• Journal of the Korean earth science society
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• v.30 no.3
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• pp.294-304
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• 2009

A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.

• Korean Journal of Remote Sensing
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• v.17 no.3
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• pp.231-242
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• 2001

In 2-pass differential SAR interferometry(DInSAR), the topographic phase signature can be removed by using a digital elevation model(DEM) to isolate the contribution of deformation from interferometric phase. This method has an advantage of no unwrapping process, but applicability is limited by precision of the DEM used. The residual phase in 2-pass differential interferogram accounts for error of DEM used in the processing provided that no actual deformation exits. The objective of this paper is a preliminary study to improve DEM precision using low precision DEM and 2-pass DInSAR technique, and we applied the 2-pass DInSAR technique to Asan area. ERS-1/2 tandem complex images and DTED level 0 DEM were used for DInSAR, and the precision of resulting DEM was estimated by a 1:25,000 digital map. The input DEM can be improved by simply adding the DInSAR output to the original low precision DEM. The absolute altitude error of the improved DEM is 9.7m, which is about the half to that of the original DTED level 0 data. And absolute altitude error of the improved DEM is better than that from InSAR technique, 15.8m. This approach has an advantage over the InSAR technique in efficiently reducing layover effects over steep slope region. This study demonstrates that 2-pass DInSAR can also be used to improve DEM precision.

• Journal of Korean Ophthalmic Optics Society
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• v.19 no.4
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• pp.551-555
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• 2014

Purpose: This study is to investigate if the improvement of visual sensory (VS) by amblyopia treatment affects the ocular functions in refractive errors, accommodative errors and phoria at distance and near. Methods: 10 subjects (17 eyes, mean age of $10.7{\pm}2.9$ years) who treated amblyopia completely, were participated for this study. Refractive errors, accommodative errors, and distance and near phoria were compared between before and after treatments of amblyopia. Refractive errors and accommodative errors at 40 cm were measured using openfield auto-refractor (NVision-5001, Shin Nippon, Japan) and using monocular estimated method (MEM) respectively. Phoria was determined at 3 m for distance and at 40 cm for near using Howell phoria card, cover test or Maddox rod. Results: Mean corrected visual acuity (CVA) significantly increased from $0.46{\pm}0.11$ (decimal notation) for before amblyopia treatment to a level of $1.03{\pm}0.13$ for after amblyopia treatment (p < 0.001). For spherical refractive error, hyperopia significantly decreased from $+2.29{\pm}0.86D$ to a level of $+1.1{\pm}2.38D$ (p < 0.05) but astigmatism did not significantly change; $-1.80{\pm}1.41D$ for before treatment and $-1.65{\pm}1.30D$D for after treatment (p > 0.05). Accommodative error significantly decreased from accommodative lag of $+1.1{\pm}0.75D$ to a level of $+0.5{\pm}0.59D$ (accommodative lag) (p < 0.05). Distance phoria significantly changed from eso $2.9{\pm}6.17PD$ (prism diopters) to a level of eso $0.2{\pm}3.49PD$ (p < 0.05), and near phoria also significantly changed from eso $0.4{\pm}2.32PD$ to level of exo $2{\pm}4.9PD$ (p < 0.05). There was a high correlation (r = 0.88, p < 0.001) between improvement of visual acuity and decrease of accommodative lag. Conclusions: Hyperopic refractive error decreased with improvement of CVA or VS by amblyopia treatment. And the improvement of VS by amblyopia treatment also improved accommodative error, and changed phoria coupled with accommodation.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.1043-1050
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• 2011

This paper presents practical results for the estimation of vibration level inside a powertrain based on the rigid body theory and measurement. The vibration level of inside powertrain has been used for the calculation of excitation force of an engine indirectly. However it was difficult to estimate or measure the vibration level inside of a powertrain when a powertrain works on the driving condition of a vehicle. To do this work, the rigid body theory is employed. At the first, the vibration on the surface of a powertrain is measured and its results are secondly used for the estimation the vibration level inside of powertrain together with rigid body theory. Also did research on how to decrease the error rate when the rigid body theory is applied. This method is successfully applied to the estimation of the vibration level on arbitrary point of powertrain on the driving condition at the road.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9C
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• pp.880-886
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• 2009

