• 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.

• International Journal of Industrial Entomology and Biomaterials
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• v.15 no.1
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• pp.39-45
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• 2007

Extracts of adult worker bodies of Vespa simillima in 2 % NaCl or acidified methanol were administered orally to mice for 70 days. Following this period, memory at one-day and one-month periods, and the effects on scopolamine-induced amnesia were examined using a step-through passive avoidance task. Changes in MK-801-induced disability after 8 days of training, and in memory one month after the trial were also assessed. Mice treated with the 2% NaCl extract showed significant improvement in memory in the behavioral tests one month after the trial, whereas mice receiving the extract in acidified methanol, did not differ from the controls in any trial. The results inidicate that Vespa simillima contains substances acting favorably on the cerebral functions of mammals.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07a
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• pp.567-570
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• 2005

Most researches on monostable LCD and bistable LCD have separately been carried out. We introduce a novel liquid crystal display mode which can be operated as both memory mode and dynamic mode. The novel LCD mode has not only a long term memory time of memory mode but also a fast response time of dynamic mode. We describe switching characteristics of dual mode. Electro-optical characteristics of memory mode and dynamic mode are unique and show the possibility of device application.

• Advances in Traditional Medicine
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• v.7 no.2
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• pp.182-190
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• 2007

Alzheimer's disease is a neurodegenerative disorder associated with a decline in cognitive abilities. Dementia is one of the aged related mental problems and a characteristic symptom of Alzheimer's disease. Nootropic agents like piracetam and cholinesterase inhibitors like $Donepezil^{\circledR}$ are used in situations where there is organic disorder in learning abilities, but the resulting side-effects associated with these agents have limited their utility. Foeniculum (F.) vulgare Linn. is widely used in Indian traditional systems of medicines and also as a house remedy for nervous debility. The present work was undertaken to assess the potential of F. vulgare as a nootropic and anti-cholinesterase agent in mice. Exteroceptive behavioral models such as Elevated plus maze and Passive avoidance paradigm were employed to assess short term and long term memory in mice. To delineate the possible mechanism through which F. vulgare elicits the anti-amnesic effects, its influence on central cholinergic activity was studied by estimating the whole brain acetylcholinesterase activity. Pretreatment of methanolic extract of fruits of F. vulgare Linn. for 8 successive days, ameliorated the amnesic effect of scopolamine (0.4 mg/kg) and aging induced memory deficits in mice. F. vulgare extract significantly decreased transfer latencies of young mice and aged mice, increased step down latency and exhibited significant anti-acetyl cholinesterase effects, when compared to piracetam, scopolamine and control groups of mice. F. vulgare might prove to be a useful memory restorative agent in the treatment of dementia seen in the elderly.

• Korean Journal of Cognitive Science
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• v.30 no.4
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• pp.239-268
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• 2019

Since last several decades, iconic memory has been accepted theoretically valid for its role of the first storage mechanism in visual memory process. However, there have been relatively less interests in iconic memory among researchers than their interests in visual short- and long-term memory. Such little interests seem to arise from a lack of detailed understandings of theories and methodologies about iconic memory and visual persistence. This study aimed to achieve the understandings by reviewing theories and empirical studies of iconic memory and visual persistence. The study further discussed future direction of iconic memory research according to the essential aspects of phenomenological and ecological criticisms against the validity of iconic memory.