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Titlebook: Evolving Ambient Intelligence; AmI 2013 Workshops, Michael J. O’Grady,Hamed Vahdat-Nejad,Gregory O’Ha Conference proceedings 2013 The Edit

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樓主: Detrusor-Muscle
11#
發(fā)表于 2025-3-24 01:23:44 | 只看該作者
https://doi.org/10.1007/978-3-211-76741-2fully. On the other hand there is an urgency provided by the changes in demographics which is putting pressure on governments to provide care to specific sectors of the population, especially older adults, a group which is growing thanks to advances in medicine and greater knowledge on the relationships between lifestyles and health.
12#
發(fā)表于 2025-3-24 06:04:05 | 只看該作者
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發(fā)表于 2025-3-24 08:06:03 | 只看該作者
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發(fā)表于 2025-3-24 12:19:02 | 只看該作者
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發(fā)表于 2025-3-24 15:46:43 | 只看該作者
https://doi.org/10.1057/978-1-137-59823-3mpanied by a proposed behavioural modeling methodology, the application also offers valuable observations that may indicate developing ill-health conditions in an early stage. The proposed platform was tested and evaluated by end-users in Spain, Greece and Finland.
16#
發(fā)表于 2025-3-24 21:50:13 | 只看該作者
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發(fā)表于 2025-3-25 02:13:51 | 只看該作者
Joint Element and Strength Reduction Methodsor data sets, we compare the performance of DS theory and several of its variations in recognising situations. The empirical results shed insight on how to select these fusion rules based on the nature of sensor data, the relationship of this data over time to the higher level hypotheses and the choice of frame of discernment.
18#
發(fā)表于 2025-3-25 04:15:34 | 只看該作者
Puligandla Viswanadham,Pratap Singh which uncertainty of appliance’s identification that have similar signatures, is addressed. Unlike other approaches, our approach uses occupant’s behavioural information to aid appliance disaggregation algorithms. We also demonstrate our technique through experimentation in a household.
19#
發(fā)表于 2025-3-25 09:18:05 | 只看該作者
20#
發(fā)表于 2025-3-25 15:03:45 | 只看該作者
Measuring the Effectiveness of User Interventions in Improving the Seated Posture of Computer Usersel subject intervention that leverages each of three distinct forms of persuasive computing and explores the success of each type. Our results show significant improvement in posture results from the most effective of our intervention types.
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