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Sensor Network - IOT

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Ontologies for smart buildings

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.

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

Ontologies for smart buildings

For:

Goal:

Other

Scope of use case

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.

Description

Seminal and technical papers introducing the vocabulary,

definitions, and concepts of smart buildings are References

[205], [206], [207] and [208]. The common view and shared

definition among the community is that a smart building is a

construction with an appropriate design and technological

support to maximize its functionalities and comfort for its

occupants with the compromise to reduce their operational

costs, and extend the life of the physical structure [204].

In Reference [205], the authors presented an initial guide to

understand the layers, taxonomy of services and best

practices for the development of smart buildings. Open

standards are claimed in order to increase interoperability

between layers and services.

In Reference [206], the authors explained variations

between different notions. The findings of the paper allow

the clarification and definition of the border between the

intelligent building and the (more advanced) smart building.

The upper bound of the smart building is defined by (the

future development of) the predictive building. To simplify a

little, from a system point of view, we may think of an

intelligent building as a building reacting to some events

whereas smart buildings are buildings which integrate and

account for intelligence, enterprise, control, and materials

and construction as an entire building system, with

adaptability, not reactivity, at the core, in order to meet the

drivers for building progression: energy and efficiency,

longevity, and comfort and satisfaction.

The INTEL online document [207] is oriented towards the

Internet of things and building management system (BMS).

Analogous to a supervisory control and data acquisition

(SCADA) system used in manufacturing, a building

management system monitors and controls various building

systems, such as heating, ventilation, air conditioning

(HVAC), and lighting with additional and often separate

systems to control elevators, fire, safety, security, and access

controls. We explain later on that our work, at the system

level, is not about BMS, which we consider to be unable to

learn using the data it is managing.

The technical document [208] gives more details about BMS,

direct digital control (DDC), the building automation system

(BAS), and Facility Master System Integrator (FMSI), all of

which are defined according to a system point of view. The

system we propose is more like an operating system for the

building or like an orchestrator of machine learning tasks or

computing tasks and it does not look like any of these

systems.

Finally, the residential buildings system project, from the

Berkeley Lab [197] is also a good source of papers, from

1978 until today, related to smart buildings with a special

focus on the movement of air and associated penalties

involving distribution of pollutants, energy and fresh air.

The ISO process or technology regulations related to Smart

Buildings are ISO 16484-2:2004 [198], ISO 16484-

6:2009[199], ISO 16484-5:2012 [200], and ISO 16484-

3:2005 [201]. They are not related to AI nor to data produced

by residents.

The objective of the use case is to study existing (open) data,

and to build new tools to collect data produced in a building

in order to classify them in ontologies. To be short, an

ontology is knowledge as a set of concepts. The idea behind

the standardization, here, is to put some order in the brute

data and to extract general knowledge. There is a lack of

inclination, in the smart building field, to structure the data

(all types of data) in order to infer and based decisions or

reactions on general knowledge instead of scattered facts.

We are also guessing here that a collective

intelligence/knowledge helps a lot toward good decisions

for people living in buildings.

Sensor Network - IOT

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