• Restorative Dentistry and Endodontics
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• v.38 no.3
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• pp.146-153
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• 2013

Objectives: Aluminum step wedge (ASW) equivalent radiodensity (eRD) has been used to quantify restorative material's radiodensity. The aim of this study was to evaluate the effects of image acquisition control (IAC) of a digital X-ray system on the radiodensity quantification under different exposure time settings. Materials and Methods: Three 1-mm thick restorative material samples with various opacities were prepared. Samples were radiographed alongside an ASW using one of three digital radiographic modes (linear mapping (L), nonlinear mapping (N), and nonlinear mapping and automatic exposure control activated (E)) under 3 exposure time settings (underexposure, normal-exposure, and overexposure). The ASW eRD of restorative materials, attenuation coefficients and contrasts of ASW, and the correlation coefficient of linear relationship between logarithms of gray-scale value and thicknesses of ASW were compared under 9 conditions. Results: The ASW eRD measurements of restorative materials by three digital radiographic modes were statistically different (p = 0.049) but clinically similar. The relationship between logarithms of background corrected grey scale value and thickness of ASW was highly linear but attenuation coefficients and contrasts varied significantly among 3 radiographic modes. Varying exposure times did not affect ASW eRD significantly. Conclusions: Even though different digital radiographic modes induced large variation on attenuation of coefficient and contrast of ASW, E mode improved diagnostic quality of the image significantly under the underexposure condition by improving contrasts, while maintaining ASW eRDs of restorative materials similar. Under the condition of this study, underexposure time may be acceptable clinically with digital X-ray system using automatic gain control that reduces radiation exposure for patient.

• 한국노년학
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• v.30 no.1
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• pp.81-91
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• 2010

The purpose of this study was to test a validity and reliability of Cognitive Performance Scale(CPS), a cognitive measure generated from 5 items(comatose status, decision making, short-term memory, making self understood, and eating). Method: 393 patients in 2 hospitals for the elderly with dementia were measured with CPS by two nurses independently. The inter-rater agreement was tested by comparing two scores. The CPS score was compared with GDS, which was measured by doctors and nurses, and MMSE score which was drawn from the claim data of Health Insurance Review & Assessment Service. Result: The correlation coefficient between CPS and GDS was 0.742(p<0.0001), CPS and MMSE was -0.794(p<0.0001). The Cronbach's coefficient alpha of CPS was 0.742, Kappa value was 0.772~1.000. The CPS showed high validity and reliability in long term care hospitals of Korea.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.5
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• pp.619-628
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• 2010

Shape Memory Alloy (SMA) has recently received attention in developing implantable surgical equipments and it is expected to lead the future medical device market by adequately imitating surgeons' flexible and delicate hand movement. However, SMA actuators have not been used widely because of their nonlinear behavior called hysteresis, which makes their control difficult. Hence, we propose a parameter, $t_1$, which is necessary for temperature control, by analyzing the open-loop step response between current and temperature and by comparing it with the values of linear differential equations. $t_1$ is a pole of the transfer function in the invariant linear model in which the input and output are current and temperature, respectively; hence, $t_1$ is found to be related to the state variable used for temperature control. When considering the parameter under heat treatment conditions, $T_{max}$ was found to assume the lowest value, and $t_1$ was irrelevant to the heat treatment.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.273-283
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• 2020

Quantitative forecasting of groundwater levels for the assessment of groundwater variation and vulnerability is very important. To achieve this purpose, various time series analysis and machine learning techniques have been used. In this study, we developed a prediction model based on LSTM (Long short term memory), one of the artificial neural network (ANN) algorithms, for predicting the daily groundwater level of 11 groundwater wells in Hankyung-myeon, Jeju Island. In general, the groundwater level in Jeju Island is highly autocorrelated with tides and reflected the effects of precipitation. In order to construct an input and output variables based on the characteristics of addressing data, the precipitation data of the corresponding period was added to the groundwater level data. The LSTM neural network was trained using the initial 365-day data showing the four seasons and the remaining data were used for verification to evaluate the fitness of the predictive model. The model was developed using Keras, a Python-based deep learning framework, and the NVIDIA CUDA architecture was implemented to enhance the learning speed. As a result of learning and verifying the groundwater level variation using the LSTM neural network, the coefficient of determination (R²) was 0.98 on average, indicating that the predictive model developed was very accurate.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1061-1069
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• 2021

Recently, the magnitude and frequency of extreme heavy rains and localized heavy rains have increased due to abnormal climate, which caused increased flood damage in river basin. As a result, the nonlinearity of the hydrological system of rivers or basins is increasing, and there is a limitation in that the lead time is insufficient to predict the water level using the existing physical-based hydrological model. This study predicted the water level at Ulsan (Taehwagyo) with a lead time of 0, 1, 2, 3, 6, 12 hours by applying deep learning techniques based on Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) and evaluated the prediction accuracy. As a result, DNN model using the sliding window concept showed the highest accuracy with a correlation coefficient of 0.97 and RMSE of 0.82 m. If deep learning-based water level prediction using a DNN model is performed in the future, high prediction accuracy and sufficient lead time can be secured than water level prediction using existing physical-based hydrological models.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.4
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• pp.128-134
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• 2022