Recently, the memory is used in many electronic devices, thus, the many researchers make a study of the memory. To increase a storage capacity per memory block, the researchers study for reducing the fabrication process of memory and multi-level cell memory which is storing more than 2-bits in a cell. However, the multi-level cell memory has low bit-error rates by various noises. In this paper, we study the noise of multi-level cell memory, and we propose the channel model of multi-level cell memory.

• ETRI Journal
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• v.36 no.6
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• pp.924-930
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• 2014

This paper presents a $0.13{\mu}m$ CMOS 3-level envelope delta-sigma modulation (EDSM) RF signal generator, which synthesizes a 2.6 GHz-centered fully symmetrical 3-level EDSM signal for high-efficiency power amplifier architectures. It consists of an I-Q phase modulator, a Class B wideband buffer, an up-conversion mixer, a D2S, and a Class AB wideband drive amplifier. To preserve fast phase transition in the 3-state envelope level, the wideband buffer has an RLC load and the driver amplifier uses a second-order BPF as its load to provide enough bandwidth. To achieve an accurate 3-state envelope level in the up-mixer output, the LO bias level is optimized. The I-Q phase modulator adopts a modified quadrature passive mixer topology and mitigates the I-Q crosstalk problem using a 50% duty cycle in LO clocks. The fabricated chip provides an average output power of -1.5 dBm and an error vector magnitude (EVM) of 3.89% for 3GPP LTE 64 QAM input signals with a channel bandwidth of 10/20 MHz, as well as consuming 60 mW for both channels from a 1.2 V/2.5 V supply voltage.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.723-730
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• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2663-2674
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• 2014

For cognitive radio sensor networks (CRSNs) that use underlay-based spectrum access, the location of the primary user (PU) plays an important role in the power control of the secondary users (SUs), because the SUs must keep the minimum interference level required by the PU. Received signal strength (RSS)-based localization schemes provide low-cost implementation and low complexity, thus it is suitable for the PU localization in CRSNs. However, the RSS-based localization schemes have a high localization error because they use an inexact path loss exponent (PLE). Thus, applying a RSS-based localization scheme into the PU localization would cause a high interference to the PU. In order to reduce the localization error and improve the channel reuse rate, we propose a RSS-based PU localization scheme that uses distance calibration for CRSNs using underlay model-based spectrum access. Through the simulation results, it is shown that the proposed scheme can provide less localization error as well as more spectrum utilization than the RSS-based PU localization using the mean and the maximum likelihood calibration.

• Physical Therapy Korea
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• v.23 no.2
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• pp.93-99
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• 2016

Background: Parametric statistical procedures are typically conducted under the condition in which a sample distribution is statistically identical with its population. In reality, investigators use inferential statistics to estimate parameters based on the sample drawn because population distributions are unknown. The uncertainty of limited data from the sample such as lack of sample size may be a challenge in most rehabilitation studies. Objects: The purpose of this study is to review the bootstrapping method to overcome shortcomings of limited sample size in rehabilitation studies. Methods: Articles were reviewed. Results: Bootstrapping method is a statistical procedure that permits the iterative re-sampling with replacement from a sample when the population distribution is unknown. This statistical procedure is to enhance the representativeness of the population being studied and to determine estimates of the parameters when sample size are too limited to generalize the study outcome to target population. The bootstrapping method would overcome limitations such as type II error resulting from small sample sizes. An application on a typical data of a study represented how to deal with challenges of estimating a parameter from small sample size and enhance the uncertainty with optimal confidence intervals and levels. Conclusion: Bootstrapping method may be an effective statistical procedure reducing the standard error of population parameters under the condition requiring both acceptable confidence intervals and confidence level (i.e., p=.05).