• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.236-238
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• 2005

In this paper, we propose a new discrete-time predictive current controller for a PMLSM(permanent magnet linear synchronous motor). The main objectives of the current controllers are that the measured stator current is tracked the command current value accurately and the transient interval is shorten as much as possible, in order to obtain high-performance of ac drive system. The conventional predictive current controller is hard to implement in full digital current controller since a finite calculation time causes a delay between the current sensing time and the time that take to apply the voltage to motor. A new control strategy is the schema that gets the fast adaptation of transient current change, the fast transient response tracking. Moreover, the simulation results will be verified the improvements of Predictive controller and accuracy of the current controller.

• Journal of Magnetics
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• v.21 no.3
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• pp.322-329
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• 2016

In this paper, we propose an array sensing detection method for smelting of submerged arc furnaces (SAF) based on electromagnetic radiation. AC magnetic field generated by electrode currents and molten currents in the furnace is reflected outside of the furnace. According to the spatial distribution of electromagnetic field a radiation model of SAF is built. We design a 3D magnetic field sensing array system in order to collect the magnetic field information. Through the collected information, the current distribution characteristics of SAF are described and the key parameters of smelting are obtained. Theoretical simulation and field test show that the curves acquired by the sensing array can accurately reflect the information of the relative displacement when the relative displacement between the array and electrode is 10 cm. Compared with the detection method of 3D single point, the proposed array sensing method of magnetic field obtains better results in terms of real-time and accuracy, and has good practical value for industrial measurement.

• Journal of Magnetics
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• v.19 no.2
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• pp.151-154
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• 2014

Recent years have seen an increasing range of planar Hall resistive (PHR) sensor applications in the field of magnetic sensing. This study describes a new application of the PHR sensor to monitor a current. Initially, thermal drift experiments of the PHR sensor are performed, to determine the accuracy of the PHR signal output. The results of the thermal drift experiments show that there is no considerable drift in the signals attained from 0.1, 0.5, 1 and 2 mA current. Consequently, the PHR sensor provides adequate accuracy of the signal output, to perform the current monitoring experiments. The performances of the PHR sensor with bilayer and trilayer structures are then tested. The minimum detectable currents of the PHR sensor using bilayer and trilayer structures are $0.51{\mu}A$ and 54 nA, respectively. Therefore, the PHR sensor having trilayer structure is the better choice to detect ultra low current of few tens nanoampere.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.103-114
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• 2024

The escalating demands for high-resolution satellite imagery necessitate the dissemination of geospatial data with superior accuracy.Achieving precise positioning is imperative for mitigating geometric distortions inherent in high-resolution satellite imagery. However, maintaining sub-pixel level accuracy poses significant challenges within the current technological landscape. This research introduces an approach wherein upsampling is employed on both the satellite image and ground control points (GCPs) chip, facilitating the establishment of a high-resolution satellite image precision sensor orientation. The ensuing analysis entails a comprehensive comparison of matching performance. To evaluate the proposed methodology, the Compact Advanced Satellite 500-1 (CAS500-1), boasting a resolution of 0.5 m, serves as the high-resolution satellite image. Correspondingly, GCP chips with resolutions of 0.25 m and 0.5 m are utilized for the South Korean and North Korean regions, respectively. Results from the experiment reveal that concurrent upsampling of satellite imagery and GCP chips enhances matching performance by up to 50% in comparison to the original resolution. Furthermore, the position error only improved with 2x upsampling. However,with 3x upsampling, the position error tended to increase. This study affirms that meticulous upsampling of high-resolution satellite imagery and GCP chips can yield sub-pixel-level positioning accuracy, thereby advancing the state-of-the-art in the field.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.75-77
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• 2003

The spatial distribution of snow cover area is a crucial input to models of hydrology and climate in alpine and other seasonally snow covered areas.The objective in our study is to develop a rapidly automatic and high accuracy snow cover mapping algorithm applicable for the Tibetan Plateau which is the most sensitive about climatic change. Monitoring regional snow extent reqires higher temoral frequency-moderate spatial resolution imagery.Our algorithm is based AVHRR and MODIS data and will provide long-term fraction snow cover area map.We present here a technique is based on the multiple endmembers approach and by taking advantages of current approaches, we developed a technique for automatic selection of local reference spectral endmembers.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.6_spc
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• pp.651-659
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• 2016

Condition monitoring (CM) encounters a large data problem due to sensors that measure vibration data with a continuous, and sometimes, high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate the efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer samples compared to traditional sampling methods. For the experiments a built-in rotating system was used and all data were compressively sampled to obtain compressed data. Optimal signal features were then selected without the reconstruction process and were used to detect and classify damage. The experimental results show that the proposed method could improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• Smart Structures and Systems
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• v.29 no.4
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• pp.589-598
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• 2022

