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Sensor Network - IOT
❯AI-dispatcher (operator) of large-scale distributed energy system infrastructure
Product Design Using AI
For:
Design Engineers, Product Development Team
Goal:
Improved Product Development / R&D
Large Companies
Knowledge representation - Bayesian Network
For:
Process Engineers.
Scope:
Applying Bayesian Networks to root cause analysis of industrial processes.
Goal:
Improve Operation Efficiency
AI-dispatcher (operator) of large-scale distributed energy system infrastructure
For:
Energy companies focused on AI solutions to drive energy production, transition
and distribution in large territories
Scope:
Monitoring, optimization and control of large-scale distributed energy systems
using deep reinforcement learning (gas, oil, power, heat, and water transmission
and distribution infrastructure systems).
Goal:
Improve Operation Efficiency, Increase Revenues, Reduce costs
Large Companies
Operations - Intelligent Monitoring of Infrastructures
For:
Plant Operators
Scope:
Optimization of thermal efficiency of a power plant using Machine Learning.
Goal:
Improve Operation Efficiency, Improved Employee Efficiency, Reduce costs
Reducing Food Recalls With AI-Dri
For:
Food Companies, Retail Stores, Restaurants
Goal:
Anticipate Risks, Improve Operation Efficiency
Predictive analytics for the behaviour and psycho-emotional conditions of eSports players using heterogeneous data and artificial intelligence
For:
End users
Scope:
Prediction of psycho-emotional conditions of eSports players. To form
predictions, we collect physiological data from wearables/video cameras/eye
trackers, game telemetry data from keyboard/mouse/demo files, and
environmental conditions followed by the application of machine learning
methods for the analysis of the collected data.
Goal:
Other
Robot consciousness
Scope:
A robot for museum tours equipped with the main capabilities of functional
consciousness, accepted by and transparent to untrained users.
Goal:
Other
Ontologies for smart buildings
For:
Those that can affect the AI system: since it is under the supervision of a
university, the data exchange with the building is controlled by the networking
team of the university and the person in charge of the security. A university
network is not so open! It is not like the Internet that individuals can publicly
access. A group of persons in charge of the GDPR would also be deployed during
the use case.
Scope:
Renovation of a building, improvement of the quality of life of the residents
(limited to data issues in the building), audience: citizens, public and private
actors, companies involved in the ICT system managing the building. The scope
is not limited to the building management system (BMS). We would like to open
it to data produced by residents, coupled with data coming from the BMS.
Goal:
Other
Unmanned protective vehicle for road works on motorways
Scope:
Unmanned operation of a protective vehicle in order to reduce the risk for road
workers in short-term and mobile road works carried out in moving traffic.
Goal:
Other
AI components for vehicle platooning on public roads
Scope:
Trains of vehicles that drive very close to each other at nearly equal speed
(platoons) on public roads, in particular platooning trucks on motorways.
Goal:
Improve Operation Efficiency
Use of robotic solution for traffic policing and control
Scope:
Robotics-based traffic policing system.
Goal:
Other
Behavioural and sentiment analytics
For:
Organizations, end users, community
Scope:
Ascertain a person's emotional state and goal from their gestures, facial
expression, and actions.
Goal:
Improve Operation Efficiency
From Weeks Down to Hours: How insurers innovate with AI to shorten claims processing time and improve customer experience
For:
- Claims Management
- Customer Experience.
Goal:
Improved Customer Experience
AI (swarm intelligence) solution for attack detection in IoT environment
For:
End users of smart metering, utility companies
Scope:
Anomaly-based attack detection in an IoT environment using swarm
Intelligence.
Goal:
Other
Robotic solution for replacing human labour in hazardous conditions
Scope:
Building an AI-based robotics solution for replacing human labour in hazardous
conditions.
Goal:
Other
Animal Disease Detection
For:
Dairy Farmers, Factory Farmers
Goal:
Anticipate Risks
Ecosystems management from causal relation inference from observational data
For:
Environment, ecosystem
Scope:
Infer important latent variables to control a whole ecosystem using a database including human observation and sensor data.
