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Elevator pitch session 2
Published on Jul 08, 2019142 Views
Miha Dežman
,Rok Pahič
,Katarina Marković
,Arijana Filipić
,Zvezdan Lončarević
,Gjorgji Nusev
,Ivan Boškov
,Johanna Amalia Robinson
,Patricia Jovičević Klug
,Tadej Krivec
,Marija Grozdanić
,Matjaž Dlouhy
Related categories
Chapter list
How to train your robot00:00
Motivation00:50
Imitation learning01:21
Reinforcement (RL) learning01:54
Reinforcement (RL) learning - 202:29
How to train your robot - 203:09
Life of chemotherapeutics in human organism-Ruthenium based speciation 03:20
New generation of chemotherapeutics04:05
Analytical method development04:50
Life of chemotherapeutics in human organism-Ruthenium based speciation - 205:20
Method for Fast Estimation of the Parameters In Order to Detect Different Operating Conditions of Electrochemical Energy Devices05:42
Energy storage systems06:16
Fractional-order identification approach07:21
Algebraic Fractional-order identification approach07:59
Algebraic Fractional-order identification approach - 208:36
Writing class for the two year humanoid robot09:03
Writing class for the two year humanoid robot - 209:15
Learning perception-action couplings09:54
Deep encoder-decoder training10:30
Deep encoder-decoder training - 210:48
Fancy math11:14
Non-robotic experiment11:19
Robotic experiment11:42
Robotic experiment - 212:01
Writing class for the two year humanoid robot - 312:09
Automated Baremetal Provisioning for Embedded Devices12:24
The Internet of Things (IoT)12:41
First step in deploying IoT devices (1)12:56
First step in deploying IoT devices (2)13:18
Methods for configuration13:31
Captive Portals13:50
How it works?14:16
Do you know what you are breathing?14:54
What about air quality?15:28
Do you know where to find info?15:38
You can get involved!16:08
But there are issues...16:27
Challenges exist beyond data quality issues16:42
What do they want?17:21
Summary17:52
Summary - 218:04
Ice Age VI: Metallic Materials18:29
Deep Cryogenic Treatment19:02
Metallic materials19:47
Deep Cryogenic Treatment of Metallic Materials20:21
Deep Cryogenic Treatment of Metallic Materials - 221:17
Gaussian Process Regression for Big Data21:47
Nonlinear Identification of Dynamic Systems22:07
Gaussian Process Regression22:27
Scalability22:50
Sparse Approximations23:03
Local Approximations and Fast Matrix Multiplication23:28
Ectodomain shedding of epidermal growth factor receptor by cysteine cathepsins23:52
Ectodomain shedding of epidermal growth factor receptor by cysteine cathepsins - 224:09
Ectodomain shedding of epidermal growth factor receptor by cysteine cathepsins - 324:46
Ectodomain shedding of epidermal growth factor receptor by cysteine cathepsins - 425:26
Ectodomain shedding of epidermal growth factor receptor by cysteine cathepsins - 526:29
How azole inhibitors affect the adsorption of corrosion relevant species26:37
What are we doing?27:11
How are we doing it?27:58
What we discovered so far?28:44
What we discovered so far? - 229:16
Virus inactivation in water by plasma29:41
Virus inactivation in water by plasma - 230:16
Virus inactivation in water by plasma - 330:23
Plasma31:22
Plasma - 231:43
Organic material31:47
Organic material - 232:22
How azole inhibitors affect the adsorption of corrosion relevant species - 232:42
Towards a mechanically compliant exoskeleton32:50
Towards a mechanically compliant exoskeleton - 233:11
Mechanical error buffer33:38
Mechanical error buffer - 233:48
Exoskeleton control problem33:54
Exoskeleton control problem - 234:16
Exoskeleton control problem - 334:21
Exoskeleton control problem - 434:41
Exoskeleton control problem - 534:57
Future work35:13
Towards a mechanically compliant exoskeleton - 335:34
Autonomous Learning of Assembly by Disassembly: (Un)screwing a lightbulb35:58
Manual programming vs. human-like learning36:09
Compliant robot reacts to force by utilizing force sensor data 36:36
A compliant robot will follow the environmential constraints36:54
Intelligent compliant control37:00
Robot can autonomously learn how to exit a maze37:16
Learning of disassembly is similiar to the maze learning37:27
Assembly is in the most cases just reverse execution of disassembly37:36
Autonomous Learning of Assembly by Disassembly: (Un)screwing a lightbulb - 238:11
“Cutting the entropy crisis” using environmental friendly corrosion inhibitors on aluminum substrates38:20
“Cutting the entropy crisis” using environmental friendly corrosion inhibitors on aluminum substrates - 238:41
Energy vs. Entropy38:57
Corrosion process39:35
Increasing the quality of life40:59
Web API for DEX Decision Modeling41:49
John Doe and the Car42:18
United tiny factories as the future for new metabolic pathways45:12
Current challenges45:47
How does it work?46:32
Proof of concept47:01
Proof of concept - 247:27
Proof of concept - 347:41
United tiny factories as the future for new metabolic pathways - 248:07
Can nanoparticles be tamed?48:11
Nanoparticles in everyday life48:51
What are nanoparticles?49:00
Nano or micro? Or can we have both?49:43
Taming of nanoparticles - hydrolysis of AIN50:13
"Winter is coming!"50:37
Feather-light and strong!51:04
Can nanoparticles be tamed? - 251:32
Dangerous seafood? Not anymore!51:39
Dangerous seafood? Not anymore! - 252:17
Dangerous seafood? Not anymore! - 352:46
Dangerous seafood? Not anymore! - 453:07
Dangerous seafood? Not anymore! - 553:19
Dangerous seafood? Not anymore! - 653:50
Dangerous seafood? Not anymore! - 754:08
Dangerous seafood? Not anymore! - 854:47
Characterization of plasma by optical emission spectroscopy55:14
Thin film applications55:31
Triode sputtering56:11
Magnetron sputtering56:50
Optical emission spectroscopy57:22
Poster sessions 258:06