• Journal of Korean Library and Information Science Society
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• v.40 no.3
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• pp.317-336
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• 2009

Several protocols have been developed to efficiently utilize a great number of distributed resources. This paper investigated the background, operations and elements of SRU and SRU Record Update protocol, compared them with other protocols, and reviewed their implementation cases. The purpose of this pager is to broaden the understanding of the two new standards and to provide a practical guide to ensure their interoperability for libraries and information service centers which want to expose their own contents and to access to external resources.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1241-1245
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• 2009

As FFT(Fast Fourier Transform) processor is used in OFDM(Orthogonal Frequency Division Multiplesing) system. According to increase requirement about mobility and broadband, Research about low power and low area FFT processor is needed. So research concern in reduction of memory size and complex multiplier is in progress. Increasing points of FFT increase memory area of FFT processor. Specially, SRU(Signal Reordering Unit) has the most memory in FFT processor. In this paper, we propose a reduced method of memory size of SRU in FFT processor. SRU of 64, 1024 point FFT processor performed implementation by VerilogHDL coding and it verified by simulation. We select the APEX20KE family EP20k1000EPC672-3 device of Altera Corps. SRU implementation is performed by synthesis of Quartus Tool. The bits of data size decide by 24bits that is 12bits from real, imaginary number respectively. It is shown that, the proposed SRU of 64point and 1024point achieve more than 28%, 24% area reduction respectively.

• Journal of the Korean Society for information Management
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• v.22 no.1 s.55
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• pp.229-248
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• 2005

This study proposes a national cooperative model of access point control, that enables local systems to utilize national access point control record, without creating their own authority records. In order to applying this model, a client/server system is developed using SRU (Search and Retrieve URL Service) protocol. The suggested access point control system will be a cost-effective and time-saving model for the local system, and will be more flexible and extensible with concept of access point control, XML record format and SRU protocol.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.488-491
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• 2008

The variations of the TFT characteristics in AMOLEDs result in the decrease of the uniformity of the displays. Measurement of the long-range uniformity (LRU) is straightforward. However, there is no method for measuring the short-range uniformity (SRU) yet. Quantifying the SRU is important in evaluating various TFT backplanes and compensation circuits. We propose new methods for measuring SRU.

• Journal of Physiology & Pathology in Korean Medicine
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• v.18 no.1
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• pp.69-74
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• 2004

We have examined the relaxative response to the water extract of Rheum undulatum L. (ERU) and water extract of alcohol steamed Rheum undulatum L. (SRU) with isolated thoracic aorta from sprague dawley (SD) rat. Rat thoracic aorta was investigated in vessel segments suspended for isometric tension recording by polygraph. Responses to ERU and SRU were investigated in vessels precontracted with 5-hydroxytryptamine(5-HT). We found that the thoracic aorta segments responded to ERU and SRU with a dose-dependent vasorelaxation : The thoracic aorta segments responded to ERU and SRU with a dose-dependent vasodilation. The amounts of emodin were 0.063%, 0.076% and 0.145% in ERU and SRU, respectable. The 5-HT induced contraction at 10-4M were inhibited by 85.2±4.76% and 84.0±2.91% after addition of the 0.1mg/mL water extract of ERU and SRU. The 5-HT induced contraction at 10/sup -3/M were inhibited by 100% after 10/sup -3/M emodin. In conclusion, vasodilation effect of the water extract of Rheum undulatum L. in rat thoracic aorta was not decreased according to the processing of alcohol steamed Rheum undulatum L.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.35-40
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• 2017

기계 독해(Machine reading comprehension)는 주어진 문맥을 이해하고, 질문에 적합한 답을 문맥 내에서 찾는 문제이다. Simple Recurrent Unit (SRU)은 Gated Recurrent Unit (GRU)등과 같이 neural gate를 이용하여 Recurrent Neural Network (RNN)에서 발생하는 vanishing gradient problem을 해결하고, gate 입력에서 이전 hidden state를 제거하여 GRU보다 속도를 향상시킨 모델이며, Self-matching Network는 R-Net 모델에서 사용된 것으로, 자기 자신의 RNN sequence에 대하여 어텐션 가중치 (attention weight)를 계산하여 비슷한 의미 문맥 정보를 볼 수 있기 때문에 상호참조해결과 유사한 효과를 볼 수 있다. 본 논문에서는 한국어 기계 독해 데이터 셋을 구축하고, 여러 층의 SRU를 이용한 Encoder에 Self-matching layer를 추가한 $S^2$-Net 모델을 제안한다. 실험 결과, 본 논문에서 제안한 $S^2$-Net 모델이 한국어 기계 독해 데이터 셋에서 EM 65.84%, F1 78.98%의 성능을 보였다.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.35-40
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• 2017

