• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.4
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• pp.139-146
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• 2006

A Signcryption proposed by Yuliang Zheng in 1997 is a hybrid public key primitive that combines a digital signature and a encryption. It provides more efficient method than a straightforward composition of an signature scheme with a encryption scheme. In a mobile communication environment, the authenticated key agreement protocol should be designed to have lower computational complexity and memory requirements. The password-based authenticated key exchange protocol is to authenticate a client and a server using an easily memorable password. This paper proposes an secure Authenticated Key Exchange protocol using Signcryption scheme. In Addition we also show that it is secure and a more efficient that other exiting authenticated key exchange protocol.

• 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.

• Earthquakes and Structures
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• v.25 no.5
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• pp.343-358
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• 2023

The superelastic viscous damper (SVD) is a hybrid passive control device comprising a viscoelastic damper and shape memory alloy (SMA) cables connected in series. The SVD is an innovative damper through which a large amount of seismic energy can dissipate. The current study assessed the seismic collapse induced by steel moment-resisting frames (SMRFs) equipped with SVDs and compared them with the performance of special MRFs and buckling restrained brace frames (BRBFs). For this purpose, nonlinear dynamic and incremental dynamic analysis (IDA) were conducted in OpenSees software. Both 5- and 9-story special MRFs, BRBFs, and MRFs equipped with the SVDs were examined. The results indicated that the annual exceedance rate for maximum residual drifts of 0.2% and 0.5% for the BRBFs and MRFs with SVDs, respectively, were considerably less than for SMRFs with reduced-beam section (RBS) connections and that the seismic performances of these structures were enhanced with the use of the BRB and SVD. The probability of collapse due to residual drift in the SVD, BRB, and RBS frames in the 9-story structure was 1.45, 1.75, and 1.05 times greater than for the 5-story frame.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• 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 Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1747-1754
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• 2016

This paper presents a high accuracy and fast hybrid on-chip temperature sensor. The proposed temperature sensor combines a SAR type temperature sensor with a ${\Sigma}{\Delta}$ type temperature sensor. The SAR type temperature sensor has fast temperature searching time but it has more error than the ${\Sigma}{\Delta}$ type temperature sensor. The ${\Sigma}{\Delta}$ type temperature sensor is accurate but it is slower than the SAR type temperature sensor. The proposed temperature sensor uses both the SAR and ${\Sigma}{\Delta}$ type temperature sensors, so that the proposed temperature sensor has high accuracy and fast temperature searching. Also, the proposed temperature sensor includes a temperature error compensating circuit by storing the temperature errors in a memory circuit after chip fabrication. The proposed temperature sensor was fabricated in 3.3V CMOS $0.35{\mu}m$ process. Its temperature resolution, power consumption, and area are $0.15^{\circ}C$, $540{\mu}W$, and $1.2mm^2$, respectively.

• Journal of Life Science
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• v.19 no.8
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• pp.1055-1061
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• 2009

${\alpha}$-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA) receptors are widespread throughout the central nervous system and appear to serve as synaptic receptors for fast excitatory synaptic transmission mediated by glutamate. Their modulation is believed to affect learning and memory. To identify the interaction proteins for the AMPA receptor subunit glutamate receptor-interacting protein 1 (GRIPl), GRIP1 interactions with armadillo family protein p0071/plakophilin-4 were investigated. GRIP1 protein bound to the tail region of p0071/plakophilin-4 but not to other armadillo family protein members in a yeast two-hybrid assay. The "S-X-V" motif at the carboxyl (C)-terminal end of p0071/plakophilin-4 is essential for interaction with GRIP1. p0071/plakophilin-4 interacted with the Postsynaptic density-95/Discs large/Zona occludens-1 (PDZ) domains of GRIPI in the yeast two-hybrid assay, as is indicated also by Glutathione S-transferase (GST) pull-down, and co-immunoprecipitated with GRIP1 antibody in brain fraction. The findings of this study provide evidence that p0071/plakophilin-4 is an interactor of GRIP1.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.183-184
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• 2013

