Watanachaturaporn, Pakorn

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Watanachaturaporn, Pakorn
Alternative Name
Watanachaturaporn, P.
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pakorn.wa@kmitl.ac.th

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    Temperature Compensation for Transformer-type Transducer
    (2021-01-01)
    Songsuwankit, Kanoknuch
    ;
    Riewruja, Vanchai
    ;
    Watanachaturaporn, Pakorn
    ;
    Rerkratn, Apinai
    ;
    Petchmaneelumka, Wandee
    A novel technique to compensate the temperature effect of a transformer-type transducer is proposed in this paper. The effect of the ambient temperature on the transformer-type transducer is investigated from a primary-winding current. The advantage of the proposed technique is that the temperature effect is compensated without requiring a temperature sensor, making it suitable for applications in robotic and automation systems operated in harsh environments. The primary-winding current of the transducer is generated using a second-generation current conveyor (CCII). The excitation signal of the transformer-type transducer is driven by the CCII and the current flowing through the primary winding is transferred to an output signal of the CCII. The deviation of the primary-winding current due to the temperature effect is evaluated from the output signal of the CCII. The temperature effect on the transducer is manipulated by a closed-loop principle using a subtract-and-sum action instead of a traditional proportional-plusintegral action to eliminate the deviation of the primary-winding current. Therefore, the temperature effect on the transducer is compensated. A linear variable differential transformer (LVDT) is used to demonstrate the proposed technique, whose performance is discussed in detail and confirmed experimentally. All devices used in this experiment are commercially available. Experimental results show that the measured error of the output signal from the LVDT at 70 C can be reduced from 6.2% without temperature compensation to 0.06% by using the proposed technique, which has the advantages of a low cost, simple configuration, and high performance.
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    A novel multi-optical/quantum memory and encoding system using multi-photon generation
    (2010-12-01)
    Glomglome, S.
    ;
    Mitatha, S.
    ;
    Watanachaturaporn, P.
    ;
    Suchat, S.
    ;
    Yupapin, P. P.
    We propose a novel system of an optical/quantum memory generation, which can be used for multi-optical/quantum memory applications. The large bandwidth of a single pulse is generated using a soliton pulse in a Kerr-type nonlinear medium, i.e. a nonlinear waveguide. The generation of the localized temporal and spatial soliton pulses within the nano-waveguide is achieved. The free spectrum range enhancement of the generated multi-soliton signals can be formed and achieved using the nano-waveguide incorporating the Mach Zhender Interferometer (MZI). The different light path of the soliton pulses is introduced by the delayed lines of the interferometer. This improves the wavelength free spectrum range, where the different entangled photon pairs can also obtained. Furthermore, the generated photons can be filtered and stored within a system, where the storage of single or multi-photons using the proposed system can be achieved, which in turn can be used for multi-optical/quantum memory applications. © 2009 Elsevier GmbH. All rights reserved.
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    Multisource classification using support vector machines: An empirical comparison with decision tree and neural network classifiers
    (2008-01-01)
    Watanachaturaporn, Pakorn
    ;
    Arora, Manoj K.
    ;
    Varshney, Pramod K.
    Remote sensing image classification has proven to be attractive for extracting useful thematic information such as landcover. However, often for a given application, spectral information acquired by a remote sensing sensor may not be sufficient to derive accurate information. Incorporation of data from other sources such as a digital elevation model (DEM), and geophysical and geological data may assist in achieving more accurate land-cover classification from remote sensing images. Recently, support vector machines (SVM) have been proposed as an alternative for classification of remote sensing data, and the results are promising. In this paper, we employ the SVM algorithm to perform multi-source classification. An IRS-1C LISS III image along with normalized differenced vegetation index (NDVI) image and DEM are used to produce a land-cover classification for a region in the Himalayas. The accuracy of SVM-based multi-source classification is compared with several other non-parametric algorithms namely a decision tree classifier, and back propagation and radial basis function neural network classifiers. The well-known kappa coefficient of agreement is used to assess classification accuracy. The differences in the kappa coefficient of classifiers have been statistically evaluated using a pairwise Z-test. The results show a significant increase in the accuracy of the SVM based classifier on incorporation of ancillary data over classification performed solely on the basis of spectral data from remote sensing sensors. © 2008 American Society for Photogrammetry and Remote Sensing.