering algorithm. Journal of High Energy Physics, 2008(04), p.063.
Stamatakis, A., Hoover, P. and Rougemont, J., 2008. A rapid bootstrap algorithm for the RAxML web servers. Systematic biology, 57(5), pp.758-771.
Zhang, G.X., Gheorghe, M. and Wu, C.Z., 2008. A quantum-inspired evolutionary algorithm based on P systems for knapsack problem. Fundamenta Informaticae, 87(1), pp.93-116.
Lombardi Jr, A.V., Barrack, R.L., Berend, K.R., Cuckler, J.M., Jacobs, J.J., Mont, M.A. and Schmalzried, T.P., 2012. The Hip Society: algorithmic approach to diagnosis and management of metal-on-metal arthroplasty. Bone & Joint Journal, 94(11 Supple A), pp.14-18.
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