Two main MBE growth techniques have been used: plasma-assisted MBE (PA-MBE), which utilizes a rf plasma to supply active nitrogen, and ammonia MBE, in which nitrogen is supplied by pyrolysis of NH3 on the sample surface during growth. PA-MBE is typically performed under metal-rich growth conditions, which results in the formation of gallium droplets on the sample surface and a narrow range of conditions for optimal growth. In contrast, high-quality GaN films can be grown by ammonia MBE under an excess nitrogen flux, which in principle should result in improved device uniformity due to the elimination of droplets and wider range of stable growth conditions. A drawback of ammonia MBE, on the other hand, is a serious memory effect of NH3 condensed on the cryo-panels and the vicinity of heaters, which ruins the control of critical growth stages, i.e. the native oxide desorption and the surface reconstruction, and the accurate control of V/III ratio, especially in the initial stage of seed layer growth. In this paper, we demonstrate that the reliable and reproducible growth of GaN on Si (110) substrates is successfully achieved by combining two MBE growth technologies using rf plasma and ammonia and setting a proper growth protocol. Samples were grown in a MBE system equipped with both a nitrogen rf plasma source (SVT) and an ammonia source. The ammonia gas purity was ＞99.9999% and further purified by using a getter filter. The custom-made injector designed to focus the ammonia flux onto the substrate was used for the gas delivery, while aluminum and gallium were provided via conventional effusion cells. The growth sequence to minimize the residual ammonia and subsequent memory effects is the following: (1) Native oxides are desorbed at $750^{\circ}C$ (Fig. (a) for [$1^-10$] and [001] azimuth) (2) 40 nm thick AlN is first grown using nitrogen rf plasma source at $900^{\circ}C$ nder the optimized condition to maintain the layer by layer growth of AlN buffer layer and slightly Al-rich condition. (Fig. (b)) (3) After switching to ammonia source, GaN growth is initiated with different V/III ratio and temperature conditions. A streaky RHEED pattern with an appearance of a weak ($2{\times}2$) reconstruction characteristic of Ga-polarity is observed all along the growth of subsequent GaN layer under optimized conditions. (Fig. (c)) The structural properties as well as dislocation densities as a function of growth conditions have been investigated using symmetrical and asymmetrical x-ray rocking curves. The electrical characteristics as a function of buffer and GaN layer growth conditions as well as the growth sequence will be also discussed. Figure: (a) RHEED pattern after oxide desorption (b) after 40 nm thick AlN growth using nitrogen rf plasma source and (c) after 600 nm thick GaN growth using ammonia source for (upper) [110] and (lower) [001] azimuth.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.401-404
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• 2015

This paper proposes a high-performance in-loop filter in HEVC(High Efficiency Video Coding) encoder for Ultra HD video processing in real time. HEVC uses in-loop filter consisting of deblocking filter and SAO(Sample Adaptive Offset) to solve the problems of quantization error which causes image degradation. In the proposed in-loop filter encoder hardware architecture, the deblocking filter and SAO has a 2-level hybrid pipeline structure based on the $32{\times}32CTU$ to reduce the execution time. The deblocking filter is performed by 6-stage pipeline structure, and it supports minimization of memory access and simplification of reference memory structure using proposed efficient filtering order. Also The SAO is implemented by 2-statge pipeline for pixel classification and applying SAO parameters and it uses two three-layered parallel buffers to simplify pixel processing and reduce operation cycle. The proposed in-loop filter encoder architecture is designed by Verilog HDL, and implemented by 205K logic gates in TSMC 0.13um process. At 110MHz, the proposed in-loop filter encoder can support 4K Ultra HD video encoding at 30fps in realtime.

• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.689-698
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• 2005

Various systems are used to provide the location based services. But, the existing systems have some problems which have difficulties in dealing with faster services for above million people. In order to solve it, a multi-level DBMS which supports both fast data processing and large data management support should be used. The multi-level DBMS with snapshots has all the data existing in disk database and the data which are required to be processed for fast processing are managed in main memory database as snapshots. To optimize performance of this system for location based services, the query classification component which classifies the queries for efficient snapshot usage is needed. In this paper, the query classification component in multi-level DBMS for location based services is designed and implemented. The proposed component classifies queries into three types: (1) memory query, (2) disk query, (3) hybrid query, and increases the rate of snapshot usage. In addition, it applies division mechanisms which divide aspatial and spatial filter condition for partial snapshot usage. Hence, the proposed component enhances system performance by maximizing the usage of snapshot as a result of the efficient query classification.