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On-Line RSS Calibration Method Based on Partial Errors-in-Variables Model
Kim, Jung-Hee,Kim, Doik IEEE 2018 IEEE SENSORS JOURNAL Vol.18 No.21
<P>The received signal strength-based localization with the propagation model is a simple and inexpensive method to estimate the location in an indoor environment. However, this approach gives inaccurate results due to environmental and situation-dependent noisy signal. Therefore, it is important to calibrate parameters of the propagation model in an on-line manner. In wireless sensor networks (WSNs), there exist not only uncertainties such as inherent measurement noise and inaccurate location of unknown nodes, but also certainties such as accurate location information of anchor nodes. By observing that both such certainty and uncertainty of the WSN can be appropriately formulated under the partial errors-in-variables (EIV) model, the proposed calibration method is derived under the partial EIV model to estimate the model parameters with a high accuracy. The solution can be obtained in a computationally efficient way by applying the weighted total least square method. Various simulations and real experiment are conducted to illustrate the performance of the proposed partial EIV-based calibration method, and we conclude that its performance is close to the results of the true parameters under various conditions.</P>
실시간으로 물체의 무게와 무게 중심을 추정하는 모듈러 그리퍼 개발
김동영(Dong-Young Kim),김도익(Doik Kim) 대한기계학회 2016 대한기계학회 춘추학술대회 Vol.2016 No.12
Recently in robot manipulation system, a pure external wrench separating method was proposed by deriving the mass and center of mass (COM) through the wrench and virtual point mass systems. In this paper, by exploiting the previous technique, we develop a modular gripper for estimating the mass and COM in real-time. In particular, the proposed modular gripper can be employed for existing manipulators in any circumstances. Finally, the experiment results show that the proposed gripper yield approximated mass and COM, compared with those of real objects.