• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10C
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• pp.965-975
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• 2005

In this paper, we propose and evaluate joint carrier and sampling frequency offset estimation schemes based on the channel estimation sequences in PLCP (Physical Layer Convergence Procedure) preamble for the proper and effcient synchronization of the MB-OFDM WB (Multi-Band Orthogonal Frequency Division Multiplexing Ultra Wide Band) systems which have recently drawn explosive attention for future W-PAN (Wireless Personal Area Network) applications. In the joint estimation schemes, we first estimate the sampling frequency offset, and then estimate the carrier frequency offset using the estimated sampling frequency offset. Moreover, to improve the reliability of the estimated offset values, each process uses a combination scheme based on weighting factors. Simulation results using IEEE 802.15 Task Group 3a UWB channel models reveal that the estimation scheme using the simple weighting factors based on easily-measurable received signal power of each sub-channel shows favorably comparable performance to the ideal scheme using the weighting factors based on the perfectly-estimated frequency response of the channel.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.471-474
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• 2022

음원 스트리밍 서비스 시장은 지속해서 성장해왔다. 그중 최근에 가장 성장세가 돋보이는 서비스는 Spotify와 Youtube music이다. 두 서비스의 추천시스템은 사용자가 좋아할 만한 음악을 계속해서 추천해 줌으로써 많은 사랑을 받고 있다. 추천시스템 성능은 추천에 활용할 수 있는 변수(Feature) 수에 비례한다고 볼 수 있다. 최대한 많은 정보를 알아야 사용자가 원하는 추천이 가능하기 때문이다. 본 논문에서는 기존에 존재하는 감정분류 방법론인 사전기반과 딥러닝 BERT를 사용한 머신기반 방법론을 적절하게 결합하여 장점을 유지하면서 단점을 보완한 하이브리드 감정 분석 모델을 제안함으로써 가사에서 느껴지는 감정 비율을 분석한다. 감정 비율을 음원 가중치 변수로 사용하면 감정 정보를 포함한 고도화된 추천을 기대할 수 있다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2437-2442
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• 2013

OFDM Wi-Fi AP is susceptible to the co-channel interference. As a countermeasure, the insertion of a smart has been addressed. Despite of the guaranteed efficiency, the complexity of the post-FFT algorithm often keeps itself from being selected as the countermeasure. Instead, simply constructed pre-FFT smart antenna of which the algorithm is based on the received signal covariance matrix is commonly used and the mathematical modeling of it has been deployed. Computer simulations evaluating the improved BER characteristics of the proposed pre-FFT using the covariance matrix of channel estimator output have been carried out. It has been demonstrated that channel matrix output based smart antenna is superior to that using received signal covariance matrix.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.7
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• pp.45-50
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• 2010

In this paper, we analysis the performance of cooperative spectrum sensing with distance based weight for cognitive radio (CR) systems and CR systems sense the spectrum of the licensed user by using a energy detection method. Threshold is determined in accordance with the constant false alarm rate (CFAR) algorithm for energy detection. The signal of licensed user is OFDM signal and the wireless channel between a licensed user and CR systems is modeled as Gaussian channel. From the simulation results, the cooperative spectrum sensing with distance based weight combining (DWC) and equal gain combing (EGC) methods shows higher spectrum sensing performance than single spectrum sensing does. And the detection probability performance with the DWC is higher than that with the EGC.

• Journal of the Korean Society for information Management
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• v.25 no.1
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• pp.211-233
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• 2008

This study examines various weighting methods for improving the performance of automatic classification based on Rocchio algorithm on two collections(LISA, Reuters-21578). First, three factors for weighting are identified as document factor, document factor, category factor for each weighting schemes, the performance of each was investigated. Second, the performance of combined weighting methods between the single schemes were examined. As a result, for the single schemes based on each factor, category-factor-based schemes showed the best performance, document set-factor-based schemes the second, and document-factor-based schemes the worst. For the combined weighting schemes, the schemes(idf*cat) which combine document set factor with category factor show better performance than the combined schemes(tf*cat or ltf*cat) which combine document factor with category factor as well as the common schemes (tfidf or ltfidf) that combining document factor with document set factor. However, according to the results of comparing the single weighting schemes with combined weighting schemes in the view of the collections, while category-factor-based schemes(cat only) perform best on LISA, the combined schemes(idf*cat) which combine document set factor with category factor showed best performance on the Reuters-21578. Therefore for the practical application of the weighting methods, it needs careful consideration of the categories in a collection for automatic classification.