기계 독해(Machine reading comprehension)는 주어진 문맥을 이해하고, 질문에 적합한 답을 문맥 내에서 찾는 문제이다. Simple Recurrent Unit (SRU)은 Gated Recurrent Unit (GRU)등과 같이 neural gate를 이용하여 Recurrent Neural Network (RNN)에서 발생하는 vanishing gradient problem을 해결하고, gate 입력에서 이전 hidden state를 제거하여 GRU보다 속도를 향상시킨 모델이며, Self-matching Network는 R-Net 모델에서 사용된 것으로, 자기 자신의 RNN sequence에 대하여 어텐션 가중치 (attention weight)를 계산하여 비슷한 의미 문맥 정보를 볼 수 있기 때문에 상호참조해결과 유사한 효과를 볼 수 있다. 본 논문에서는 한국어 기계 독해 데이터 셋을 구축하고, 여러 층의 SRU를 이용한 Encoder에 Self-matching layer를 추가한 $S^2$-Net 모델을 제안한다. 실험 결과, 본 논문에서 제안한 $S^2$-Net 모델이 한국어 기계 독해 데이터 셋에서 EM 65.84%, F1 78.98%의 성능을 보였다.

• Proceedings of KOSOMES biannual meeting
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• 2006.11a
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• pp.245-251
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• 2006

This is paper aims to evaluate allocation order of SRU using Analytic Network Process. For evaluation, in this paper, assess about person, ship and environment related risk by fuzzy logic and AHP(Analytic hierarchy Process). Also, quantity and quality operation efficiency assess by DEA (Data Envelopment Analysis) and Liquate scale. finally total weight calculate by ANP. At the result, Rescue Units of MP, YS RCC/RSC is order higher. Thus, it needs to have more rescue ships and rescue devices for relieving the risk in the future.

• Korean Management Science Review
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• v.31 no.3
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• pp.61-76
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• 2014

There are three methods for calculating the optimal level for spare part inventories in a MIME (Multi Indenture and Multi Echelon) system : marginal analysis, Lagrangian relaxation method, and genetic algorithm. However, their solutions are sub-optimal solutions because the MIME system is neither convex nor separable by items. To be more specific, SRUs (Shop Replaceable Units) are required to fix a defected LRU (Line Replaceable Unit) because one LRU consists of several SRUs. Therefore, the level of both SRU and LRU cannot be calculated independently. Based on the limitations of three existing methods, we proposes a improved algorithm applying marginal analysis on determining LRU stock level and genetic algorithm on determining SRU stock level. It can draw optimal combinations on LRUs through separating SRUs. More, genetic algorithm enables to extend the solution search space of a SRU which is restricted in marginal analysis applying greedy algorithm. In the numerical analysis, we compare the performance of three existing methods and the proposed algorithm. The research model guarantees better results than the existing analytical methods. More, the performance variation of the proposed method is relatively low, which means one execution is enough to get the better result.

• ETRI Journal
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• v.41 no.3
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• pp.371-382
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• 2019

Machine reading comprehension is the task of understanding a given context and finding the correct response in that context. A simple recurrent unit (SRU) is a model that solves the vanishing gradient problem in a recurrent neural network (RNN) using a neural gate, such as a gated recurrent unit (GRU) and long short-term memory (LSTM); moreover, it removes the previous hidden state from the input gate to improve the speed compared to GRU and LSTM. A self-matching network, used in R-Net, can have a similar effect to coreference resolution because the self-matching network can obtain context information of a similar meaning by calculating the attention weight for its own RNN sequence. In this paper, we construct a dataset for Korean machine reading comprehension and propose an $S^2-Net$ model that adds a self-matching layer to an encoder RNN using multilayer SRU. The experimental results show that the proposed $S^2-Net$ model has performance of single 68.82% EM and 81.25% F1, and ensemble 70.81% EM, 82.48% F1 in the Korean machine reading comprehension test dataset, and has single 71.30% EM and 80.37% F1 and ensemble 73.29% EM and 81.54% F1 performance in the SQuAD dev dataset.