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Lee

AAAI Conferences

Providing energy conservation services becomes a hot research topic because more and more people attach importance to environmental protection. This research proposes a framework that consists of four process models: appliance recognition, activity-appliances model, unattended appliances detection, and energy conservation service. Appliance recognition model can recognizes the operating states of appliances from raw sensing data of electric power. An activity-appliances model has been built to associate activities with appliances according to the data of Open Mind Common Sense Project. Using the relationship between activities can help to detect unattended appliances, which are consuming electric power but not take part in the resident's activities. After obtain information of appliance operating states and unattended appliances, residents can receive energy conservation services for notifying the energy consumption information. Finally, the experimental results show that dynamic Baysian network approach can achieve higher than 92% accuracy for appliance recognition. Data of activity-appliances model shows most appliances are strong activity-related.


Explainable-AI (Artificial Intelligence) Image Recognition Startup Pilots Smart Appliance with Bosch

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Z Advanced Computing, Inc. (ZAC), an AI (Artificial Intelligence) software startup, is developing its Smart Home product line through a paid-pilot for smart appliances for BSH Home Appliances, the largest manufacturer of home appliances in Europe and one of the largest in the world. BSH Home Appliances Corporation is a subsidiary of the Bosch Group, originally a joint venture between Robert Bosch GmbH and Siemens AG. ZAC Smart Home product line uses ZAC Explainable-AI Image Recognition. ZAC is the first to apply Explainable-AI in Machine Learning. "You cannot do this with other techniques, such as Deep Convolutional Neural Networks," said Dr. Saied Tadayon, CTO of ZAC.


Maitre

AAAI Conferences

Western societies are facing demographic challenges due the rapid aging of their population. In this context, economic and social issues are emerging, such as an increasing number of elderly in need of home cares and a shortage of caregivers. Smart home technology has imposed itself as a potential avenue of solution to these important issues. Its goal is to provide adapted assistance to a semi-autonomous resident in the form of hints, suggestions, reminders, and to take preventive actions, for instance turning off the oven, in the case of an emergency. The main scientific challenge related to this kind of assistance concerns the problem of recognizing, in real time, of the on-going activities of the resident in order to provide punctual guidance for the completion of everyday tasks. In the literature, the majority of the proposed solutions for activity recognition exploit a complex and expensive network of intrusive sensors (i.e.


Samsung Bixby to Expand

#artificialintelligence

Recall Samsung's acquisition of Harman-Kardon for advanced audio. Expect to hear much more of this Samsung to expand Bixby, voice recognition features to more smart home appliances by 2020 By Deidre Richardson For four consecutive quarters up through May, Samsung has dominated the home appliances market. With the announcement of the Galaxy Note 8 and its expanded Bixby capabilities such as Quick Commands (using small phrases to perform multiple actions simultaneously), as well as the global rollout of Bixby voice in English to over 200 countries earlier this week, Samsung is making a firm statement: that its Bixby AI, whether you want it or not, is here to stay.


Appliance Recognition and Unattended Appliance Detection for Energy Conservation

AAAI Conferences

Providing energy conservation services becomes a hot research topic because more and more people attach importance to environmental protection. This research proposes a framework that consists of four process models: appliance recognition, activity-appliances model, unattended appliances detection, and energy conservation service. Appliance recognition model can recognizes the operating states of appliances from raw sensing data of electric power. An activity-appliances model has been built to associate activities with appliances according to the data of Open Mind Common Sense Project. Using the relationship between activities can help to detect unattended appliances, which are consuming electric power but not take part in the resident’s activities. After obtain information of appliance operating states and unattended appliances, residents can receive energy conservation services for notifying the energy consumption information. Finally, the experimental results show that dynamic Baysian network approach can achieve higher than 92% accuracy for appliance recognition. Data of activity-appliances model shows most appliances are strong activity-related.