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.29-32
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• 2002

본 논문은 수평적 시공간 부호화 기법(Horizontal Space-Time Coding)과 송수신단에 별도의 빔형성 기법을 결합한 JBSTC(Joint Beamforming Space-Time Coding) 구조에 대한 제안 및 동작원리를 기술하고 성능을 분석하였다. JBSTC는 MIMO(Multi-Input Multi-Output) 송수신 시스템 채널 모델링을 기반으로 송신측 안테나간 상호 상관 행렬과 수신측 안테나간 상호 상관 행렬을 이용하여 송수신 빔형성기의 안테나 가중치들을 계산한다. 본 논문에서 제안하는 JBSTC 방식은 송수신단에서 발생하는 안테나 간격과 각퍼짐에 따른 상호 상관 값을 효과적으로 보정함으로써 기존의 수평적 시공간 부호화 기법보다 우수함을 알 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.799-801
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• 2004

인터넷 정보의 축적량이 증가함으로 인해 사용자는 원하는 정보를 찾기가 더욱 어려워졌다 따라서 수많은 문서들 중에서 원하는 정보를 효과적으로 찾아주는 정보검색 시스템의 중요성이 증가하게 되었으며 이에 대한 연구도 활발히 진행되었다. 인터넷 문서에서 추출할 수 있는 정보들은 링크 정보, Anchor Text 정보, Title Text 정보, 본문 Text 정보 등이 있으며, 이런 정보들을 이용한 수많은 정보검색 시스템이 개발되거나 모델이 연구되고 있다 본 논문에서는 기존에 이용되어 왔던 일반적인 추출 점보들을 정제 및 처리를 통해 성능을 높일 수 있는 방안을 연구했던 선행 연구를 기반으로 한 실험 결과 및 사이트 가중치를 추가한 모델을 제시한다.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.88-98
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• 1998

Subspace classifier is a popular pattern recognition method based on Karhunen-Loeve transform. This classifier describes a high dimensional pattern by using a reduced dimensional subspace. Because of the loss of information induced by dimensionality reduction, however, a subspace classifier sometimes shows unsatisfactory recognition performance to the patterns having quite similar principal components each other. In this paper, we propose the use of multiple novelty neural network classifiers constructed on novelty vectors to adopt minor components usually ignored and present a method of improving recognition performance through combining those with the subspace classifier. We develop the proposed classifier on handwritten numeral database and analyze its properties. Our proposed classifier shows better recognition performance compared with other classifiers, though it requires more weight links.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.3
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• pp.123-128
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• 2019

This paper proposes the stock price prediction based on the artificial intelligence, where the model with recurrent convolution neural network (RCNN) layers is adopted. In the motivation of this prediction, long short-term memory model (LSTM)-based neural network can make the output of the time series prediction. On the other hand, the convolution neural network provides the data filtering, averaging, and augmentation. By combining the advantages mentioned above, the proposed technique predicts the estimated stock price of next day. In addition, in order to emphasize the recent time series, a custom weighted loss function is adopted. Moreover, stock data related to the stock price index are adopted to consider the market trends. In the experiments, the proposed stock price prediction reduces the test error by 3.19%, which is over other techniques by about 19%.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.359-361
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• 1999

내용 기반 영상 검색을 위해 본 연구에서는 Vector Quantization을 이용하여 영상을 구성하는 주요 객체들의 특징 추출 방법을 제안한다. 내용 기반 검색 시스템에서 사용되는 영상의 주요 특징들은 색상, 질감, 형태 및 영상을 구성하고 있는 객체들의 공간적 위치 등이 사용된다. 이러한 특징들 중에서 어떤 특징들을 사용하고 또 어떤 방식으로 결합하느냐에 따라 혹은 영상의 특성을 잘 나타낼 수 있는 주요 특징을 어떻게 추출, 표현하느냐에 따라 검색 성능에 큰 영향을 미친다. 이 중 본 논문에서는 일반적인 색상, 질감 특징 추출방법과 더불어 Vector Quantization 알고리즘을 이용하여 정지 영상을 구성하고 있는 객체들의 대표 색상과 질감 특징을 빠르게 추출하고 이를 내용 기반 검색에 이용함으로써 객체의 위치, 회전 및 크기 변화에 무관한 검색을 가능케 했다. 연구의 실험 결과 VQ를 이용함으로써 대표특징치 추출시간을 줄일 수 있었고 검색시 색상과 질감 특징의 가중치를 각각 0.5, 0.5로 주는 것이 가장 높은 검출율을 보였으며 제안된 방식에 의해 '사람' 영상의 경우 0.9의 검출율을 보였다.