• Journal of Ocean Engineering and Technology
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• v.35 no.5
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• pp.336-346
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• 2021

The prediction of wave conditions is crucial in the field of marine and ocean engineering. Hence, this study aims to predict the significant wave height through machine learning (ML), a soft computing method. The adopted metocean data, collected from 2012 to 2020, were obtained from the Korea Institute of Ocean Science and Technology. We adopted the feedforward neural network (FNN) and long-short term memory (LSTM) models to predict significant wave height. Input parameters for the input layer were selected by Pearson correlation coefficients. To obtain the optimized hyperparameter, we conducted a sensitivity study on the window size, node, layer, and activation function. Finally, the significant wave height was predicted using the FNN and LSTM models, by varying the three input parameters and three window sizes. Accordingly, FNN (W48) (i.e., FNN with window size 48) and LSTM (W48) (i.e., LSTM with window size 48) were superior outcomes. The most suitable model for predicting the significant wave height was FNN(W48) owing to its accuracy and calculation time. If the metocean data were further accumulated, the accuracy of the ML model would have improved, and it will be beneficial to predict added resistance by waves when conducting a sea trial test.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.23-28
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• 2021

Data analysis research using deep learning has recently been studied in various field. In this paper, we conduct a GNSS (Global Navigation Satellite System)-based meteorological study applying deep learning by estimating the ZWD (Zenith tropospheric Wet Delay) through MLP (Multi-Layer Perceptron) and LSTM (Long Short-Term Memory) models. Deep learning models were trained with meteorological data and ZWD which is estimated using zenith tropospheric total delay and dry delay. We apply meteorological data not used for learning to the learned model to estimate ZWD with centimeter-level RMSE (Root Mean Square Error) in both models. It is necessary to analyze the GNSS data from coastal areas together and increase time resolution in order to estimate ZWD in various situations.

• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.157-166
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• 2021

Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.

• Convergence Security Journal
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• v.23 no.5
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• pp.65-72
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• 2023

This paper proposes a method for detecting malicious domains considering human habitual characteristics by building a Deep Learning model based on LSTM (Long Short-Term Memory). DGA (Domain Generation Algorithm) malicious domains exploit human habitual errors, resulting in severe security threats. The objective is to swiftly and accurately respond to changes in malicious domains and their evasion techniques through typosquatting to minimize security threats. The LSTM-based Deep Learning model automatically analyzes and categorizes generated domains as malicious or benign based on malware-specific features. As a result of evaluating the model's performance based on ROC curve and AUC accuracy, it demonstrated 99.21% superior detection accuracy. Not only can this model detect malicious domains in real-time, but it also holds potential applications across various cyber security domains. This paper proposes and explores a novel approach aimed at safeguarding users and fostering a secure cyber environment against cyber attacks.

• Journal of the Korean Solar Energy Society
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• v.39 no.5
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• pp.41-52
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• 2019

The purpose of the work is to develop a simple solar irradiance prediction model using a deep learning method, the LSTM (long term short term memory). Other than existing prediction models, the proposed one uses only the cloudiness among the information forecasted from the national meterological forecast center. The future cloudiness is generally announced with four categories and for three-hour intervals. In this work, a daily irradiance pattern is used as an input vector to the LSTM together with that cloudiness information. The proposed model showed an error of 5% for learning and 30% for prediction. This level of error has lower influence on the load prediction in typical building cases.

• Electronics and Telecommunications Trends
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• v.34 no.2
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• pp.40-50
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• 2019

Considerable accuracy improvements in deep learning have recently been achieved in many applications that require large amounts of computation and expensive memory. However, recent advanced techniques for compacting and accelerating the deep learning model have been developed for deployment in lightweight devices with constrained resources. Lightweight deep learning techniques can be categorized into two schemes: lightweight deep learning algorithms (model simplification and efficient convolutional filters) in nature and transferring models into compact/small ones (model compression and knowledge distillation). In this report, we briefly summarize various lightweight deep learning techniques and possible research directions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3855-3867
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• 2022

Name separation (splitting full names into surnames and given names) is not a tedious task in a multiethnic country because the procedure for splitting surnames and given names is ethnicity-specific. Malaysia has multiple main ethnic groups; therefore, separating Malaysian full names into surnames and given names proves a challenge. In this study, we develop a two-phase framework for Malaysian name separation using deep learning. In the initial phase, we predict the ethnicity of full names. We propose a recurrent neural network with long short-term memory network-based model with character embeddings for prediction. Based on the predicted ethnicity, we use a rule-based algorithm for splitting full names into surnames and given names in the second phase. We evaluate the performance of the proposed model against various machine learning models and demonstrate that it outperforms them by an average of 9%. Moreover, transfer learning and fine-tuning of the proposed model with an additional dataset results in an improvement of up to 7% on average.

