A simple method to estimate the impedance of the human hand for physical human-robot interaction

Lee, Kyeong Ha; Lee, Hyukjin; Lee, Junghoon; Ji, Sang-Hoon; Koo, Ja Choon

doi:10.1109/urai.2017.7992697

Cited by 9 publications

(1 citation statement)

References 6 publications

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“…Padhy et al [2] built a novel synchrophasor measurement-based wide-area centralized damping controller to improve the stability of a power system in presence of time-varying delay and packet dropout in the communication network. Lee et al [3] proposed a method to drop an object on the human hand and measuring external force and the movement distance of the hand to estimate the impedance parameter. Seuret et al [4] proposed equivalent formulation of Moon et al's inequality, allowing us to discover strong links not only with the most recent and efficient matrix inequalities such as the reciprocally convex combination lemma and also its relaxed version but also with some previous inequalities such as the approximation inequality introduced in Shao (2009) or free-matrixbased inequality.…”

Section: Introductionmentioning

confidence: 99%

A Network Delay Factor Model Based on the Hidden Markov Model and Latent Dirichlet Allocation

Yuchi

Yang

et al. 2019

IEEE Access

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Network systems are widely used in today's digital communication systems and applications. Network delay is a very important factor affecting the efficiency and reliability of the system, and researchers often use a model to predict and understand the causes and patterns of delay. For our model, we propose beginning with the Hidden Markov Model (HMM) of the transmission network, which is used to analyze the network's operational situation and to predict the approximate state of the system into the future using the HMM prediction algorithm. Then, employing Latent Dirichlet Allocation (LDA), we put forward the Delay Factor Model (DFM) value for the delay. In the DFM, we need to map the delay's interval to an integer, designated as DII (Delay Interval Integer), and the factors returned define the hidden states of the HMM. From the view of the DFM, DII is generated from a factor and the previous DII randomly. We use the Gibbs Sampling approach to obtain an estimation of the DFM's parameters. By defining the HMM and DFM, we can forecast future delay with high accuracy, and the result can be shown to follow the peaks and troughs of the real operation of the network system's delay patterns.INDEX TERMS Delay factor model, derivation, hidden Markov model, prediction.

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Section: Introductionmentioning

confidence: 99%