• Applied Science and Convergence Technology
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• v.23 no.2
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• pp.72-82
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• 2014

Lithium-ion batteries (LIBs) have become popular electrochemical devices. Due to the unique advantages of LIBs in terms of high operating voltage, high energy density, low self-discharge, and absence of memory effects, their application range, which was primarily restricted to portable electronic devices, is now being extended to high-power applications, such as electric vehicles (EVs) and hybrid electrical vehicles (HEVs). Among various anode materials, $Li_4Ti_5O_{12}$ (LTO) is believed to be a promising anode material for high-power LIBs due to its advantages of high working potential and outstanding cyclic stability. However, the rate performance of LTO is limited by its intrinsically low electronic conductivity and poor $Li^+$ ion diffusion coefficient. This review highlights the recent progress in improving the rate performance of LTO through doping, compositing, and nanostructuring strategies.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.288-289
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• 2012

최근 데이터 저장을 위한 고속 입출력에서 병목현상을 해결하기 위해 다양한 SSD(Solid State Drive) 관련 연구가 많이 수행되고 있다. 대표적인 것으로 비휘발성 메모리인 플래시와 차세대 반도체 메모리인 SCM(Storage Class Memory) SSD가 있고, 휘발성 메모리인 DRAM기반의 SSD가 있다. 플래시 또는 SCM 메모리기반 저장장치는 하드 디스크기반 저장장치에 비해 읽기 속도가 빠르며, 내구성이 강하다는 장점으로 새로운 저장장치 시스템의 저장매체로 부각되고 있으나, 단위 저장 공간 당 높은 가격으로 인해 저장장치 시스템에 적용하기 에는 많은 문제점이 있다. 최근에는 이러한 문제를 해결하기 위해 고용량의 하드디스크와 SSD를 RAID 또는 단일 저장장치 매체로 구성하는 하이브리드 저장장치에 관한 연구와 제품이 출시되고 있다. 본 논문에서는 이러한 하이브리드 저장 매체 어레이를 저장장치 시스템으로 구성하기 위한 볼륨구성과 해당 서버에 볼륨 제공 서비스를 수행하기 위한 하이브리드 저장장치 시스템 설계 방법에 대해 설명한다.

• 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.

• Wind and Structures
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• v.38 no.6
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• pp.461-475
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• 2024

A reliable wind speed forecasting method is crucial for the applications in wind engineering. In this study, the generalized S-transform (GST) is innovatively applied for wind speed forecasting to uncover the time-frequency characteristics in the non-stationary wind speed data. The improved grey wolf optimizer (IGWO) is employed to optimize the adjustable parameters of GST to obtain the best time-frequency resolution. Then a hybrid method based on IGWO-optimized GST is proposed to validate the effectiveness and superiority for multi-step non-stationary wind speed forecasting. The historical wind speed is chosen as the first input feature, while the dynamic time-frequency characteristics obtained by IGWO-optimized GST are chosen as the second input feature. Comparative experiment with six competitors is conducted to demonstrate the best performance of the proposed method in terms of prediction accuracy and stability. The superiority of the GST compared to other time-frequency analysis methods is also discussed by another experiment. It can be concluded that the introduction of IGWO-optimized GST can deeply exploit the time-frequency characteristics and effectively improving the prediction accuracy.

• 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.