• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.85-90
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• 2022

In this paper, an asynchronous nonvolatile memory module using a self-diagnosis function was designed. For the system to work, a lot of data must be input/output, and memory that can be stored is required. The volatile memory is fast, but data is erased without power, and the nonvolatile memory is slow, but data can be stored semi-permanently without power. The non-volatile static random-access memory is designed to solve these memory problems. However, the non-volatile static random-access memory is weak external noise or electrical shock, data can be some error. To solve these data errors, self-diagnosis algorithms were applied to non-volatile static random-access memory using error correction code, cyclic redundancy check 32 and data check sum to increase the reliability and accuracy of data retention. In addition, the possibility of application to an asynchronous non-volatile storage system requiring reliability was suggested.

• Korean Journal of Child Studies
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• v.24 no.2
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• pp.1-14
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• 2003

In this test for the effect of misinformation and mental reinstatement on accuracy of recall in children, misinformation or neutral informations was presented to each of 80 five- and 80 nine - year - old children(Total : 160). Two days later they were asked to recall original information in one of two conditions; free recall or mental reinstatement. For 5-year-old children, mental reinstatement enhanced memory performance and increased the accuracy despite the presentation of misinformation. For 9-year-old children, there was no significant difference between free recall and mental reinstatement condition. For younger children, mental reinstatement may be an effective way of enhancing memory performance.

• ETRI Journal
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• v.44 no.3
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• pp.438-449
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• 2022

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject-predicate-object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.23-30
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• 2021

Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, unsupervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.113-123
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• 2006

Flash memory has been increasingly used in handhold devices not only for data storage, but also for code storage. Because NAND flash memory only provides sequential access feature, a traditionally accepted solution to execute the program from NAND flash memory is shadowing. But, shadowing has significant drawbacks increasing a booting time of the system and consuming severe DRAM space. Demand paging has obtained significant attention for program execution from NAND flash memory. But. one of the issues is that there has been no effort to bound demand paging cost in flash memory and to analyze the worst case performance of demand paging. For the worst case timing analysis of programs running from NAND flash memory. the worst case demand paging costs should be estimated. In this paper, we propose two different WCRT analysis methods considering demand paging costs, DP-Pessimistic and DP-Accurate, depending on the accuracy and the complexity of analysis. Also, we compare the accuracy butween DP-Pessimistic and DP-Accurate by using the simulation.

• Science of Emotion and Sensibility
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• v.14 no.4
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• pp.605-616
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• 2011

The present study examined memory accuracy for faces with positive, negative and neutral emotional expressions to test whether their emotional content can affect visual working memory (VWM) performance. Participants remembered a set of face pictures in which facial expressions of the faces were randomly assigned from pleasant, unpleasant and neutral emotional categories. Participants' task was to report presence or absence of an emotion change in the faces by comparing the remembered set against another set of test faces displayed after a short delay. The change detection accuracies of the pleasant, unpleasant and neutral face conditions were compared under two memory exposure duration of 500ms vs. 1000ms. Under the duration of 500ms, the accuracy in the pleasant condition was higher than both unpleasant and neutral conditions. However the difference disappeared when the duration was extended to 1000ms. The results indicate that a positive facial expression can improve VWM accuracy relative to the negative or positive expressions especially when there is not enough time for forming durable VWM representations.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.73-78
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• 2021

Recently, the number of people who visit the hospital due to diabetes is increasing. According to the Korean Diabetes Association, it is statistically indicated that one in seven adults aged 30 years or older in Korea suffers from diabetes, and it is expected to be more if the pre-diabetes, fasting blood sugar disorders, are combined. In the last study, the validity of Triglyceride and Cholesterol associated with diabetes was confirmed and analyzed using Random Forest. Random Forest has a disadvantage that as the amount of data increases, it uses more memory and slows down the speed. Therefore, in this paper, we compared and analyzed Random Forest and XGBoost, focusing on improvement of learning speed and prevention of memory waste, which are mainly dealt with in machine learning. Using XGBoost, the problem of slowing down and wasting memory was solved, and the accuracy of the diabetes recognition factor was further increased.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.11
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• pp.1254-1264
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• 2008

This paper suggests an extended memory polynomial model that improves accuracy in modeling memory effects of RF power amplifiers(PAs), and verifies effectiveness of the suggested method. The extended memory polynomial model includes cross-terms that are products of input terms that have different delay values to improve the limited accuracy of basic memory polynomial model that includes the diagonal terms of Volterra kernels. The complexity of the memoryless model, memory polynomial model, and the suggested model are compared. The extended memory polynomial model is represented with a matrix equation, and the Volterra kernels are extracted using least square method. In addition, the structure of digital predistorter and digital signal processing(DSP) algorithm based on the suggested model and indirect learning method are proposed to implement a digital predistortion linearization. To verify the suggested model, the predicted output of the model is compared with the measured output for a 10W GaN HEMT RF PA and 30 W LDMOS RF PA using 2.3 GHz WiBro input signal, and adjacent-channel power ratio(ACPR) performance with the proposed digital predistortion is measured. The proposed model increases model accuracy for the PAs, and improves the linearization performance by reducing ACPR.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.10
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• pp.131-136
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• 2007

In this paper, we estimated the effects of different color stimulation on the cognitive function of human quantitatively. For the stimulations we used color lights with 6 color filters such as red, yellow, green, blue, violet and white. The experiment was performed in a soundproof chamber. 50 young male and female subjects were participated in the experiment. To find the appropriate color cognitive function, the endogenous visuospatial attention task(EVAT) and one back working memory task(OWMT) were performed. The reaction time and accuracy degree were measured. The results showed that the reaction time of EVAT was the fastest and the accuracy degree of attention task was the highest in green environment. The reaction time of OWMT was the fastest in yellow and the accuracy degree of memory task was the highest in blue. For physiological parameters, we measured electrocardiogram(ECG) and HRV spectrum analysis, HF/LF color environment. These results can be used as an indicator in the design of color environment and clinical applications.

• ETRI Journal
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• v.44 no.4
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• pp.672-685
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• 2022

This article proposes a subject-independent application of brain-computer interfacing (BCI). A 32-channel Electroencephalography (EEG) device is used to measure imagined speech (SI) of four words (sos, stop, medicine, washroom) and one phrase (come-here) across 13 subjects. A deep long short-term memory (LSTM) network has been adopted to recognize the above signals in seven EEG frequency bands individually in nine major regions of the brain. The results show a maximum accuracy of 73.56% and a network prediction time (NPT) of 0.14 s which are superior to other state-of-the-art techniques in the literature. Our analysis reveals that the alpha band can recognize SI better than other EEG frequencies. To reinforce our findings, the above work has been compared by models based on the gated recurrent unit (GRU), convolutional neural network (CNN), and six conventional classifiers. The results show that the LSTM model has 46.86% more average accuracy in the alpha band and 74.54% less average NPT than CNN. The maximum accuracy of GRU was 8.34% less than the LSTM network. Deep networks performed better than traditional classifiers.