國立虎尾科技大學 |

Physically Motivated Feature Development for Machine Learning Applications.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Physically Motivated Feature Development for Machine Learning Applications./
作者:	Czarnek, Nicholas.
面頁冊數:	1 online resource (171 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
Contained By:	Dissertation Abstracts International79-02B(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355236903

Physically Motivated Feature Development for Machine Learning Applications.
Czarnek, Nicholas.

Physically Motivated Feature Development for Machine Learning Applications. - 1 online resource (171 pages)

Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.

Thesis (Ph.D.)--Duke University, 2017.

Includes bibliographical references

Feature development forms a cornerstone of many machine learning applications. In this work, we develop features, motivated by physical or physiological knowledge, for several applications: energy disaggregation, brain cancer prognosis, and landmine detection with seismo-acoustic vibrometry (SAVi) sensors. For event-based energy disaggregation, or the automated process of extracting component specific energy data from a building's aggregate power signal, we develop low dimensional features that capture transient information near changes in energy signals. These features reflect the circuit composition of devices comprising the aggregate signal and enable classifiers to discriminate between devices. To develop image based biomarkers, which may help clinicians develop treatment strategies for patients with glioblastoma brain tumors, we exploit physiological evidence that certain genes are both predictive of patient survival and correlated with tumor shape. We develop features that summarize tumor shapes and therefore serve as surrogates for the genetic content of tumors, allowing survival prediction. Our final analysis and the main focus of this document is related to landmine detection using SAVi sensors. We exploit knowledge of both landmine shapes and the interactions between acoustic excitation and the ground's vibration response to develop features that are indicative of target presence. Our analysis, which employs these novel features, is the first evaluation of a large dataset recorded under realistic conditions and provides evidence for the utility of SAVi systems for use in combat arenas.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355236903Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Physically Motivated Feature Development for Machine Learning Applications.
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500 $a Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
500 $a Adviser: Leslie Collins.
502 $a Thesis (Ph.D.)--Duke University, 2017.
504 $a Includes bibliographical references
520 $a Feature development forms a cornerstone of many machine learning applications. In this work, we develop features, motivated by physical or physiological knowledge, for several applications: energy disaggregation, brain cancer prognosis, and landmine detection with seismo-acoustic vibrometry (SAVi) sensors. For event-based energy disaggregation, or the automated process of extracting component specific energy data from a building's aggregate power signal, we develop low dimensional features that capture transient information near changes in energy signals. These features reflect the circuit composition of devices comprising the aggregate signal and enable classifiers to discriminate between devices. To develop image based biomarkers, which may help clinicians develop treatment strategies for patients with glioblastoma brain tumors, we exploit physiological evidence that certain genes are both predictive of patient survival and correlated with tumor shape. We develop features that summarize tumor shapes and therefore serve as surrogates for the genetic content of tumors, allowing survival prediction. Our final analysis and the main focus of this document is related to landmine detection using SAVi sensors. We exploit knowledge of both landmine shapes and the interactions between acoustic excitation and the ground's vibration response to develop features that are indicative of target presence. Our analysis, which employs these novel features, is the first evaluation of a large dataset recorded under realistic conditions and provides evidence for the utility of SAVi systems for use in combat arenas.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
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