• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.1-7
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• 2020

With the increase in cyber attacks, automated IDS using machine learning is being studied. According to recent research, the IDS using the recursive learning model shows high detection performance. However, the simple application of the recursive model may be difficult to reflect the associated session characteristics, as the overlapping session environment may degrade the performance. In this paper, we designed the session management module and applied it to LSTM (Long Short-Term Memory) recursive model. For the experiment, the CSE-CIC-IDS 2018 dataset is used and increased the normal session ratio to reduce the association of mal-session. The results show that the proposed model is able to maintain high detection performance even in the environment where session relevance is difficult to find.

• Wind and Structures
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• v.38 no.2
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• pp.147-160
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• 2024

The aerodynamic force is a significant component that influences the stability and safety of structures. It has unstable properties and depends on computer precision, making its long-term prediction challenging. Accurately estimating the aerodynamic traits of structures is critical for structural design and vibration control. This paper establishes an unsteady aerodynamic time series prediction model using Long Short-Term Memory (LSTM) network. The unsteady aerodynamic force under varied Reynolds number and angles of attack is predicted by the LSTM model. The input of the model is the aerodynamic coefficients of the 1 to n sample points and output is the aerodynamic coefficients of the n+1 sample point. The model is predicted by interpolation and extrapolation utilizing Unsteady Reynolds-average Navier-Stokes (URANS) simulation data of flow around a circular cylinder, square cylinder and airfoil. The results illustrate that the trajectories of the LSTM prediction results and URANS outcomes are largely consistent with time. The mean relative error between the forecast results and the original results is less than 6%. Therefore, our technique has a prospective application in unsteady aerodynamic force prediction of structures and can give technical assistance for engineering applications.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.527-540
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• 2012

We examined the effect of delayed and immediate rewards on short- and long-term memory performance depending on the level of stress. It has been demonstrated that delaying feedback during memory tasks could lead to better retention than presenting it immediately (a.k.a., feedback delay benefit or delay-retention effect). In this study, we manipulated stress level(high-stress or low-stress), reward-timing(delayed or immediate reward), reward-existence(500 or 0 won) and retrieval-timing(delayed or immediate memory test). On the high-stress learning condition, one week later, the number of correct answers with delayed-rewards were significantly more than that of delayed-no-rewards but there was not any difference between immediate-rewards and immediate-no-rewards. On the other hand, in the high-stressful immediate memory test, immediate-rewards only had a positive effect on memory performance. The results indicated that delayed rewards improved long-term memory performance by promoting memory consolidation and the sensitivity to rewards was higher under the high-stress condition.

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.83-89
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• 2017

In this paper, we propose an extended method of one-hot Long Short-Term Memory (LSTM) and evaluate the performance on spam filtering task. Most of traditional methods proposed for spam filtering task use word occurrences to represent spam or non-spam messages and all syntactic and semantic information are ignored. Major issue appears when both spam and non-spam messages share many common words and noise words. Therefore, it becomes challenging to the system to filter correct labels between spam and non-spam. Unlike previous studies on information filtering task, instead of using only word occurrence and word context as in probabilistic models, we apply a neural network-based approach to train the system filter for a better performance. In addition to one-hot representation, using term weight with attention mechanism allows classifier to focus on potential words which most likely appear in spam and non-spam collection. As a result, we obtained some improvement over the performances of the previous methods. We find out using region embedding and pooling features on the top of LSTM along with attention mechanism allows system to explore a better document representation for filtering task in general.

• Journal of Life Science
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• v.29 no.5
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• pp.564-569
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• 2019

