• Journal of Radiation Protection and Research
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• v.12 no.1
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• pp.12-25
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• 1987

A basic multichannel analyser (MCA) system have been designed and constructed with the successive approximation type ADC (Analog to Digital Converter). Linear Gate, window, and palse stretcher consist of mainly linear and logic IC's, and are properly combined together to achieve short dead time and good linearity of the system. ADC 1211 (analysing time: $120{\mu}sec$) and S-RAM (static random acess memory) 6264 are used in ADC module. Two 6264 memories are connected in parallel in order to-provide enough counting capacity ($2^{16}-1$). Interfaced microcomputer Apple II controls this system and analizes the counted data. The system is tested by input pulses between 0V to 10V from oscillator.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2002.11a
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• pp.69-71
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• 2002

$(Bi, La)Ti_{3}O_{12}(BLT)$ ferroelectric thin films were prepared on $Al_{2}O_{3}/Si$ substrates by the sol-gel method. The as-coated films were post-annealed at the temperature of $650^{\circ}C$ and $700^{\circ}C$ for 30 min. The crystallinty, surface morphologies and electrical properties were affected by the annealing temperatures. The BLT films annealed at above $650^{\circ}C$ exhibited typical bismuth layered perovskite structures with (00$\ell$) preferred orientation. The granular shaped grains with a size of approximately 90nm was formed in the film sample annealed at $700^{\circ}C$. The memory window volatge of the BLT film was 2.5V. The leakage current of BLT films annealed at $650^{\circ}C$ was about $1\times10^{-7}A/\textrm{cm}^2$ at 3V.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.362-364
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• 2018

Predicting network traffic volume has become a popular topic recently due to its support in many situations such as detecting abnormal network activities and provisioning network services. Especially, predicting the volume of the next upcoming traffic from the series of observed recent traffic volume is an interesting and challenging problem. In past, various techniques are researched by using time series forecasting methods such as moving averaging and exponential smoothing. In this paper, we propose a long short-term memory neural network (LSTM) based network traffic volume prediction method. The proposed method employs the changing rate of observed traffic volume, the corresponding time window index, and a seasonality factor indicating the changing trend as input features, and predicts the upcoming network traffic. The experiment results with real datasets proves that our proposed method works better than other time series forecasting methods in predicting upcoming network traffic.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.07a
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• pp.71-74
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• 2000

Pt/Pb$TiO_3$/$SrTiO_3$/p-Si films were prepared by metallo-organic solution deposition(M0SD) method and investigated its structure and ferroelectric properties. Crystallinity of specimen as a funtions of post annealing temperature and the thickness of $SrTiO_3$(STO) buffer layer was studied using XRD and AFM. Based on C-V and P-E curve, $PbTiO_3$(PTO) capacitors showed good ferroelectric hysteresis arising from the polarization switching properties. When the thickness of ST0 buffer layer between PTO and Si substrate was 260 nrn and the post annealing temperature was $650^{\circ}C$, it was showed that production of the pyrochlore phase due to interdiffusion of Si into FTO was prevented. The dielectric constant of FTO thin films calculated from a maximum Cma in the accumulation region was 180 and the dielectric loss was 0.30 at 100 kHz frequency. The memory window in the C-V curve is 1.6V at a gate voltage of 5V.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.129-143
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• 2021

This paper proposes actuator fault detection and adaptive fault-tolerant control algorithms using performance index and human-like learning for longitudinal autonomous vehicles. Conventional longitudinal controller for autonomous driving consists of supervisory, upper level and lower level controllers. In this paper, feedback control law and PID control algorithm have been used for upper level and lower level controllers, respectively. For actuator fault-tolerant control, adaptive rule has been designed using the gradient descent method with estimated coefficients. In order to adjust the control parameter used for determination of adaptation gain, human-like learning algorithm has been designed based on perceptron learning method using control errors and control parameter. It is designed that the learning algorithm determines current control parameter by saving it in memory and updating based on the cost function-based gradient descent method. Based on the updated control parameter, the longitudinal acceleration has been computed adaptively using feedback law for actuator fault-tolerant control. The finite window-based performance index has been designed for detection and evaluation of actuator performance degradation using control error.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4664-4681
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• 2020

Modulation recognition (MR) plays a key role in cognitive radar, cognitive radio, and some other civilian and military fields. While existing methods can identify the signal modulation type by extracting the signal characteristics, the quality of feature extraction has a serious impact on the recognition results. In this paper, an end-to-end MR method based on long short-term memory (LSTM) and the gated recurrent unit (GRU) is put forward, which can directly predict the modulation type from a sampled signal. Additionally, the sliding window method is applied to fast-changing mixed-modulation signals for which the signal modulation type changes over time. The recognition accuracy on training datasets in different SNR ranges and the proportion of each modulation method in misclassified samples are analyzed, and it is found to be reasonable to select the evenly-distributed and full range of SNR data as the training data. With the improvement of the SNR, the recognition accuracy increases rapidly. When the length of the training dataset increases, the neural network recognition effect is better. The loss function value of the neural network decreases with the increase of the training dataset length, and then tends to be stable. Moreover, when the fast-changing period is less than 20ms, the error rate is as high as 50%. As the fast-changing period is increased to 30ms, the error rates of the GRU and LSTM neural networks are less than 5%.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.9-16
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• 2021

