• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.127-137
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• 2024

Physical models, which are often used for water resource management, are difficult to build and operate with input data and may involve the subjective views of users. In recent years, research using data-driven models such as machine learning has been actively conducted to compensate for these problems in the field of water resources, and in this study, an artificial neural network was used to simulate long-term rainfall runoff in the Osipcheon watershed in Samcheok-si, Gangwon-do. For this purpose, three input data groups (meteorological observations, daily precipitation and potential evapotranspiration, and daily precipitation - potential evapotranspiration) were constructed from meteorological data, and the results of training the LSTM (Long Short-term Memory) artificial neural network model were compared and analyzed. As a result, the performance of LSTM-Model 1 using only meteorological observations was the highest, and six LSTM-MLP ensemble models with MLP artificial neural networks were built to simulate long-term runoff in the Fifty Thousand Watershed. The comparison between the LSTM and LSTM-MLP models showed that both models had generally similar results, but the MAE, MSE, and RMSE of LSTM-MLP were reduced compared to LSTM, especially in the low-flow part. As the results of LSTM-MLP show an improvement in the low-flow part, it is judged that in the future, in addition to the LSTM-MLP model, various ensemble models such as CNN can be used to build physical models and create sulfur curves in large basins that take a long time to run and unmeasured basins that lack input data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5580-5593
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• 2019

A malicious Uniform Resource Locator (URL) recognition and detection method based on the combination of Attention mechanism with Convolutional Neural Network and Long Short-Term Memory Network (Attention-Based CNN-LSTM), is proposed. Firstly, the WHOIS check method is used to extract and filter features, including the URL texture information, the URL string statistical information of attributes and the WHOIS information, and the features are subsequently encoded and pre-processed followed by inputting them to the constructed Convolutional Neural Network (CNN) convolution layer to extract local features. Secondly, in accordance with the weights from the Attention mechanism, the generated local features are input into the Long-Short Term Memory (LSTM) model, and subsequently pooled to calculate the global features of the URLs. Finally, the URLs are detected and classified by the SoftMax function using global features. The results demonstrate that compared with the existing methods, the Attention-based CNN-LSTM mechanism has higher accuracy for malicious URL detection.

• 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.

• Clinical and Experimental Pediatrics
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• v.57 no.3
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• pp.101-109
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• 2014

The use of glucocorticoids (GCs) in the perinatal period is suspected of being associated with adverse effects on long-term neurodevelopmental outcomes for preterm infants. Repeated administration of antenatal GCs to mothers at risk of preterm birth may adversely affect fetal growth and head circumference. Fetal exposure to excess GCs during critical periods of brain development may profoundly modify the limbic system (primarily the hippocampus), resulting in long-term effects on cognition, behavior, memory, co-ordination of the autonomic nervous system, and regulation of the endocrine system later in adult life. Postnatal GC treatment for chronic lung disease in premature infants, particularly involving the use of dexamethasone, has been shown to induce neurodevelopmental impairment and increases the risk of cerebral palsy. In contrast to studies involving postnatal dexamethasone, long-term follow-up studies for hydrocortisone therapy have not revealed adverse effects on neurodevelopmental outcomes. In experimental studies on animals, GCs has been shown to impair neurogenesis, and induce neuronal apoptosis in the immature brains of newborn animals. A recent study has demonstrated that dexamethasone-induced hypomyelination may result from the apoptotic degeneration of oligodendrocyte progenitors in the immature brain. Thus, based on clinical and experimental studies, there is enough evidence to advice caution regarding the use of GCs in the perinatal period; and moreover, the potential long-term effects of GCs on brain development need to be determined.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.183-191
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• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.

• Journal of Oriental Neuropsychiatry
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• v.9 no.2
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• pp.19-35
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• 1998

Some oriental medicine turned out to have a significant clinical effect on the cure for dementia. Therefore, thorough scientific tests for physiological effect of oriental medicne are needed. This study is aimed at doing experimental studies on the effects of two medicines, Jowiseungcheongtang and hyungbangjihwangtang, on the cure for dementia.For the demonstration of the effect of the two medicines on aged rats, we perfomed a radial arm medicines on aged rats, water maze task, known for their proper learning paradigm for behavior.Previous studies on aging and dementia show that aged rats displayed significant impariments in the learning of the radial arm maze task compared with younger rats. As in experiment 1, we found that the learning of the radial arm maze task compared with younger rats. As in experiment 1, we found that the learning deficits aged rats exhibit in radial arm maze task were improved with the application of each medicine. The resutls suggest that these two medicine can be effective to patients whose working or shortterm memory is impaired. In experiment 2 we studied the effect of the two medicines on the deficit of the aged rats with the Morris water maze task known for measuring long-tern memory. We did not find significant results between the performance of the ages rats and the younger ones. Considered the different results previous studies have reported, more thorough studies are needed to investigate the effect of the medicines on long-term memory.In conclusion, the results we found in experiment 1 and 2 suggest that Jowisengcheongtang and hyungbangjihwangtang can have useful effects for the cure of age-related memory (especially for short-term memory)deficits. Recent interests in dementia urges researchers concerned to explore the effect of oriental medicine on the disease. As there have been relatively few behavioral or scientific studies on dementia using oriental medicine to date, further studies are expected are expected to continue to elucidate 'what the wisdom of the oriental medicine tells about dementia'.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.63
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• pp.13-21
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• 2001

The present study compared the two experimental methods on inquiring the image structure of odors: Presenting a stimulus is one, and not presenting a stimulus is the other. For experiment one, five odors were presented, and the subjects were instructed to evaluate the odors on 7-point scale for each of the 25 adjectives. For experiment two, no odor was presented, and the subjects were instructed to perform the pair-wise comparisons for the each pair of two adjectives on their similarities on 7-point scale. The data from the two experiments were analysed and compared using MDS(Multi-Dimensional Scaling), Correlation, Cluster Analysis. The results showed that there was no structural differences between two experimental methods in term of the Image structure of odors. But, minor disparity was found between two methods in terms of density of distribution of the adjectives. It was construed that the difference came from the difference of the memory that was used for each of the experiments; that is, short term memory for experiment one and long-term memory for experiment two.

• Proceedings of the Korea Society of Design Studies Conference
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• 2004.05a
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• pp.304-305
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• 2004

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.69-81
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• 2019

We consider bivariate long range dependent (LRD) time series forecasting using a deep learning method. A long short-term memory (LSTM) network well-suited to time series data is applied to forecast bivariate time series; in addition, we compare the forecasting performance with bivariate fractional autoregressive integrated moving average (FARIMA) models. Out-of-sample forecasting errors are compared with various performance measures for functional MRI (fMRI) data and daily realized volatility data. The results show a subtle difference in the predicted values of the FIVARMA model and VARFIMA model. LSTM is computationally demanding due to hyper-parameter selection, but is more stable and the forecasting performance is competitively good to that of parametric long range dependent time series models.