Goal:
Improved Product Development / R&D, Improve Operation Efficiency
Weather Forecasting in Agriculture
For:
Farmers
Goal:
Anticipate Risks, Improve Operation Efficiency
Robotic vision scene awareness
For:
Customers, 3 rd parties, end users, community
Scope:
Determining the environment the robot is in and which actions are available to
it.
Goal:
Improve Operation Efficiency
AI to understand adulteration in commonly used food items
For:
Consumers, farmers, health monitoring agencies
Scope:
Understand the patterns in hyperspectral / near infrared (NIR) or visual imaging specifically for adulteration in milk, bananas and mangoes.
Goal:
Improve Operation Efficiency
AI based text to speech services with personal voices for people with speech impairments
For:
People with speech impairments
Scope:
All people who have some sort of speech impairment including but not limited to three basic types: articulation disorders, fluency disorders, and voice disorders.
Goal:
Improved Customer Experience
Large Companies
Manufacturing and Factories – Predictive Maintenance
For:
Operation, R&D
Scope:
Avoiding unplanned shutdowns in Manufacturing using Machine Learning to predict failure states in equipment.
Goal:
Anticipate Risks, Improve Operation Efficiency
Self-driving aircraft towing vehicle
Scope:
Self-Driving towing vehicle for aircrafts, operating on an airfield autonomously.
Goal:
Improve Operation Efficiency
Improving productivity for warehouse operation
For:
Warehouse manager
Scope:
Big data analysis for enhancing productivity.
Goal:
Other
AI-dispatcher (operator) of large-scale distributed energy system infrastructure
For:
Energy companies focused on AI solutions to drive energy production, transition
and distribution in large territoriesGoal:
Improve Operation Efficiency, Increase Revenues, Reduce costsProblem addressed
To develop an effective industrial AI solution that is able to recommend the
optimal control of energy infrastructure systems in real-time in order to:
satisfy the energy demands of consumers;
minimize possible negative impacts on the environment;
reduce operational costs through systems real-time continuous.
Scope of use case
Monitoring, optimization and control of large-scale distributed energy systems
using deep reinforcement learning (gas, oil, power, heat, and water transmission
and distribution infrastructure systems).
Description
Motivation
The existing technologies do not provide an effective
solution to the problem of optimization of distributed energy
systems in real time. At the same time, the effects of
optimization in the energy sector are substantial.
Objects (systems) under consideration
Real large-scale distributed energy systems (gas, oil, power,
heat, water transmission and distribution infrastructure
systems). The main features of systems under consideration:
Territorial distribution and a large number of
interconnected units of equipment with individual
characteristics
The complex physics of technological processes
Huge amounts of real-time information from various
sensors
Problem statement
The central goal of the AI solution that is being developed is
formulated as follows: to ensure the supply of energy of a
certain quality at the right time to all consumers of a
distributed energy system, taking into account all
technological limitations and minimizing the operational
costs through systems real-time continuous optimization.
Solving this problem requires solving a number of subtasks.
Solution approach
The AI solution uses an approach of industrial system
modelling based on hybrid models that combine the benefits
of traditional physics-based modelling and machine learning
capabilities. We use the reliable digital twins of energy
systems and virtual simulators to simulate the systems
physics (dynamics) and we train deep reinforcement
learning models of these systems.
Current results
PoC of the AI system has been developed, which consists of:
digital twins of real gas infrastructure systems;
reliable physics-based models and virtual simulators of
these systems, actively used in industry;
model-free deep reinforcement learning algorithms,
connected with the above-mentioned virtual simulators;
services for training models, visualizing and analysing
the results.
Computational experiments proved that the initial objective
can be achieved with the help of modern AI technologies. The
results show the effectiveness using AI based technologies to
optimize and control of distributed energy systems and that
these solutions can outperform both human capabilities and
traditional optimization algorithms that were proposed
earlier.
Technologies
Physics-based modelling, deep reinforcement learning
technologies, deep learning frameworks, big data
technologies, streaming platforms, cloud-native architecture
of AI-system.
Sensor Network - IOT
Raw Data
AI: Perceive
Times Series
AI: Understand