Nakdong river estuary is being operated with the goal of expanding the period of seawater inflow from this year to 2022 every month and creating a brackish water area within 15 km of the upstream of the river bank. In this study, the deep learning algorithm Long Short-Term Memory (LSTM) was applied to predict the salinity of the Nakdong Bridge (about 5 km upstream of the river bank) for the purpose of rapid decision making for the target brackish water zone and prevention of salt water damage. Input data were constructed to reflect the temporal and spatial characteristics of the Nakdong River estuary, such as the amount of discharge from Changnyeong and Hamanbo, and an optimal model was constructed in consideration of the hydraulic characteristics of the Nakdong River Estuary by changing the degree according to the sequence length. For prediction accuracy, statistical analysis was performed using the coefficient of determination (R-squred) and RMSE (root mean square error). When the sequence length was 12, the R-squred 0.997 and RMSE 0.122 were the highest, and the prior prediction time showed a high degree of R-squred 0.93 or more until the 12-hour interval.

• Structural Monitoring and Maintenance
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• v.7 no.2
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• pp.109-124
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• 2020

Benefiting from the massive monitoring data collected by the Structural health monitoring (SHM) system, scholars can grasp the complex environmental effects and structural state during structure operation. However, the monitoring data is often missing due to sensor faults and other reasons. It is necessary to study the recovery method of missing monitoring data. Taking the structural temperature monitoring data of Nanjing Dashengguan Yangtze River Bridge as an example, the long short-term memory (LSTM) network-based recovery method for missing structural temperature data is proposed in this paper. Firstly, the prediction results of temperature data using LSTM network, support vector machine (SVM), and wavelet neural network (WNN) are compared to verify the accuracy advantage of LSTM network in predicting time series data (such as structural temperature). Secondly, the application of LSTM network in the recovery of missing structural temperature data is discussed in detail. The results show that: the LSTM network can effectively recover the missing structural temperature data; incorporating more intact sensor data as input will further improve the recovery effect of missing data; selecting the sensor data which has a higher correlation coefficient with the data we want to recover as the input can achieve higher accuracy.

• Proceedings of the KIEE Conference
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• 1998.07e
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• pp.1788-1790
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• 1998

The dielectric layers in AC plasma display panel(AC PDP) are essential to the discharge cell structure, because they protect metal electrodes from sputtering by positive ion bombarding in discharge plasma and form a sheath of wall charges which are essential to memory function of AC PDP. This layer should have high dielectric strength and also be transparent because the luminance of PDP is strongly correlated this layer. In this paper, we discussed the dielectric strength and transparency of the dielectric layer under various conditions. As a result, on the $15{\mu}m$ thickness, the minimum dielectric strength was $29V/{\mu}m$ and the transmittance coefficient was about 80% after $570^{\circ}C$ firing process. It can be proposed that the resonable dielectric thickness in AC PDP is $15{\mu}m$ because it has about 80V margin on the maximum applied voltage.

• Journal of Korean Neurosurgical Society
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• v.46 no.6
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• pp.588-591
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• 2009

Radiologic findings of Bing-Neel syndrome, which is an extremely uncommon complication resulting from malignant lymphocyte infiltration into the central nervous system (CNS) in patients with Waldenstr$\ddot{o}$m's macroglobulinemia (WM), have been infrequently reported due to extreme rarity of the case. A 75-year-old man with WM presented at a neurology clinic with progressive gait and memory disturbances, and dysarthria of 2 months duration. Cerebrospinal fluid and serum protein electrophoresis and immunofixation electrophoresis showed IgM kappa-type monoclonal gammopathy. Brain magnetic resonance imaging revealed multifocal, hyperintense lesions on T2 weighted-images. Brain diffusion-weighted imaging (DWI) demonstrated hyperintensities in cerebral and cerebellar lesions that appeared isointense on apparent diffusion coefficient maps, which were compatible with vasogenic edema. Although histologic analysis is a confirmative study to prove direct cell infiltration into the brain, brain MRI with DWI may be a good supportive study to diagnose Bing-Neel syndrome.

• Journal of Broadcast Engineering
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• v.6 no.2
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• pp.187-194
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• 2001

The Advanced Intra Coding(AIC) mode included in the H.263+ which is for video ceding at low bit rates, selects one prediction direction of DCT coefficients among three directions for every intra macroblock. Based ()n the selected direction, It uses a different DCT scanning order, quantization scheme, and VLC table. The use of the AIC mode brings higher compression ratio and improved PSNR than the H.263 baseline. This paper proposes a simplified submode selection method for a direction of the DCT coefficient prediction for advanced ultra coding in H.263. Compared to the conventional method the proposed simplified method can achieve a similar coding Performance at computational complexity reduced to 1/7 and with much less memory requirement. The proposed method will be useful in implementing a simplified H.263 system.