• Annual Conference of KIPS
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• 2018.05a
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• pp.562-565
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• 2018

A wide range of IoT applications use information collected from networks of sensors for monitoring and controlling purposes. However, the frequent appearance of fault data makes it difficult to extract correct information, thereby sending incorrect commands to actuators that can threaten human privacy and safety. For this reason, it is necessary to have a mechanism to detect fault data collected from sensors. In this paper, we present a trend-adaptive multi-scale principal component analysis (Trend-adaptive MS-PCA) model for data fault detection. The proposed model inherits advantages of Discrete Wavelet Transform (DWT) in capturing time-frequency information and advantages of PCA in extracting correlation among sensors' data. Experimental results on a real dataset show the high effectiveness of the proposed model in data fault detection.

• Current Research on Agriculture and Life Sciences
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• v.30 no.2
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• pp.124-130
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• 2012

Sweetpotato whitefly, Bemisia tabaci, is a vector of more than 100 plant-diseased viruses, as well as a serious pest of various horticultural plants. This species harbors a primary endosymbiont Portiera along with several secondary endosymbionts such as Cardinium and Hamiltonella. We investigated whether or not TYLCV acquisition alters the densities of endosymbionts in the body of B. tabaci using quantitative real-time PCR. Our results showed that the densities of both Cardinium and Hamiltonella, but not Portiera, increased upon acquisition of TYLCV. In addition, expression of GroEL, a molecular chaperone produced by Hamiltonella, was significantly upregulated in TYLCV-infected whiteflies. Our results suggest that endosymbionts may play an important role in TYLCV transmission mechanism within the body of B. tabaci.

• Annual Conference of KIPS
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• 2001.04a
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• pp.61-64
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• 2001

본 논문에서는 실시간 MMDBMS 를 위한 효율적인 색인 기법을 제안한다. 기존의 주기억장치 트리 기반 색인 구조는 범위 검색을 효과적으로 지원할 수 있지만 한 노드에 대한 접근시간과 평균 접근시간의 차이가 클 수 있기 때문에 실시간 특성을 보장하지 못하는 단점이 있다. 또한 해시 기반 색인 구조는 간단한 검색에서 접근 시간이 매우 빠르고 일정하지만 범위 검색을 지원하지 못하는 단점이 있다. 이러한 두 색인 구조의 단점을 해결하기 위해 본 논문에서는 동적 확장이 가능하며 검색 시간이 빠르고 실시간 특성을 지원할 수 있는 ECBH(Extendible Chained Bucket Hashing)와 범위 검색에 더욱 효과적인 $T^{\ast}$-트리를 상호보완적으로 결합하여 Hyper-TH(Hyper Tree-Hash)라는 실시간 MMDBMS 에 적합한 새로운 색인 기법을 제안하고 구현한다. 그리고 성능 평가를 통해 제안하는 색인 기법의 우수성을 증명한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.171-172
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• 2019

In this paper, we consider the dynamic method for the searching development of Web map to the monitoring object in the risk environments. It is to recognize the real-time detection to the risk situation based on the location monitoring mechanism of management to the object movement.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1231-1242
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• 2019

This paper presents a two-dimensional attention-based long short-memory (2D-ALSTM) model for stock index prediction, incorporating input attention and temporal attention mechanisms for weighting of important stocks and important time steps, respectively. The proposed model is designed to overcome the long-term dependency, stock selection, and stock volatility delay problems that negatively affect existing models. The 2D-ALSTM model is validated in a comparative experiment involving the two attention-based models multi-input LSTM (MI-LSTM) and dual-stage attention-based recurrent neural network (DARNN), with real stock data being used for training and evaluation. The model achieves superior performance compared to MI-LSTM and DARNN for stock index prediction on a KOSPI100 dataset.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.11 no.2
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• pp.67-72
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• 2001

An efficient encryption mechanism for transmitting voice packets on the Internet was proposed in this study. The VPN approach of encrypting all the packets through a gateway increases delay and delay jitter that may degrade the quality of service (QoS) in real-time communications. A user-controlled secure Internet phone, therefore. was designed and implemented. The secure phone enables the user to apply encryption to his own call when necessary, and reduces security overheads on the gateway.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3348-3364
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• 2021

Magic matrix-based data hiding schemes are applied to transmit secret information through open communication channels safely. With the development of various magic matrices, some higher dimensional magic matrices are proposed for improving the security level. However, with the limitation of computing resource and the requirement of real time processing, these higher dimensional magic matrix-based methods are not advantageous. Hence, a kind of data hiding scheme based on a single or a group of multi-dimensional flexible magic matrices is proposed in this paper, whose magic matrix can be expanded to higher dimensional ones with less computing resource. Furthermore, an adaptive mechanism is proposed to reduce the embedding distortion. Adapting to the secret data, the magic matrix with least distortion is chosen to embed the data and a marker bit is exploited to record the choice. Experimental results confirm that the proposed scheme hides data with high security and a better visual quality.

• Annual Conference of KIPS
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• 2009.04a
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• pp.853-855
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• 2009

센서 노드를 위한 운영체제는 제한된 시스템 자원 하에서 동작하므로 전력 소모량을 최소화 시킬 수 있는 시스템 레벨의 저전력 기법과 함께 실시간성을 지원해야 한다. 이에 본 논문에서는 저전력 마이크로프로세서인 ATmega128L 기반의 센서 노드 하드웨어 플랫폼을 설계하고, 센서노드 플랫폼에서 동작하는 멀티스레드 기반의 실시간 운영체제인 RT-UNOS를 개발하였다. 제안한 센서 노드 플랫폼의 동작 검증을 위하여 기존의 센서노드용 운영체제인 TinyOS와 MANTIS, cc-EDF와의 성능을 구현한 센서노드 상에서 실험을 진행하여 비교 분석하였다.

• Annual Conference of KIPS
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• 2009.04a
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• pp.856-858
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• 2009

최근 휴대폰, PMP 와 같은 모바일 장치를 개발하는 데에 그 성능과 저전력화 SoC 기술을 적용하고 있다. 또한 화상통화와 같은 영상 및 음성 멀티미디어 서비스가 확장되고 있다. 그러나 현재 모바일 SoC 기술에서 멀티미디어 서비스의 실시간 요구사항을 고려한 네트워크 프로토콜 설계에 대한 연구가 부족하다. 이에 본 논문에서는 실시간성 모바일 SoC 에서 실시간성을 제공하는 네트워크 프로토콜 스택을 설계하는 기법을 제안하고자 한다. 그리고 제안한 기법이 구현된 실시간 네트워킹 SoC 플랫폼의 성능을 실험하였으며, 그 결과 기존의 기법보다 더 좋음을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.721-739
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• 2023

Rating prediction is an important issue in recommender systems, and its accuracy affects the experience of the user and the revenue of the company. Traditional recommender systems use Factorization Machinesfor rating predictions and each feature is selected with the same weight. Thus, there are problems with inaccurate ratings and limited data representation. This study proposes a deep recommendation model based on self-attention Factorization (SAFMR) to solve these problems. This model uses Convolutional Neural Networks to extract features from user and item reviews. The obtained features are fed into self-attention mechanism Factorization Machines, where the self-attention network automatically learns the dependencies of the features and distinguishes the weights of the different features, thereby reducing the prediction error. The model was experimentally evaluated using six classes of dataset. We compared MSE, NDCG and time for several real datasets. The experiment demonstrated that the SAFMR model achieved excellent rating prediction results and recommendation correlations, thereby verifying the effectiveness of the model.