PRE2019 4 Group3

SPlaSh

Group members

Student name	Student ID	Study	E-mail
Kevin Cox	1361163	Mechanical Engineering	k.j.p.cox@student.tue.nl
Menno Cromwijk	1248073	Biomedical Engineering	m.w.j.cromwijk@student.tue.nl
Dennis Heesmans	1359592	Mechanical Engineering	d.a.heesmans@student.tue.nl
Marijn Minkenberg	1357751	Mechanical Engineering	m.minkenberg@student.tue.nl
Lotte Rassaerts	1330004	Mechanical Engineering	l.rassaerts@student.tue.nl

First feedback meeting

SPLASH: THE PLASTIC SHARK (tekst van Dennis)

Er ligt heel veel plastic in de zee en dit brengt heel veel problemen met zich mee. Op dit moment is er al een project bezig wat ook bezig is met het schoonmaken van de zee, namelijk The Ocean Cleanup.

We zouden bij dit onderwerp veel verschillende dingen kunnen doen. We zouden een prototype kunnen maken (LEGO, CAD), we zouden iets met beeldherkenning kunnen doen en we kunnen onderzoek doen naar het nut van het gebruik van de SPlaSh.

MOGELIJKHEDEN

1. Ik denk voor het beste resultaat, dat het het best is om het ontwerp in CAD te maken en dat we hier eventueel een simulatie van kunnen maken waarin je kunt zien hoe het werkt.

2. De reden dat we iets met beeldherkenning kunnen doen is dat de SPlaSh plastic, vissen en misschien nog wel andere dingen moet kunnen herkennen.

3. Voor het USE-aspect van dit vak kunnen we kijken of er behoefte is aan de SPlaSh en of mensen er geld in zouden investeren, omdat het een wereldwijd toepasbaar project zal zijn.

SOURCES

1. https://theoceancleanup.com/

2. https://nobleo-technology.nl/project/fully-autonomous-wasteshark/

3. https://www.portofrotterdam.com/nl/nieuws-en-persberichten/waste-shark-deze-haai-eet-plastic

Problem statement and objectives (Kevin)

Plastic in the ocean -> should go

Current solutions trap fish

Knowing the difference between fish and plastic

Who are the users? (Kevin)

Society / scientists?

What do they require? (Kevin)

A clean ocean, safety for fish

Approach, milestones and deliverables (Menno)

For the planning, A Gannt Chart is created with the most important things. The overall view of our planning is that in the first two weeks, a lot of research has to be done. This needs to be done for, among other things, the problem statement, users and the current technology. Which is the wanted to be done in the first week. In the second week, more information about different types of neural networks and the working of different layers should be investigated to gain more knowledge. Also, this could lead to installing multiple packages or programs on our Laptops, which needs time to test if they work. During this second week, a data-set should be created or found that can be used to train our model. If this cannot be found online and thus should be created, this would take much more time than one week. But it’s hoped to be finished after the third week. After this, the group is split into people who creates the design and applications of the robot, and people who work on the creation of the neural network. After week 5, an idea of the robotics should be elaborated with the use of drawings or digital visualizations. Also all the possible Neural Networks should be elaborated and tried so that in week 6, conclusions can be drawn for the best working Neural Network. This means that in week 7, the Wiki-page can be concluded with a conclusion and discussion about the neural network that should be used and the working of the device. Week 8 is finally used to prepare for the presentation.

Currently, no real subdivision has been done to devide between the robotics hardware and software. This should be done in the following weeks and then the Gannt chart, visual below, can be filled in per person.

"ik weet nog niet hoe ik hier een plaatje krijg van de gannt chart"

State of the Art (Lotte, Dennis, Marijn)

Image Recognition (Lotte) robotics?

Over the past decade or so, great steps have been made in developing deep learning methods for image recognition and classification [1]. A decent version of image recognition started when Convolutional Neural Networks (CNN) were used. After more development it appeared that by just using a deep network, an accurate result can be achieved. Since, making deeper networks costs a lot of computations and memory, these had to be reduced, by using less convolutions. However, high-speed processing of deep neural networks is still a challenge. Due to all developments over the past years, image classification has surpassed human level performance. However, there are still limitations to the current image recognition technologies. First of all, most methods are supervised, which means they need big amounts of labelled training data, that needs to be put together by someone [1]. Another problem is that sometimes small distortions can cause a wrong classification of an image [1]. This can already be caused by shadows on an object that can cause color and shape differences [2].

Logbook

Week 1

Name	Total hours	Break-down
Kevin Cox	hrs	description (Xh)
Menno Cromwijk	hrs	description (Xh)
Dennis Heesmans	hrs	description (Xh)
Marijn Minkenberg	1	Setting up wiki page (1h), X
Lotte Rassaerts	hrs	description (Xh)

Template

Name	Total hours	Break-down
Kevin Cox	hrs	description (Xh)
Menno Cromwijk	hrs	description (Xh)
Dennis Heesmans	hrs	description (Xh)
Marijn Minkenberg	hrs	description (Xh)
Lotte Rassaerts	hrs	description (Xh)