• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.123-123
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• 2020

The present study is aimed to correcting radar-based mean areal precipitation forecasts to improve urban flood predictions and uncertainty analysis of water levels contributed at each stage in the process. For this reason, a long short-term memory (LSTM) network is used to reproduce three-hour mean areal precipitation (MAP) forecasts from the quantitative precipitation forecasts (QPFs) of the McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE). The Gangnam urban catchment located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 24 heavy rainfall events, 22 grid points from the MAPLE system and the observed MAP values estimated from five ground rain gauges of KMA Automatic Weather System. The corrected MAP forecasts were input into the developed coupled 1D/2D model to predict water levels and relevant inundation areas. The results indicate the viability of the proposed framework for generating three-hour MAP forecasts and urban flooding predictions. For the analysis uncertainty contributions of the source related to the process, the Bayesian Markov Chain Monte Carlo (MCMC) using delayed rejection and adaptive metropolis algorithm is applied. For this purpose, the uncertainty contributions of the stages such as QPE input, QPF MAP source LSTM-corrected source, and MAP input and the coupled model is discussed.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1283-1293
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• 2022

The purpose of this study is to predict residual chlorine in order to maintain stable residual chlorine concentration in sedimentation basin by using artificial intelligence algorithms in water treatment process employing pre-chlorination. Available water quantity and quality data are collected and analyzed statistically to apply into mathematical multiple regression and artificial intelligence models including multi-layer perceptron neural network, random forest, long short term memory (LSTM) algorithms. Water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage data are used as the input parameters to develop prediction models. As results, it is presented that the random forest algorithm shows the most moderate prediction result among four cases, which are long short term memory, multi-layer perceptron, multiple regression including random forest. Especially, it is result that the multiple regression model can not represent the residual chlorine with the input parameters which varies independently with seasonal change, numerical scale and dimension difference between quantity and quality. For this reason, random forest model is more appropriate for predict water qualities than other algorithms, which is classified into decision tree type algorithm. Also, it is expected that real time prediction by artificial intelligence models can play role of the stable operation of residual chlorine in water treatment plant including pre-chlorination process.

• Wind and Structures
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• v.38 no.1
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• The Korean Journal of Physiology and Pharmacology
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• v.18 no.6
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• pp.457-460
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• 2014

At central synapses, activity-dependent synaptic plasticity has a crucial role in information processing, storage, learning, and memory under both physiological and pathological conditions. One widely accepted model of learning mechanism and information processing in the brain is Hebbian Plasticity: long-term potentiation (LTP) and long-term depression (LTD). LTP and LTD are respectively activity-dependent enhancement and reduction in the efficacy of the synapses, which are rapid and synapse-specific processes. A number of recent studies have a strong focal point on the critical importance of another distinct form of synaptic plasticity, non-Hebbian plasticity. Non-Hebbian plasticity dynamically adjusts synaptic strength to maintain stability. This process may be very slow and occur cell-widely. By putting them all together, this mini review defines an important conceptual difference between Hebbian and non-Hebbian plasticity.

• Korean Medical Education Review
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• v.12 no.2
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• pp.27-33
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• 2010

The purpose of this study was to review the definition of cognitive load (CL), the relationship between CL and instructional design, and to provide a viewpoint of CL in curriculum and instructional design in medical education. Cognitive load theory (CLT) makes use of three hypotheses about the structure of human memory: working memory (WM) is limited in terms of the amount of information it can hold, in contrast with WM, long term memory is assumed to have no limits and organizes information as schemata. CL indicates the mental load on the limitation of WM. CLT has been used to design instructional interventions that help to ease the learning process. Extraneous CL is related to irrelevant instructional interventions, while intrinsic CL is the complexity of the information itself. Germane CL is the cognitive process for acquiring schema formation. It is a necessary CL to achieve deeper comprehension and solve problems. The range of medical education includes complex, multifaceted and knowledge-rich domains with clinical skills and attitudes. Therefore, CLT may be used to guide instructional design in medical education in terms of decreasing extraneous CL, adjusting intrinsic CL and enhancing the germane CL.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.15 no.10
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• pp.831-837
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• 2002

