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Private Panoramas Gallery


Another type of hobby gallery project, focused on own made panorama photos, from different places I was travelling to. This is even simpler than my other gallery project Antypodish Galleries.
Main difference in comparison to my other gallery is, these panorama pictures can be slided side way with mouse, or finger (on touchscreen).

I used in this project available solution from JQuery Panorama ViewerEmbed a Panorama Pictures on Your WebsiteCreated by Pete R., Founder of Travelistly and BucketListly.






University



Unity


3DoF Gimbal Controller : Neural Network (NN)


Presenting 3 Degrees of Freedom Gimbal Controller, based on thruster controller for spacecraft (i.e. satellite) applications in Unity3D, using Evolving Neural Network - NEAT.

It was been while, number of searches, efforts, trials and errors past, aiming, to create the controller in Unity3D, which will allow orient a satellite toward target reference, i.e. an orbit of the planet. Rather than using standard Unity3D method Lerp, to set the orientation, I wanted to simulate close to realistic behavior, of thruster based controller for spacecrafts. This means, each individual axis of the satellite (spacecraft), can be controlled individually, to ensure desired orientation of 3 axis. Not only orientation can be defined for each 3 axis, but also angular velocity for any of axis. This allows for example, for traveling satellite, to spin around one of its axis, while pointing into desired direction.


The project was made using Unity 3D 2018.2.0b4. Howver, since is c# based, it should be able work on older version of Unity3D. Unity Package can be accessed here:
NEAT_3DoF_GimbalController.unitypackage
In case it do not work for any reaosn, specially for oolder version, copy of project folder is here:
NEAT_3DoF_Orientation.7z


Project uses Evolving Neural Network - NEAT, based on Evolving Neural Networks through Augmenting Topologies.
NEAT Paper by Kenneth O. Stanley and Risto Miikkulainen:
Evolving Neural Networks through Augmenting Topologies


Project is derived from:
Evolving Neural Networks NEAT With 3D Cars + Tutorial
by
The One, published July 2016
Evolving Neural Networks NEAT With 3D Cars + Tutorial





Machine Learning


Unity : Power Grid : Genetic Neural Network


Project goal is to simulate behavior of power grid (very simplified), using Evolving Neural Network approach.

The concept of using Neural Network for controlling power production, based on demand and associated cost, is to prove the concept, if such system can respond accordingly to many variables, ensuring that power demand is met, yet reducing power overproduction across multiple power plants.

Due to short deadline for this project, power grid design has been extremely simplified, in respect to initial goal.

Network of substations has been narrowed into single substation. Hence, no step up/down power grid is considered. Power level across system is an arbitrary value as Mega Watts for simplification, rather than realistic power grid values of M/G Watts, on each system point. There are no other sub stations, hence no power redirection, no line overload, or no power grid failure scenarios, in this state.



2 consumers are power delivery points. Their produce a power demand, with two peak times spikes, morning and afternoon.

System includes 3 power plants:


Solar Power Station, which for simplification, produces energy only during day time, with increasing output at morning and increasing output in the evening. Additional weather events and its forecasting are not taken int consideration.

Large Power Station has relative slow and lagging response to the power demand change. Either significant overproduction is required, or supporting power station, with faster response.

Small Power Station is supporting power station, to its larger version. Its maximal power output is much lower, but its response is much faster, to meet volaille power demand changes. However, power production cost is also much higher. So it would be desired, to reduce its output, when adequate power demand is lower.

Click for more details ...

Project uses Evolving Neural Network - NEAT, based on Evolving Neural Networks through Augmenting Topologies.pdf (NEAT Paper by Kenneth O. Stanley and Risto Miikkulainen)




Unity3D : 3DoF Gimbal Controller : Genetic Neural Network


Project focused on designing of 3DoF Gimbal Controller, to simulate behavior of orbiting spacecraft controlled with thrusters, with given reference point, using Evolving Neural Network (NEAT) approach.

It was been while, number of searches, efforts, trials and errors past, aiming, to create the controller in Unity, which will allow orient a satellite toward target reference, i.e. an orbit of the planet. Rather than using standard Unity method Lerp, to set the orientation, I wanted to simulate close to realistic behavior, of thruster based controller for spacecrafts. This means, each individual axis of the satellite (spacecraft), can be controlled individually, to ensure desired orientation of 3 axis. Not only orientation can be defined for each 3 axis, but also angular velocity for any of axis. This allows for example, for traveling satellite, to spin around one of its axis, while pointing into desired direction.

Video discuses steps taken, to train brain in Evolving Neural Network and showcases results. Configuration took steps in brain training, which resulted in only few generations of species, to achieve decent responsive system. C# code is also discussed.

As input to neural network, I used angular velocity error of xyz (3 inputs), in respect to the target, rather than position, or rotation itself. As output, I simulate thrusters behavior, by applying torque force on each side of model for xyz (3 outputs), where value is accepted both positive and negative. Can try imagine space shuttle, which fires multiple directional thrusters, to orient spacecraft in a space.

Fitness of Nerual Network, is the summary of angular velocity error, in respect to the reference angular velocity.

Unity Package. The project was made using Unity 2018.2.0b4. Howver, since is c# based, it should be able work on older version of Unity.

Project uses Evolving Neural Network - NEAT, based on Evolving Neural Networks through Augmenting Topologies.pdf (NEAT Paper by Kenneth O. Stanley and Risto Miikkulainen)





Industry



Gaming : Mix



Gaming : Minecraft



Gaming : From The Depths : Modding



Gaming : From The Depths : Filming



Gaming : From The Depths : Designs



Artwork



Architecture