• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.06a
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• pp.156-157
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• 2007

For tens of years many advantages of Phase-Change Random Access Memory(PRAM) were introduced. Although the performance improved gradually, there are some portions which must be improved. So, we studied new constitution of $Ge_1Se_1Te_2$ chalcogenide material to improve phase transition characteristic. Actually, the performance properties have been improved surprisingly. However, crystallization time was as long as ever for amorphization time. We conducted this experiment in order to solve that problem by doping-Sb.

• 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.

• Journal of Physiology & Pathology in Korean Medicine
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• v.20 no.2
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• pp.471-476
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• 2006

Chongmyung-tang(CMT, 聰明湯), oriental herbal medicine which consists of Polygaglae Radix(遠志), Acori Graminei Rhizoma(石菖蒲) and Hoelen(白茯神) has effect on amnesia, dementia. In order to evaluate effect of CMT on memory and learning in mice, CMT extract was used for studies. This paper describes the effects of CMT extract on memory and learning processes by using the passive and active avoidance performance tests, novel object recognition task and water maze task. The CMT extract ameliorated the memory retrieval deficit induced by ethanol in the passive avoidance responses but did not affect ambulatory activity of normal mice. These results suggest that CMT has an ameliorating effect on memory retrieval impairment. CMT extract decreased spontaneous motor activity(SMA) in the latter sessions of memory registration in active avoidance responses. These results suggest that CMT has partly transquilizing or antianxiety effects. In novel object recognition task to measure visual recognition memory, CMT-administered mice enhanced in long term memory for 1-3 days. In water maze task to measure spatial learning, which requires the activation of NMDA receptors in the hippocampus, spatial learning in CMT-administered mice was faster than in wild-type mice. These results suggest that CMT enhances memory and activates NMDA receptors.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.11a
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• pp.80-81
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• 2005

Chalcogenide phase change memory has high performance to be next generation memory, because it is a nonvolatile memory processing high programming speed, low programming voltage, high sensing margin, low consumption and long cycle duration. We have developed a sample of PRAM with thermal protected layer. We have investigated the phase transition behaviors in function of process factor including thermal protect layer. As a result, we have observed that set voltage and duration of protect layer are more improved than no protect layer.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.95-96
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• 1999

The authors have considered the 1/f fluctuation of vial rhythm with $1/f\beta$ spectrum of $\alpha$ wave in relation to the invigoration for the learning memory by paid their attention to the hippocampus protocol in this paper. At the first clinical experiment, the data of the remembrance test at short period is able to make as the foundation of the repeat memory. It can replace this memory with long period memory through the hippocampus by the superposition of the same memory-nerve circuits.

• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.51-65
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• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2612-2633
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• 2020

Due to the explosive growth of multimedia speech data, how to protect the privacy of speech data and how to efficiently retrieve speech data have become a hot spot for researchers in recent years. In this paper, we proposed an encrypted speech retrieval scheme based on long short-term memory (LSTM) neural network and deep hashing. This scheme not only achieves efficient retrieval of massive speech in cloud environment, but also effectively avoids the risk of sensitive information leakage. Firstly, a novel speech encryption algorithm based on 4D quadratic autonomous hyperchaotic system is proposed to realize the privacy and security of speech data in the cloud. Secondly, the integrated LSTM network model and deep hashing algorithm are used to extract high-level features of speech data. It is used to solve the high dimensional and temporality problems of speech data, and increase the retrieval efficiency and retrieval accuracy of the proposed scheme. Finally, the normalized Hamming distance algorithm is used to achieve matching. Compared with the existing algorithms, the proposed scheme has good discrimination and robustness and it has high recall, precision and retrieval efficiency under various content preserving operations. Meanwhile, the proposed speech encryption algorithm has high key space and can effectively resist exhaustive attacks.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.149-160
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• 2017

Sentiment analysis is the process of determining whether a piece of document, text or conversation is positive, negative, neural or other emotion. Sentiment analysis has been applied for several real-world applications, such as chatbot. In the last five years, the practical use of the chatbot has been prevailing in many field of industry. In the chatbot applications, to recognize the user emotion, sentiment analysis must be performed in advance in order to understand the intent of speakers. The specific emotion is more than describing positive or negative sentences. In light of this context, we propose deep learning models for conducting multi-class sentiment analysis for identifying speaker's emotion which is categorized to be joy, fear, guilt, sad, shame, disgust, and anger. Thus, we develop convolutional neural network (CNN), long short term memory (LSTM), and multi-layer neural network models, as deep neural networks models, for detecting emotion in a sentence. In addition, word embedding process was also applied in our research. In our experiments, we have found that long short term memory (LSTM) model performs best compared to convolutional neural networks and multi-layer neural networks. Moreover, we also show the practical applicability of the deep learning models to the sentiment analysis for chatbot.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.1053-1061
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• 2013

In this paper, we propose the time series forecasting models for internet traffic with long memory and heteroscedasticity. To control and forecast traffic volume, we first introduce the traffic forecasting models which are determined by the volatility and heteroscedasticity of the traffic. We then analyze and predict the heteroscedasticity and the long memory properties for forecasting traffic volume. Depending on the characteristics of the traffic, Fractional ARIMA model, Fractional ARIMA-GARCH model are applied and compared with the MAPE(Mean Absolute Percentage Error) Criterion.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1329-1330
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• 2007

Phase-change random access memory(PRAM) has many advantages compare with the existing memory. For example, fast programming speed, low programming voltage, high sensing margin, low power consume and long cyclability of read/write. Though it has many advantages, there are some points which must be improved. So, we invented and studied new constitution of $Ge_{1}Se_{1}Te_{2}$ chalcogenide material. Actually, the performance properties have been improved surprisingly. However, crystallization time was as long as ever for amorphization time. In this paper, we studied in order to make set operation time and reset operation voltage reduced. In the present work, by alloying Sb in $Ge_{1}Se_{1}Te_{2}$. we could confirm that improved its set operation time and reset operation voltage. As a result, the method of Sb-alloyed $Ge_{1}Se_{1}Te_{2}$ can be solution to decrease the set operation time and reset operation voltage.