Scaled SONOS transistors have been fabricated by 0.35$\mu\textrm{m}$ CMOS standard logic process. The thickness of stacked ONO(blocking oxide, memory nitride, tunnel oxide) gate insulators measured by TEM are 2.5 nm, 4.0 nm and 2.4 nm, respectively. The SONOS memories have shown low programming voltages of ${\pm}$8.5 V and long-term retention of 10-year Even after 2 ${\times}$ 10$\^$5/ program/erase cycles, the leakage current of unselected transistor in the erased state was low enough that there was no error in read operation and we could distinguish the programmed state from the erased states precisely The tight distribution of the threshold voltages in the programmed and the erased states could remove complex verifying process caused by over-erase in floating gate flash memory, which is one of the main advantages of the charge-trap type devices. A single power supply operation of 3 V and a high endurance of 1${\times}$10$\^$6/ cycles can be realized by the programming method for a flash-erased type EEPROM.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.735-744
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• 2009

In this paper, we introduce a fast approach for Bayesian detection of change points in long-memory processes. Since a heavy computation is needed to evaluate the likelihood function of long-memory processes, a method for simplifying the computational process is required to efficiently implement a Bayesian inference. Instead of estimating the parameter, we consider selecting a element from the set of possible parameters obtained by categorizing the parameter space. This approach simplifies the detection algorithm and reduces the computational time to detect change points. Since the parameter space is (0, 0.5), there is no big difference between the result of parameter estimation and selection under a proper fractionation of the parameter space. The analysis of Nile river data showed the validation of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1649-1654
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• 2021

In this paper, we introduce a regularization of long short-term memory (LSTM) based fall detection system using TensorFlow that can detect falls that can occur in the elderly. Fall detection uses data from a 3-axis acceleration sensor attached to the body of an elderly person and learns about a total of 7 behavior patterns, each of which is a pattern that occurs in daily life, and the remaining 3 are patterns for falls. During training, a normalization process is performed to effectively reduce the loss function, and the normalization performs a maximum-minimum normalization for data and a L2 regularization for the loss function. The optimal regularization conditions of LSTM using several falling parameters obtained from the 3-axis accelerometer is explained. When normalization and regularization rate λ for sum vector magnitude (SVM) are 127 and 0.00015, respectively, the best sensitivity, specificity, and accuracy are 98.4, 94.8, and 96.9%, respectively.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.427-427
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• 2012

Ferroelectric-gate field effect transistor based memory using a nanowire as a conducting channel offers exceptional advantages over conventional memory devices, like small cell size, low-voltage operation, low power consumption, fast programming/erase speed and non-volatility. We successfully fabricated ferroelectric nonvolatile memory devices using both n-type and p-type Si nanowires coated with organic ferroelectric poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)] via a low temperature fabrication process. The devices performance was carefully characterized in terms of their electrical transport, retention time and endurance test. Our p-type Si NW ferroelectric memory devices exhibit excellent memory characteristics with a large modulation in channel conductance between ON and OFF states exceeding $10^5$; long retention time of over $5{\times}10^4$ sec and high endurance of over 105 programming cycles while maintaining ON/OFF ratio higher $10^3$. This result offers a viable way to fabricate a high performance high-density nonvolatile memory device using a low temperature fabrication processing technique, which makes it suitable for flexible electronics.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.1
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• pp.39-49
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• 2011

Turbo code needs long decoding time because of iterative decoding. To communicate with high speed, we have to shorten decoding time and it is possible with parallel process. But memory contention can cause from parallel process, and it reduces performance of decoder. To avoid memory contention, QPP interleaver is proposed in 2006. In this paper, we propose MDF method which is fit to QPP interleaver, and has relatively short decoding time and reduced logic. And introduce the design of MAP decode module using MDF method. Designed decoder is targetted to FPGA of Xilinx, and its throughput is 80Mbps maximum.