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Sensor Network - IOT
❯Self-driving aircraft towing vehicle
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
Self-driving aircraft towing vehicle
Goal:
Improve Operation EfficiencyProblem addressed
A towing vehicle that would, on command, autonomously navigate to an
assigned aircraft, attach itself, tow the aircraft to an assigned location (a runway
for departures, a gate for arrivals), autonomously detach itself, and navigate to
an assigned location, either a staging area or to service another aircraft.
Scope of use case
Self-Driving towing vehicle for aircrafts, operating on an airfield autonomously.
Description
Advances in self-driving automobiles make it technologically
feasible to apply this technology for the purpose of taxiing
planes to the runway from the terminal gate and vice-versa.
Deploying self-driving vehicles for this purpose offers fewer
technical challenges than deploying them on roadways and
highways.
Routes from gates to runways and runways to gates are
typically pre-determined, with little or no possibility for
alternatives. In addition, to ensure safety, constraints on
taxiing operations are rigid and unambiguous.
Rules such as separation constraints between taxiing aircraft
and those governing right-of-way at intersection points are
clearly documented and enforced by ramp and ATC
controllers. These rules and procedures reduce the overall
uncertainty in the operational environment and therefore
potentially simplify the models that are expected to be
employed by self-driving vehicles.
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Nominal autonomous operation of the towing vehicle (tug) is
captured as the following sequence (for the case of
departures): a tug sits at a tug depot, a designated area of the
airport surface where tugs recharge and return when not in
service. When the tug receives a message, describing time,
route, and gate, it travels to the specified gate following the
provided route. As the tug approaches the specified gate, it
navigates to a designated ready position. Once the ground
marshal attending the gate signals readiness for attachment,
the tug assesses the environment to verify the surroundings
are obstacle-free before moving to dock with the aircraft.
Once a taxi navigation plan is received from the centralized
route planner and the aircraft crew and ground marshal both
signal ready to push back, the tug pushes the aircraft away
from the gate and begins navigation through its assigned
route. When reaching a designated location in the take off
queue near the runway, the tug autonomously detaches from
the aircraft, moves to a safe position away from the aircraft,
signals to the aircrafts crew through a cockpit display that it
is detached, and navigates back to the depot along the route
provided by the planner.
Sensor Network - IOT
Live Video
AI: Perceive