Browsing by Author "Lin, Y. Y."

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A new data-driven neural fuzzy system with collaborative fuzzy clustering mechanism
(2015-11-01)
Prasad, M.
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Lin, Y. Y.
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Lin, C. T.
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Er, M. J.
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Prasad, O. K.
In this paper, a novel fuzzy rule transfer mechanism for self-constructing neural fuzzy inference networks is being proposed. The features of the proposed method, termed data-driven neural fuzzy system with collaborative fuzzy clustering mechanism (DDNFS-CFCM) are; (1) Fuzzy rules are generated facilely by fuzzy c-means (FCM) and then adapted by the preprocessed collaborative fuzzy clustering (PCFC) technique, and (2) Structure and parameter learning are performed simultaneously without selecting the initial parameters. The DDNFS-CFCM can be applied to deal with big data problems by the virtue of the PCFC technique, which is capable of dealing with immense datasets while preserving the privacy and security of datasets. Initially, the entire dataset is organized into two individual datasets for the PCFC procedure, where each of the dataset is clustered separately. The knowledge of prototype variables (cluster centers) and the matrix of just one halve of the dataset through collaborative technique are deployed. The DDNFS-CFCM is able to achieve consistency in the presence of collective knowledge of the PCFC and boost the system modeling process by parameter learning ability of the self-constructing neural fuzzy inference networks (SONFIN). The proposed method outperforms other existing methods for time series prediction problems. (C) 2015 Elsevier B.V. All rights reserved.
Design and implementation of a dual-antenna GPS receiver
(2020-01-01)
Lin, Y. Y.
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Hsu, T. H.
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Chen, T. L.
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機械工程學系
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Department of Mechanical Engineering
In the Global Position System (GPS) technology, the GPS receiver (GPSR) has to receive the satellite signal to locate its position in a 3D space. For the GPS applications where the GPSR carrier (vehicle) is rotating, the signal-receiving-path could be temporally blocked by the attitude of the vehicle. And, once the GPSR loses track of the signal, it takes at least 6 seconds for the GPSR to resume its positioning task. This paper proposes a novel dual-antenna GPSR system (hardware architecture and algorithms) that can solve the intermittent signal problem when the vehicle is rotating and continuing on its positioning task. Current experimental results indicate that the proposed GPSR design can successfully output its position when the vehicle is rotating at 7.5 rpm. At 22 rpm, some satellite signals remain on-lock but some do not, depending on the strength of receiving signals.
Device Instability of ReRAM and a Novel Reference Cell Design for Wide Temperature Range Operation
(2017-09-01)
Lin, Y. H.
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Lin, Y. Y.
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Lee, F. M.
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Ho, Y. H.
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Hsu, K. C.
This letter addresses two difficult challenges for transition metal oxide resistive random access memories (ReRAMs)-sensitivity to operation temperature and random fluctuation of resistance value. A careful study of a WOx ReRAM array reveals that these devices are unstable and their read currents fluctuate with time due to random telegraph noise and structure relaxation. Consequently, even after careful programming, the current distribution can broaden with time and tail bit population grows. Furthermore, ReRAM state is sensitive to temperature and a reference cell made of the same device material must be used. Thus, although the broadening of read current distribution may be tolerated by creating a larger RESET/SET memory window, the current fluctuation still makes it impossible to predict the output of the reference cell, leading to array malfunction. This letter investigates the current (resistance) instability in detail and proposes a reference array with a novel trimming method that can stabilize the reference current level. The proposed reference array has very good dc stress reliability as well as wide-range temperature tracking performance from -40 degrees C to 85 degrees C.
Takagi-Sugeno-Kang Type Collaborative Fuzzy Rule Based System
(2014)
Chou, K. P.
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Prasad, M.
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Lin, Y. Y.
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Joshi, S.
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Lin, C. T.
In this paper, a Takagi-Sugeno-Kang (TSK) type collaborative fuzzy rule based system is proposed with the help of knowledge learning ability of collaborative fuzzy clustering (CFC). The proposed method split a huge dataset into several small datasets and applying collaborative mechanism to interact each other and this process could be helpful to solve the big data issue. The proposed method applies the collective knowledge of CFC as input variables and the consequent part is a linear combination of the input variables. Through the intensive experimental tests on prediction problem, the performance of the proposed method is as higher as other methods. The proposed method only uses one half information of given dataset for training process and provide an accurate modeling platform while other methods use whole information of given dataset for training.