This study aims to develop a human activity recognition (HAR) system as a Deep-Learning (DL) classification model, distinguishing various human activities. We solely rely on the signals from a wristband accelerometer worn by a person for the user's convenience. 3-axis sequential acceleration signal data are gathered within a predefined time-window-slice, and they are used as input to the classification system. We are particularly interested in developing a Deep-Learning model that can outperform conventional machine learning classification performance. A total of 13 activities based on the laboratory experiments' data are used for the initial performance comparison. We have improved classification performance using the Convolutional Neural Network (CNN) combined with an auto-encoder feature reduction and parameter tuning. With various publically available HAR datasets, we could also achieve significant improvement in HAR classification. Our CNN model is also compared against Recurrent-Neural-Network(RNN) with Long Short-Term Memory(LSTM) to demonstrate its superiority. Noticeably, our model could distinguish both general activities and near-identical activities such as sitting down on the chair and floor, with almost perfect classification accuracy.

• Asia pacific journal of information systems
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• v.30 no.4
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• pp.719-740
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• 2020

The stock market changes continuously as new information emerges, affecting the judgments of investors. Online news articles are valued as a traditional window to inform investors about various information that affects the stock market. This paper proposed new ways to utilize online news articles with technical indicators. The suggested hybrid model consists of three models. First, a self-attention-based convolutional neural network (CNN) model, considered to be better in interpreting the semantics of long texts, uses news content as inputs. Second, a self-attention-based, bi-long short-term memory (bi-LSTM) neural network model for short texts utilizes news titles as inputs. Third, a bi-LSTM model, considered to be better in analyzing context information and time-series models, uses 19 technical indicators as inputs. We used news articles from the previous day and technical indicators from the past seven days to predict the share price of the next day. An experiment was performed with Korean stock market data and news articles from 33 top companies over three years. Through this experiment, our proposed model showed better performance than previous approaches, which have mainly focused on news titles. This paper demonstrated that news titles and content should be treated in different ways for superior stock price prediction.

• Journal of the Korean Vacuum Society
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• v.9 no.4
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• pp.315-320
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• 2000

We have investigated the crystal structure and electrical properties of Pt/SBT/$ZrO_2$/Si (MFIS) and Pt/SBT/Si (MFS) structures for the gate oxide of ferroelectric memory. XRD spectra and SEM showed that the SBT film of SBT/$ZrO_2$/Si structure had larger grain than that of SBT/Si structure. $ZrO_2$ film between SBT film and Si substrate is confirmed as a good candidate for a diffusion barrier by the analysis of AES. The remanent polarization decreased and coercive voltage increased in Pt/SBT/$ZrO_2$/Pt/$SiO_2$/Si structure. This effect may increase memory window of MFIS structure directly related to the coercive voltage. From the capacitance-volt-age characteristics, the memory windows of Pt/SBT (210 nm)/$ZrO_2$ (28 nm)/Si structure were in the range of 1~l.5 V at the applied voltage of 4~6 V. The current densities of Pt/SBT/ZrO$_2$/Si with as -deposited Pt electrode and annealed at $800^{\circ}C$ in $O_2$ambient were $8\times10^{-8} A/\textrm{cm}^2$ and $4\times10^{-8}A/\textrm{cm}^2$ , respectively.

• The Korean Journal of Archival Studies
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• no.39
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• pp.101-138
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• 2014

This study examined the influence and meaning of SNS as the hot topic of our time from the archival perspective and also studied the 'SNS records management'. The many users mean a high accessibility and utilization of SNS, which increase the influence and value of SNS as a record. Politically, SNS is a tool that strengthens the communication among the voters, politicians and the public while economically, it is a window to accept the complaints of the customers and a marketing tool. In addition, the voices of social minorities are also recorded unlike in the traditional media, which makes the SNS record a method to gain the social variety and diversity. SNS is a place of formation of collective memory and collective memory itself. Furthermore, it can play the role of public sphere. It also is a place for generation of 'big data' in an archival sense. In addition, this study has classified the SNS records management into primary and secondary management that include record management entities, subjects, periods, methods, and causes. This study analyzed the history, status, and the meaning of SNS to assess the values and meanings as the preliminary study for the future SNS record management studies.