Heavy drinking disrupts the nervous system by activation of GABA receptors and inhibition of glutamate receptors, thereby preventing short-term memory formation. Degradation of cognition by alcohol induces blackouts, and it can lead to alcoholic dementia if repeated. Therefore, drugs need to be developed to prevent alcohol-induced blackout. In this study, we confirmed the effect of an ethanol extract of Cassia obtusifolia seeds (COE) on alcohol-induced memory impairment. The effects of COE and ethanol on cognitive functions mice were examined using the passive avoidance and Y-maze tests. The manner in which alcohol affects long-term potentiation (LTP) in relation to the learning and memory was confirmed by electrophysiology performed on mouse hippocampal slices. We also measured N-methyl-D-aspartate (NMDA) receptor-mediated field excitatory synapses (fEPSPs), which have a known association with cognitive impairment caused by ethanol. Ethanol caused memory impairments in passive avoidance and Y-maze tests. COE prevented these ethanol-induced memory impairments in these tests. Ethanol also blocked LTP induction in the mouse hippocampus, and COE prevented this ethanol-induced LTP deficit. Ethanol decreased NMDA receptor-mediated fEPSPs in the mouse hippocampus, and this decrease was prevented by COE. These results suggest that COE might be useful in preventing alcohol-induced neurological dysfunctions, including blackouts.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.55 no.8
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• pp.399-403
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• 2006

An accurate constant output voltage provided by the analog memory cell may be used by the low power oscillator to generate an accurate low frequency output signal. This accurate low frequency output signal may be used to maintain long-term timing accuracy in host devices during sleep modes of operation when an external crystal is not available to provide a clock signal. Further, incorporation of the analog memory cell in the low power oscillator is fully implementable in a 0.35um Samsung standard CMOS process. Therefore, the analog memory cell incorporated into the low power oscillator avoids the previous problems in a oscillator by providing a temperature-stable, low power consumption, size-efficient method for generating an accurate reference clock signal that can be used to support long sleep mode operation.

• International journal of advanced smart convergence
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• v.7 no.3
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• pp.1-7
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• 2018

This study relates to a composition for improvement of memory or prevention and treatment of cognitive impairment using waste resources rich in beneficial substances. This study makes good effects to inhibit the activity of acetylcholinesterase in brain tissue and to improve the cognitive functions in a simulation model of cognitive impairment induced by scopolamine, so it can be available in the promotion of memory and the prevention and treatment of cognitive impairment. The composition uses the extract of fruit peels, which have long been used without causing toxicity in a wide range of food applications; therefore, it can be used safely without a risk of side effects, even in the case of a long-term administration for the preventive purpose. Furthermore, this research is a very beneficial invention in the environment-friendly aspect in association with the recycling of resources, as it is based on the novel efficacies of fruit peels, which have been conventionally disposed as a refuse of fruits due to their poor sensory qualities despite the content of beneficial substances.

• Molecules and Cells
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• v.39 no.7
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• pp.515-523
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• 2016

Long-term immunity to many viral and bacterial pathogens requires$ CD8^+$ memory T cell development, and the induction of long-lasting$ CD8^+$ memory T cells from a $na{\ddot{i}}ve$, undifferentiated state is a major goal of vaccine design. Formation of the memory$ CD8^+$ T cell compartment is highly dependent on the early activation cues received by $na{\ddot{i}ve}$ $CD8^+$ T cells during primary infection. This review aims to highlight the cellularity of various niches within the lymph node and emphasize recent evidence suggesting that distinct types of T cell activation and differentiation occur within different immune contexts in lymphoid organs.

• Biomolecules & Therapeutics
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• v.28 no.6
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• pp.512-518
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• 2020

Stroke is a leading cause of long-term disability in ischemic survivors who are suffering from motor, cognitive, and memory impairment. Previously, we have reported suppressing LPA₅ activity with its specific antagonist can attenuate acute brain injuries after ischemic stroke. However, it is unclear whether suppressing LPA₅ activity can also attenuate chronic brain injuries after ischemic stroke. Here, we explored whether effects of LPA₅ antagonist, TCLPA5, could persist a longer time after brain ischemic stroke using a mouse model challenged with tMCAO. TCLPA5 was administered to mice every day for 3 days, starting from the time immediately after reperfusion. TCLPA5 administration improved neurological function up to 21 days after tMCAO challenge. It also reduced brain tissue loss and cell apoptosis in mice at 21 days after tMCAO challenge. Such long-term neuroprotection of TCLPA5 was associated with enhanced neurogenesis and angiogenesis in post-ischemic brain, along with upregulated expression levels of vascular endothelial growth factor. Collectively, results of the current study indicates that suppressing LPA₅ activity can provide long-term neuroprotection to mice with brain ischemic stroke.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.