Timber structures are susceptible to structural damages caused by variations in moisture content (MC), inducing severe durability deterioration and safety issues. Therefore, it is of great significance to detect MC levels in timber structures. Compared to current methods for timber MC detection, which are time-consuming and require bulky equipment deployment, Lead Zirconate Titanate (PZT)-enabled stress wave sensing combined with statistic machine learning classification proposed in this paper show the advantage of the portable device and ease of operation. First, stress wave signals from different MC cases are excited and received by PZT sensors through active sensing. Subsequently, two non-baseline features are extracted from these stress wave signals. Finally, these features are fed to a statistic machine learning classifier (i.e., naïve Bayesian classification) to achieve MC detection of timber structures. Numerical simulations validate the feasibility of PZT-enabled sensing to perceive MC variations. Tests referring to five MC cases are conducted to verify the effectiveness of the proposed method. Results present high accuracy for timber MC detection, showing a great potential to conduct rapid and long-term monitoring of the MC level of timber structures in future field applications.

• Journal of Ocean Engineering and Technology
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• v.38 no.3
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• pp.103-114
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• 2024

Remote sensing wave observation data are crucial when analyzing ocean waves, the main external force of coastal disasters. Nevertheless, it has limitations in accuracy when used in low-wind environments. Therefore, this study collected the raw data from MIROS Wave and Current Radar (MWR) and wave radar at the Ieodo Ocean Research Station (IORS) and applied the optimal filter by combining filters provided by MIROS software. The data were validated by a comparison with South Jeju ocean buoy data. The results showed it maintained accuracy for significant wave height, but errors were observed in significant wave periods and extreme waves. Hence, this study used an artificial neural network (ANN) to improve these errors. The ANN was generalized by separating the data into training and test datasets through stratified sampling, and the optimal model structure was derived by adjusting the hyperparameters. The application of ANN effectively improved the accuracy in significant wave periods and high wave conditions. Consequently, this study reproduced past wave data by enhancing the reliability of the MWR, contributing to understanding wave generation and propagation in storm conditions, and improving the accuracy of wave prediction. On the other hand, errors persisted under high wave conditions because of wave shadow effects, necessitating more data collection and future research.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.2
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• pp.59-66
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• 2005

Accuracy of current measurement in fiber optic current sensor(FOCS), especially, unidirectional polarimetric fiber optic current sensor(PFOCS) is affected by the environment perturbations such as acoustic vibrations changes to the sensing fiber, and intrinsic perturbations such as the bending fiber that the sensing fiber wound around a current carrying wire. The perturbations affect the birefringence properties of sensing fiber in sensor head and cause false current readings. Thus, using compensation technique, reciprocal PFOCS, for unidirectional PFOCS the perturbations are suppressed. In this paper, we carried out the numerical analysis of performance in reciprocal PFOCS including the degree of polarization error, and false current of environmental and intrinsic perturbations on the sensing fiber. Also, we compared the effect of mirror with the faraday rotation mirror(FRM) in reciprocal PFOCS configuration. And the different optical source's wavelengths, 633nm and 1300nm is used. In the results, at 633nm, using mirror and FRM, the degree of polarization error is calculated to $2.3\%$ and $0.0196\%$, respectively. At $1300{\cal}nm$ using mirror and FRM the degree of polarization error is calculated to $9.97\%$ and $0.0196\%$, respectively. Also, compared with false current, the results is calculated to $9.82{\times}10^{-9}A$ and $1.4{\times}10^{-17}A$, respectively, and show that the reciprocal PFOCS is more robust configuration than unidiretionnal PFOCS for environmental and intrinsic perturbations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3364-3382
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• 2023

Remote sensing image segmentation plays an important role in realizing intelligent city construction. The current mainstream segmentation networks effectively improve the segmentation effect of remote sensing images by deeply mining the rich texture and semantic features of images. But there are still some problems such as rough results of small target region segmentation and poor edge contour segmentation. To overcome these three challenges, we propose an improved semantic segmentation model, referred to as MRU-Net, which adopts the U-Net architecture as its backbone. Firstly, the convolutional layer is replaced by BasicBlock structure in U-Net network to extract features, then the activation function is replaced to reduce the computational load of model in the network. Secondly, a hybrid multi-scale recognition module is added in the encoder to improve the accuracy of image segmentation of small targets and edge parts. Finally, test on Massachusetts Buildings Dataset and WHU Dataset the experimental results show that compared with the original network the ACC, mIoU and F1 value are improved, and the imposed network shows good robustness and portability in different datasets.