• The Korean Journal of Physiology and Pharmacology
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• v.21 no.6
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• pp.579-589
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• 2017

Anesthetics are used extensively in surgeries and related procedures to prevent pain. However, there is some concern regarding neuronal degeneration and cognitive deficits arising from regular anesthetic exposure. Recent studies have indicated that brain-derived neurotrophic factor (BDNF) and cyclic AMP response element-binding protein (CREB) are involved in learning and memory processes. Genistein, a plant-derived isoflavone, has been shown to exhibit neuroprotective effects. The present study was performed to examine the protective effect of genistein against isoflurane-induced neurotoxicity in rats. Neonatal rats were exposed to isoflurane (0.75%, 6 hours) on postnatal day 7 (P7). Separate groups of rat pups were orally administered genistein at doses of 20, 40, or 80 mg/kg body weight from P3 to P15 and then exposed to isoflurane anesthesia on P7. Neuronal apoptosis was detected by TUNEL assay and FluoroJade B staining following isoflurane exposure. Genistein significantly reduced apoptosis in the hippocampus, reduced the expression of proapoptotic factors (Bad, Bax, and cleaved caspase-3), and increased the expression of Bcl-2 and Bcl-xL. RT-PCR analysis revealed enhanced BDNF and TrkB mRNA levels. Genistein effectively upregulated cAMP levels and phosphorylation of CREB and TrkB, leading to activation of cAMP/CREB-BDNF-TrkB signaling. PI3K/Akt signaling was also significantly activated. Genistein administration improved general behavior and enhanced learning and memory in the rats. These observations suggest that genistein exerts neuroprotective effects by suppressing isoflurane-induced neuronal apoptosis and by activating cAMP/CREB-BDNF-TrkB-PI3/Akt signaling.

• The Korean Journal of Rehabilitation Nursing
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• v.5 no.2
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• pp.205-216
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• 2002

This quasi-experimental study was done to test the effects of meta-memory enhancing program for elderly women. Data were collected 12 to 30, August 2002 from 34elderly women over 65 years living in Busan city. Subjects were 15 of experimental group and 19 of control group. The metamemory enhancing program was developed by five sessions composing of 1.5-2.0 hours one session. In experiment group, this program was performed for three weeks, twice per week. The degrees of four memory performance tasks were measured using instrument of Elderly Verbal Learning Test(Choi Kyung Mi, 1988) and Face Recognition Instrument(Min Hye Sook, 1999) and the metamemory were measured using MIA questionnaire(Dixon et al., 1988). Research results are as following. 1. After participating in five times memory training programs, experimental group has the significant increase of metamemory in comparison with control group.(t=59.58, p< 0.0001). In particular, the concepts of strategy(t=20.44, p< 0.0001), achievement (t=21.94, p< 0.0001), and locus degree (t=59.58, p< 0.0001) among sub-concepts of the metamemory are increasing significantly. 2. After participating in five time memory training programs, the degree of immediate word recall(t=17.25, p< 0.0001) and face recognition(t=16.69, p< 0.0001) among four memory tasks in experimental group are increasing significantly compared with those measures of control group. Considering this results, this metamemory enhancing program was found as an effective nursing program for metamemory improvement of elderly women's memory.

• Toxicological Research
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• v.33 no.2
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• pp.165-171
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• 2017

Bisphenol A (BPA) is a monomer used in a polymerization reaction in the production of polycarbonate plastics. It has been used in many consumer products, including plastics, polyvinyl chloride, food packaging, dental sealants, and thermal receipts. However, there is little information available on the inhalation toxicity of BPA. Therefore, the aim of this study was to determine its inhalation toxicity and effects on the estrous cycle, spatial learning, and memory. Sprague-Dawley rats were exposed to 0, 10, 30, and $90mg/m^3$ BPA, 6 hr/day, 5 days/week for 8 weeks via whole-body inhalation. Mortality, clinical signs, body weight, hematology, serum chemistry, estrous cycle parameters, performance in the Morris water maze test, and organ weights, as well as gross and histopathological findings, were compared between the control and BPA exposure groups. Statistically significant changes were observed in serum chemistry and organ weights upon exposure to BPA. However, there was no BPA-related toxic effect on the body weight, food consumption, hematology, serum chemistry, organ weights, estrous cycle, performance in the Morris water maze test, or gross or histopathological lesions in any male or female rats in the BPA exposure groups. In conclusion, the results of this study suggested that the no observable adverse effect level (NOAEL) for BPA in rats is above $90mg/m^3$/6 hr/day, 5 days/week upon 8-week exposure. Furthermore, BPA did not affect the estrous cycle, spatial learning, or memory in rats.