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| 005 | 20260817162740.0 | ||
| 008 | 240229s2022 th u m tt 000 a eng d | ||
| 035 | _a.b12421649 | ||
| 099 | _aAIT Thesis no.DSAI-22-02 | ||
| 100 | 0 | _aSiraphat Boonchan | |
| 245 | 1 | 0 | _aAnomaly detection in the home with seismic sensors |
| 260 |
_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2022 |
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| 300 |
_a81 leaves : _bill. |
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| 490 | 1 |
_aThesis ; _vno. DSAI-22-02 |
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| 500 | _aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Data Science and Artificial Intelligence, School of Engineeing and Technology | ||
| 502 | _aThesis (M. Eng.) - Asian Institute of Technology, 2022 | ||
| 520 | _aFalls are a global public health problem. Falls happen to people of all ages, especially on the elderly. Throughout the last decade, we have seen improvements in fall detection system due to technology development and the revolution of deep learning. However, using vibration signal analysis can compensate the weakness and also overcomes the drawbacks associated with the traditional system, and this is a novel idea that needs to be studied further. This thesis studies the embedded system and design space for unsupervised anomaly detection model using modern deep learning best practices. The performance and effectiveness of this system to immediately send alert message to user via LINE apllication when abnormal events occur. Accordingly, this study can help the home residents when an anomolous event or falling down event is occurring. | ||
| 650 | 0 | _aAnomaly detection (Computer security) | |
| 650 | 0 |
_aArtificial intelligence _xData processing |
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| 650 | 0 | _aDeep learning (Machine learning) | |
| 700 | 1 |
_aDailey, Matthew N., _eChairperson |
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| 700 | 0 |
_aMongkol Ekpanyapong, _eExamination Committee |
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| 700 | 0 |
_aChaklam Silpasuwanchai, _eExamination Committee |
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| 710 | 2 |
_aRoyal Thai Government Fellowship, _eScholarship Donor |
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| 810 | 2 |
_aAsian Institute of Technology. _tThesis ; _vno. DSAI-22-02 |
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| 856 | 4 | 0 |
_3Full-Text _uhttp://203.159.5.9/ait-thesis/Viewer/viewer.php?id=B20415 |
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