• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.527-536
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• 2017

With the recent development of smart grid industry, the necessity for efficient EMS(Energy Management System) has been increased. In particular, in order to reduce electric load and energy cost, sophisticated electric load forecasting and efficient smart grid operation strategy are required. In this paper, for more accurate electric load forecasting, we extend the data collected at demand time into high time resolution and construct an artificial neural network-based forecasting model appropriate for the high time resolution data. Furthermore, to improve the accuracy of electric load forecasting, time series data of sequence form are transformed into continuous data of two-dimensional space to solve that problem that machine learning methods cannot reflect the periodicity of time series data. In addition, to consider external factors such as temperature and humidity in accordance with the time resolution, we estimate their value at the time resolution using linear interpolation method. Finally, we apply the PCA(Principal Component Analysis) algorithm to the feature vector composed of external factors to remove data which have little correlation with the power data. Finally, we perform the evaluation of our model through 5-fold cross-validation. The results show that forecasting based on higher time resolution improve the accuracy and the best error rate of 3.71% was achieved at the 3-min resolution.

• Journal of the Microelectronics and Packaging Society
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• v.19 no.1
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• pp.81-87
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• 2012

This paper focuses on three-dimensional measurement system of micro balls on micro Ball-Grid-Array(BGA) packages in-line. Most of visual inspection system still suffers from sophisticate reflection characteristics of micro balls. For accurate shape measurement of them, a specially designed visual sensor system is proposed under the sensing principle of phase shifting moire interferometry. The system consists of a pattern projection system with four projection subsystems and an imaging system. In the projection system, four subsystems have spatially different projection directions to make target objects experience the pattern illuminations with different incident directions. For the phase shifting, each grating pattern of subsystem is regularly moved by PZT actuator. To remove specular noise and shadow area of BGA balls efficiently, a compact multiple-pattern projection and imaging system is implemented and tested. Especially, a sensor fusion algorithm to integrate four information sets, acquired from multiple projections, into one is proposed with the basis of Bayesian sensor fusion theory. To see how the proposed system works, a series of experiments is performed and the results are analyzed in detail.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.15-20
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• 2017

Recently, with the development of the IT industry, interest in computer convergence technology is increasing in various fields. Especially, in the semiconductor field, a vision system that uses a camera and computer convergence is often used to inspect semiconductor device defects in the production process. Various systems have been studied to remove noise, which is a major cause of degradation in processing of data related to these image processing systems. In this paper, we try to detect defects in BGA (Ball Grid Array) package devices by recognizing defects in advance during mass production. We propose a measurement system using a Gaussian filter, a Median filter, and an Average filter, which are widely used for noise reduction of image data Applying the proposed system to the manufacturing process of the BGA package can be used to judge whether the defect is good or not, and it is expected that productivity will be improved.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.04b
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• pp.7-7
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• 2009

Lead telluride (PbTe) is a very promising thermoelectric material due to its narrow band gap (0.31 eV at 300 K), face-centered cubic structure and large average excitonic Bohr radius (46 nm) allowing for strong quantum confinement within a large range of size. In this work, we present the thermoelectric properties of individual single-crystalline PbTe nanowires grown by a vapor transport method. A combination of electron beam lithography and a lift-off process was utilized to fabricate inner micron-scaled Cr (5 nm)/Au (130 nm) electrodes of Rn (resistance of a near electrode), Rf (resistance of a far electrode) and a microheater connecting a PbTe nanowire on the grid of points. A plasma etching system was used to remove an oxide layer from the outer surface of the nanowires before the deposition of inner electrodes. The carrier concentration of the nanowire was estimated to be as high as $3.5{\times}10^{19}\;cm^{-3}$. The Seebeck coefficient of an individual PbTe nanowire with a radius of 68 nm was measured to be $S=-72{\mu}V/K$ at room temperature, which is about three times that of bulk PbTe at the same carrier concentration. Our results suggest that PbTe nanowires can be used for high-efficiency thermoelectric devices.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.2
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• pp.736-749
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• 2019

This paper employs computational tools to investigate the diffraction effects in regular head waves on the added resistance and wake on the propeller plane. The objective ships are a 66,000 DWT bulk carrier and a 3,600 TEU container ship. Fixed and free to heave and pitch conditions at design speed have been taken into account. Two-phase unsteady Reynolds averaged Navier-Stokes equations have been solved using the finite volume method; and a realizable k-ε model has been applied for the turbulent closure. The free surface is obtained by solving a VOF equation. The computations are carried out at the same scale of the model tests. Grid and numerical wave damping zones are applied to remove unwanted wave reflection at the boundaries. The computational results are analyzed using the Fourier series. The added resistances in waves at the free condition are higher than those at the fixed condition, which are nearly constant for all wavelengths. The wake velocity in waves is higher than that in calm water, and is accelerated where the wave crest locates on the propeller plane. When the vertical motion at the stern goes upward, the wake velocity also accelerated.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.161-161
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• 2023

