• Journal of Broadcast Engineering
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• v.12 no.2
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• pp.159-176
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• 2007

In the paper, we implemented a software system that encodes XML instance documents conforming to a schema document according to the MPEG-7 BiM compression method, and decodes the encoded documents vice versa. We designed software structures of BiM encoder and decoder as class hierarchies, and then implemented the structures. The implemented BiM encoder shows a compression ratio of 9.44% on the average. The BiM encoder is a general-purpose XML compressor that can encode any instance documents conforming to a schema document described in XML Schema language including the MPEG-7 schema. The BiM encoder thus can be used in many application fields including digital broadcasting environment, where encoding XML instance documents is needed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.9 no.1
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• pp.1-10
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• 1984

The time division multiplexing PCM encoder which is constructed for an airborne telemetering system is investigated. Selected by program switch, the PCM encoder has 0~64 words/framd($\pm$5V full scale) of allowable analog input channels, 0~30bits(5V$\pm$1V or 0V$\pm$1V dc) of discrete channels, 70 and 140K bits/sec of bit rate and 8~12bits/word of resolution. And filtered output PCM code is NRZ-L and Bi-S through the 5 pole Bessel LPF(f=100kHz), and the maximum accuracy of PCM encoder is $\pm$0.2% of its full scale.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.188-191
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• 2020

본 연구는 COVID-19 질의 응답 태스크를 위한 Poly-encoder 기반의 태스크를 제안하였다. COVID-19 질의 응답 시스템은 사람들에게 최신 정보에 대해 빠르고 신뢰성이 높은 정보를 전달하는 특성을 가져야한다. 검색 기반 질의 응답 시스템은 pairwise 연산을 기반으로 수행되는데, Poly-encoder는 사전 학습된 트랜스포머(transformer)기반의 pairwise 연산 방법론 중 기존 Cross-encoder와 Bi-encoder보다 실사용 및 성능이 뛰어남을 보였다 [1]. 특히, Poly-encoder는 정확도가 높으면서도 빠른 응답속도를 가지며 검색기반의 각종 태스크에서 좋은 성능을 보였다. 따라서 본 연구는 COVID-19를 위한 Poly-encoder기반의 질의 응답 태스크를 위하여 기존 질의 응답 태스크와 페르소나 기반의 질의 응답 태스크로 두 가지 유형의 태스크를 생성하여 모델을 학습하였다. 또한 신뢰성 있는 리소스정보로부터 모델에 최신 정보 반영을 위하여 자동 크롤러를 구축하여 데이터를 수집하였다. 마지막으로 전문가를 통한 데이터셋을 구축하여 질문-응답과 질의어-질문에 대한 모델 검증을 수행하였다.

• Proceedings of the IEEK Conference
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• 2000.06a
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• pp.29-32
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• 2000

In this paper, we present an extension of the multi-code CDMA (code division multiple access) systems based on bi-orthogonal modulation by employing a convolutional encoder and an interleaver before serial-to-parallel conversion in the modulator. Bandwidth expansion by the convolutional encoder can be compensated for by the bi-orthogonal modulation, and the interleaver in the system scrambles the convolutionally encoded data bits so that, after serial-to-parallel conversion, each code channel conveys those bits far apart in time. The result is that the proposed system with several order of magnitude less implementational complexity, achieves quite close performance of the conventional systems comprised of Walsh modulation and multiple convolutional encoders and interleavers in all the code channels.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.131-134
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• 2022

