• The Journal of Bigdata
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• v.4 no.2
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• pp.35-45
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• 2019

Recently, the damage with social cost of malicious comments is increasing. In addition to the news of talent committing suicide through the effects of malicious comments. The damage to malicious comments including abusive language and slang is increasing and spreading in various type and forms throughout society. In this paper, we propose a technique for detecting abusive language using a bi-directional long short-term memory neural network model. We collected comments on the web through the web crawler and processed the stopwords on unused words such as English Alphabet or special characters. For the stopwords processed comments, the bidirectional long short-term memory neural network model considering the front word and back word of sentences was used to determine and detect abusive language. In order to use the bi-directional long short-term memory neural network, the detected comments were subjected to morphological analysis and vectorization, and each word was labeled with abusive language. Experimental results showed a performance of 88.79% for a total of 9,288 comments screened and collected.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.313-319
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• 2023

In this paper, a time series machine learning model, Long Short Term Memory (LSTM), is applied into the bubble flow noise data and the underwater projectile launch noise data to predict missing values of time-series underwater noise data. The former is mixed with bubble noise, flow noise, and fluid-induced interaction noise measured in a pipe and can be classified into three types. The latter is the noise generated when an underwater projectile is ejected from a launch tube and has a characteristic of instantaenous noise. For such types of noise, a data-driven model can be more useful than an analytical model. We constructed an LSTM model with given data and evaluated the model's performance based on the number of hidden units, the number of input sequences, and the decimation factor of signal. It is shown that the optimal LSTM model works well for new data of the same type.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.142-148
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• 2023

In this paper, we identify performance issues in executing compute kernels from PolyBench, which includes compute kernels that are the core computational units of various data-intensive workloads, such as deep learning and data-intensive applications, on Processing-in-Memory (PIM) devices. Therefore, using our in-house simulator, we measured and compared the various performance metrics of workloads based on traditional out-of-order and in-order processors with Processing-in-Memory-based systems. As a result, the PIM-based system improves performance compared to other computing models due to the short-term data reuse characteristic of computational kernels from PolyBench. However, some kernels perform poorly in PIM-based systems without a multi-layer cache hierarchy due to some kernel's long-term data reuse characteristics. Hence, our evaluation and analysis results suggest that further research should consider dynamic and workload pattern adaptive approaches to overcome performance degradation from computational kernels with long-term data reuse characteristics and hidden data locality.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.577-594
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• 2002

For the estimation and test of long memory feature in volatilities of stock indices and individual companies semiparametric approach, Geweke and Porter-Hudak (1983), is employed. Empirical study supports the strong evidence of volatility persistence in Korean stock market. Most of indices and individual companies have the feature of long term dependence of volatility. Hence the short memory models are unable to explain the volatilities in Korean stock market.

• Sleep Medicine and Psychophysiology
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• v.7 no.1
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• pp.10-17
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• 2000

To investigate the neurobiological bases of learning and memory is one of the ambitious goals of modern neuroscience. The progress in this field of recent years has not only brought us closer to understanding the molecular mechanism underlying long-lasting changes in synaptic strength, but it has also provided further evidence that these mechanisms are required for memory formation. Since twenty years ago, several studies for the tests of the hypothesis that NMDA-dependent hippocampal long-term potentiation(LTP) underlies learning have been reported. Also, in the recent year, data from mutant mice showed that a potential role for NMDA-dependent LTP in hippocampal CA1 and spatial learning. Although the current evidence for the role of NMDA receptor in learning and memory is not still obvious, NMDA receptor seems to act as a critical switch for activation of a cascade of events that underlie synaptic plasticity.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1630-1643
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• 2023

The correct situation awareness (SA) of operators is important for managing nuclear power plants (NPPs), particularly in accident-related situations. Among the three levels of SA suggested by Ensley, Level 3 SA (i.e., projection of the future status of the situation) is challenging because of the complexity of NPPs as well as the uncertainty of accidents. Hence, several prediction methods using artificial intelligence techniques have been proposed to assist operators in accident prediction. However, these methods only predict short-term plant status (e.g., the status after a few minutes) and do not provide information regarding the uncertainty associated with the prediction. This paper proposes an algorithm that can predict the multivariate and long-term behavior of plant parameters for 2 h with 120 steps and provide the uncertainty of the prediction. The algorithm applies bidirectional long short-term memory and an attention mechanism, which enable the algorithm to predict the precise long-term trends of the parameters with high prediction accuracy. A conditional variational autoencoder was used to provide uncertainty information about the network prediction. The algorithm was trained, optimized, and validated using a compact nuclear simulator for a Westinghouse 900 MWe NPP.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.657-663
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• 2016

In this work, a novel silicon (Si) based floating body synaptic transistor (SFST) is studied to mimic the transition from short-term memory to long-term one in the biological system. The structure of the proposed SFST is based on an n-type metal-oxide-semiconductor field-effect transistor (MOSFET) with floating body and charge storage layer which provide the functions of short- and long-term memories, respectively. It has very similar characteristics with those of the biological memory system in the sense that the transition between short- and long-term memories is performed by the repetitive learning. Spike timing-dependent plasticity (STDP) characteristics are closely investigated for the SFST device. It has been found from the simulation results that the connectivity between pre- and post-synaptic neurons has strong dependence on the relative spike timing among electrical signals. In addition, the neuromorphic system having direct connection between the SFST devices and neuron circuits are designed.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.

• Journal of Digital Convergence
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• v.11 no.7
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• pp.297-304
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• 2013

This research explores the effect of TV news camerawork and viewers' involvement on memory of news through experiment. For this experimental research, 2 groups of subjects composed of university students were exposed to different types of TV news and responded to survey questions which were analysed by SPSS program. This research found that camerawork of TV news doesn't have an effect on short-term memory but on long-term memory. Though the fact viewers' involvement has a positive effect on shot-term and long-term memory was found, interactive effect of viewers' involvement and camerawork as an peripheral clue was not found.

• International Journal of Advanced Culture Technology
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• v.5 no.4
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• pp.57-62
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• 2017

In this paper, we applied long short term memory(LSTM) for classifying political polarity in cyber public sphere. The data collected from the cyber public sphere is transformed into word corpus data through word embedding. Based on this word corpus data, we train recurrent neural network (RNN) which is connected by LSTM's. Softmax function is applied at the output of the RNN. We conducted our proposed system to obtain experimental results, and we will enhance our proposed system by refining LSTM in our system.