Bias correction of values is a necessary step in downscaling coarse and systematically biased global climate models for use in local climate change impact studies. In addition to univariate bias correction methods, many multivariate methods which correct multiple variables jointly - each with their own mathematical designs - have been developed recently. While some literature have focused on the inter-comparison of these multivariate bias correction methods, none have focused extensively on the effect of diverse configurations (i.e., different combinations of input variables to be corrected) of climate variables, particularly high-dimensional ones, on the ability of the different methods to remove biases in uni- and multivariate statistics. This study evaluates the impact of three configurations (inter-variable, inter-spatial, and full dimensional dependence configurations) on four state-of-the-art multivariate bias correction methods in a national-scale domain over South Korea using a gridded approach. An inter-comparison framework evaluating the performance of the different combinations of configurations and bias correction methods in adjusting various climate variable statistics was created. Precipitation, maximum, and minimum temperatures were corrected across 306 high-resolution (0.2°) grid cells and were evaluated. Results show improvements in most methods in correcting various statistics when implementing high-dimensional configurations. However, some instabilities were observed, likely tied to the mathematical designs of the methods, informing that some multivariate bias correction methods are incompatible with high-dimensional configurations highlighting the potential for further improvements in the field, as well as the importance of proper selection of the correction method specific to the needs of the user.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.715-727
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• 2024

During fast neutron imaging, besides the dark current noise and readout noise of the CCD camera, the main noise in fast neutron imaging comes from high-energy gamma rays generated by neutron nuclear reactions in and around the experimental setup. These high-energy gamma rays result in the presence of high-density gamma white spots (GWS) in the fast neutron image. Due to the microscopic quantum characteristics of the neutron beam itself and environmental scattering effects, fast neutron images typically exhibit a mixture of Gaussian noise. Existing denoising methods in neutron images are difficult to handle when dealing with a mixture of GWS and Gaussian noise. Herein we put forward a deep learning approach based on the Swin Transformer UNet (SUNet) model to remove high-density GWS-Gaussian mixture noise from fast neutron images. The improved denoising model utilizes a customized loss function for training, which combines perceptual loss and mean squared error loss to avoid grid-like artifacts caused by using a single perceptual loss. To address the high cost of acquiring real fast neutron images, this study introduces Monte Carlo method to simulate noise data with GWS characteristics by computing the interaction between gamma rays and sensors based on the principle of GWS generation. Ultimately, the experimental scenarios involving simulated neutron noise images and real fast neutron images demonstrate that the proposed method not only improves the quality and signal-to-noise ratio of fast neutron images but also preserves the details of the original images during denoising.

• Geophysics and Geophysical Exploration
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• v.17 no.3
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• pp.137-146
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• 2014

This study presents a procedure for discrimination of artificial events from earthquakes occurred in and around the Korean Peninsula using data set in the Wonju KSRS seismograph network, Korea. Two training sets representing natural and artificial earthquakes were constructed with 150 and 56 events, respectively, with high signal to noise ratio. A frequency band, Pg(4-6 Hz)/Lg(5-7 Hz), which is optimal for the discrimination of seismic sources was derived from the two-dimensional grid of Pg/Lg spectral amplitude ratio. The corrections for the effects of earthquake magnitude and hypocentral distance were carried out for improvement of discrimination capability. For correcting the effect of magnitude dependence due to the inverse proportionality of corner frequency to seismic moment, the Brune's source spectrum was subtracted from the observation spectrum. The spectrum was corrected using the optimal damping coefficient to remove damping effect with the hypocentral distance. The effect of locally varying spectrum ratio was cancelled correcting variation of wave propagation along the ray path. The performance in discrimination between training sets of natural and artificial events was compared using the Mahalanobis distance in each step of correction. The procedure of magnitude, distance, and path corrections show clear improvements of the discrimination results with increasing Mahalanobis distance, from 1.98 to 3.01, between two training sets.

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.175-184
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• 2000

A nondestructive experimental feasibility study was conducted using magnetometer to find buried cultural objects at pottery and steel matters in low-relief mountaineous area of Doojeong-dong, Cheonan, Chungnam Province from May 23 to July 18, 1998. Magnetic survey was carried out with $20cm{\times}20cm$ grid in a site of $20m{\times}40m$ before excavation, and the distribution of magnetic anomalies was compared with the results of excavation. Magnetic sensor was located on the surface of ground during the magnetic survey on the basis of an experimental result. Positive magnetic anomalies of maximum 130 nT are found over a pair of potteries. Magnetic anomaly map reveals several anomalous points in the 1st and 4th quadrants of the survey site, from where potteries and their fragments were confirmed. Six points out of seven points cprrelated with magnetic anomaly are found contain earthwares, whereas a magnetically uncorrelated location produced earthware made of unbaked clay. Steel waste such as cans and wires hidden in soil and bushes also influenced magnetic anomalies. Therefore, it is better to remove such steel wastes prior to magnetic survey if possible. Some magnetically anomalous points produced no archaeological object on excavation. This may be explained by shallower level of excavation than burial depth.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.9
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• pp.217-224
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• 2019

Recently, sensor networks are built and used on many kinds of fields such as home, traffic, medical treatment and power grid. Sensing data manipulation on these fields could be a serious threat on property and safety. Thus, a proper way to block sensing data manipulation is necessary. In this paper, we propose IoT(Internet of Things) sensing data validation mechanism based on data obfuscation and variance analysis to remove manipulated sensing data effectively. IoT sensor device modulates sensing data with obfuscation function and sends it to a user. The user demodulates received data to use it. Fake data which are not modulated with proper obfuscation function show different variance aspect with valid data. Our proposed mechanism thus can detect fake data by analyzing data variance. Finally, we measured data validation time for performance analysis. As a result, block rate for false data was improved by up to 1.45 times compared with the existing technique and false alarm rate was 0.1~2.0%. In addition, the validation time on the low-power, low-performance IoT sensor device was measured. Compared to the RSA encryption method, which increased to 2.5969 seconds according to the increase of the data amount, the proposed method showed high validation efficiency as 0.0003 seconds.