자연어처리 분야 중 질의응답 태스크는 전통적으로 많은 연구가 이뤄지고 있는 분야이며, 최근 밀집 벡터를 사용한 리트리버(Dense Retriever)가 성공함에 따라 위키피디아와 같은 방대한 정보를 활용하여 답변하는 오픈 도메인 QA(Open-domain Question Answering) 연구가 활발하게 진행되고 있다. 대표적인 검색 모델인 DPR(Dense Passage Retriever)은 바이 인코더(Bi-encoder) 구조의 리트리버로서, BERT 모델 기반의 질의 인코더(Query Encoder) 및 문단 인코더(Passage Encoder)를 통해 임베딩한 벡터 간의 유사도를 비교하여 문서를 검색한다. 하지만, BERT와 같이 엔티티(Entity) 정보에 대해 추가적인 학습을 하지 않은 언어모델을 기반으로 한 리트리버는 엔티티 정보가 중요한 질문에 대한 답변 성능이 저조하다. 본 논문에서는 엔티티 중심의 질문에 대한 답변 성능 향상을 위해, 엔티티를 잘 이해할 수 있는 LUKE 모델 기반의 리트리버를 제안한다. KorQuAD 1.0 데이터셋을 활용하여 한국어 리트리버의 학습 데이터셋을 구축하고, 모델별 리트리버의 검색 성능을 비교하여 제안하는 방법의 성능 향상을 입증한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.5-6
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• 2021

본 논문에서는 Semi-supervised Learning 방식의 이상감지 방법을 제안한다. 취득한 소음 데이터를 이미지화 시킨 후 Convolution AutoEncoder 학습 방법을 이용하여 모델을 학습한다. 고장 데이터와 정상 데이터 간의 데이터 불균형 문제가 대두되기 때문에 정상 데이터만을 활용한 이상감지는 실제 산업현장의 상황에 알맞게 사용할 수 있을 것이라 기대한다.

• Journal of Broadcast Engineering
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• v.20 no.1
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• pp.171-182
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• 2015

HEVC can increase the coding efficiency significantly compared with H.264/AVC however it requires much larger computational complexities in both encoder and decoder. In this paper, the decision process of inter-prediction modes in the HEVC reference software has been studied and a fast algorithm to reduce the computational complexity of encoder and decoder is introduced. The proposed scheme introduces a early decision criteria using the outputs of uni-directional predictions to skip the bi-directional prediction estimation. From the experimental results, it was proven that the proposed method can reduce the encoding complexity by 12.0%, 14.6% and 17.2% with 0.6%, 1.0% and 1.5% of coding efficiency penalty, respectively. In addition, the ratio of bi-directional prediction mode was reduced by 6.3%, 11.8% and 16.6% at the same level of coding efficiency penalty, respectively, which should lead to the decoder complexity reduction. Finally, the effects of the proposed scheme are maintained regardless of the use of the early skip decision algorithm which is implemented in the HEVC reference software.

• ETRI Journal
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• v.44 no.3
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• pp.438-449
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• 2022

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject-predicate-object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.447-453
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• 2004

In the case of a capsule that can acquire and transmit images from the intestines, the size of the module and the battery capacity in the capsule are subject to restriction. The capsule must be swallowable and the battery must maintain the stable power during the capsule travels in the gastrointestinal tract. Therefore, it is important to control the endoscope using bi-directional wireless communication. In this study, encoder and decoder CPLD modules for bi-directional capsule endoscopes were designed and implemented. The designed controller for capsule endoscope can transmit the images of GI-track from inside to outside of the body and the capsules can be controlled by external controller simultaneously. The designed and implemented controller was verified by an in-vivo animal experiments. From these experiments, it was verified that the CPLD module for bi-directional capsule endoscope satisfied the design specifications.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.321-328
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• 2008

In this paper, we propose the hardware architecture of two-dimensional discrete wavelet transform (2D DWT) and quantization for using JPEG2000. Color 2-D DWT processor is proposed that is to apply to JPEG 2000 Hard-wired Encoder. JPEG 2000 DWT processor uses the Daubechies' (9,7) bi-orthogonal filter, and we design by minimizing error of the DWT transformer by ${\pm}1$ LSB during compression and decompression. We designed the DWT filters that using by using shift and adder structure instead of multiplier structure which raise the hardware complexity. It is improve the operation speed of filters and reduce the hardware complexity. The proposed system is designed by the hardware description language Verilog-HDL and verified by Synopsys Design Analyzer using TSMC 0.25${\mu}m$ ASIC library.