This presentation explains Deep Learning for the industrial applications and development of Vertical Application for the Industrial Market using a combination of Deep Learning and Machine Learning to the Engineering field. Now that we are expanding the market to the more general world of IoT and Industry 4.0 we understand that most companies are looking to vertical applications, we based our focus on the concept of Combinatorial Innovation: development on the most performing Open Source libraries like Nervana Neon or TensorFlow and integrate the Data Analysis. Enrico Busto

1. AI + ENGINEERING
From Physical toVirtual Sensors using DEEP LEARNING
#aiwtb

2. We developed a novel approach by adapting Deep Learning algorithms to the classical
Engineering applications like signal filtering, sensor fusion and control systems.
This gave us a competitive advantage over the existing solutions.
AI + ENGINEERING

3. This is made possible by what we call
COMBINATORIAL INNOVATION
We Re-Engineer the Deep Learning Algorithms developed for the web
and adapt it to work on generic sensor data.

4. HUGE demand for Deep LearningVertical Applications
in Industrial Environments
Examples - some of our projects:

5. TIRE DOT CODE RECOGNITION
DOT XB 4J R523 4213

6. Target Detected
Class: Missile Launcher
Type: MAZ-543 Uragan
MILITARYTARGETS DETECTION AND RECOGNITION

7. ADVANCEDTRACTION CONTROLS
VEHICLE DYNAMICS
DRIVING STYLE DETECTION

8. VIRTUALTHERMOMETERS
VIRTUAL FLOW-METERS
EMISSION CONTROLS
TURBO SPEED

9. ADVANCED NONLINEAR CONTROLS
WASHING MACHINES

10. ADAPTIVE LIGHTING CONTROLS
EARLY FAULT DETECTION

11. PREDICTIVE MAINTENANCE
PRODUCTION FORECAST

12. TEMPERATURE FORECASTING
HVAC OPTIMIZATION

13. Lets focus on FORECASTING

14. t
y
t0
future
Typical approach with FFNN

15. t
y
t0
Dimensionality problem

16. Recurrent Neural Networks RNN
u(t) X y(t)
Mind This LOOP !!

17. Xu(t0) y(t0)
Xu(t1) y(t1)
Xu(t2) y(t2)
Xu(tn) y(tn)
Xu(t2) y(t2)
Something Happens
at a certain moment in time
The INTERNAL STATE
keeps the event in memory
But the MEMORY of the event
is soon forgotten
The
VANISHING GRADIENT PROBLEM
TIME FLOW

18. Input Node
Input Gate
Output Gate
Forgetting Gate
Output Node
u
y
X
σ
φ
Input
Output
State
Sigmoid [0 ÷1]
Tanh [-1 ÷+1]
⊕
+
π
Sum
Concatenation
Product
σ
σ
σ
φ φu yX⊕ +
π
ππ
Long-ShortTerm Memory LSTM

19. Input Node
Input Gate
u
y
X
σ
φ
Input
Output
State
Sigmoid [0 ÷1]
Tanh [-1 ÷+1]
⊕
+
π
Sum
Concatenation
Product
σ
φu X⊕ +π
Long-ShortTerm Memory LSTM

20. Forgetting Gate
u
y
X
σ
φ
Input
Output
State
Sigmoid [0 ÷1]
Tanh [-1 ÷+1]
⊕
+
π
Sum
Concatenation
Product
σ
u X⊕ +
π
Long-ShortTerm Memory LSTM

21. Output Gate
Output Node
u
y
X
σ
φ
Input
Output
State
Sigmoid [0 ÷1]
Tanh [-1 ÷+1]
⊕
+
π
Sum
Concatenation
Product
σ
φu yX⊕ π
Long-ShortTerm Memory LSTM

22. How to build the InputVector for the LSTM

23. t
y
PRESENT
FUTUREPAST
This is a CONTROLLED VARIABLE:
you know PAST and FUTURE
IF NEEDED here you can
use a causal digital filter
The Target Signal should only be filtered
with a NON-CAUSAL digital filter to avoid LAG

24. This is the INPUT VECTOR
for the LSTM
u
This is the TARGET VALUE
for the LSTM (training) and
PREDICTION (inference)
y
This is the TARGET VALUE
for the LSTM (training) and
PREDICTION (inference)
Future Values of the CONTROLLED VARIABLES
can be shifted back to present
This is the (vanishing) PAST: you do not have
to manage it: the internal states of the LSTM
will manage (forget) it for you.
PRESENT
FUTUREPAST

25. Remember to Subscribe to the
MACHINE LEARNING ITALY Meetup
To be Updated on Examples - Code - Benchmarks
meetup.com/it-IT/Machine-Learning-Italy

26. Visit
add-for.com/training-material
To download the code and the sample data
Contact Me
it.linkedin.com/in/ebusto

27. Time for CODING