Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 서일홍 | - |
dc.date.accessioned | 2019-10-11T07:19:24Z | - |
dc.date.available | 2019-10-11T07:19:24Z | - |
dc.date.issued | 2019-05 | - |
dc.identifier.citation | International Conference on Electronics, Information, and Communication, no. 8706462 | en_US |
dc.identifier.isbn | 978-899500444-9 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/8706462 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/110996 | - |
dc.description.abstract | In this paper, we propose a method to improve the recognition performance of a probabilistic model through entropy analysis after transforming time-varying reaction force/torque signals into frequency components. To perform tasks that require physical interaction, it is important for robots to recognize reaction force/torque during the interactions between robots and environments. However, the reaction force/torque measured by F/T sensor contains a lot of noise signals due to the sensitivity of the sensor. Therefore, the recognition performance depends on noise signals included in training and/or test dataset. To solve this problem, the reaction force/torque signals are transformed from time domain into frequency domain by fast Fourier transform. Then, some task-relevant frequency components are selected based on their entropy analysis, after which they are used to learn a hidden Markov model. To evaluate our proposed method, several robot manipulation tasks are performed using an open dataset including reaction force/torque signals: approaching, transferring and positioning. | en_US |
dc.description.sponsorship | This work was supported by the Technology Innovation Industrial Program funded by the Ministry of Trade, (MI, South Korea) [10048320&100731 61], Technology Innovation Program. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Entropy Analysis | en_US |
dc.subject | Fast Fourier Transform | en_US |
dc.subject | Hidden Markov models | en_US |
dc.subject | Recognition | en_US |
dc.subject | Feature Selection | en_US |
dc.title | Probabilistic modeling of reaction force/torque through Fourier transform and entropy analysis | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.23919/ELINFOCOM.2019.8706462 | - |
dc.relation.page | 1-3 | - |
dc.contributor.googleauthor | Cho, Nam Jun | - |
dc.contributor.googleauthor | Lee, Sang Hyoung | - |
dc.contributor.googleauthor | Suh, Il Hong | - |
dc.contributor.googleauthor | Kim, Hong-Seok | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF ELECTRONIC ENGINEERING | - |
dc.identifier.pid | ihsuh | - |
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