• Journal of the Korean Solar Energy Society
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• v.31 no.6
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• pp.23-31
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• 2011

The wind energy is one of the most prospective resources in renewable energy. However, the WTGS shouldn't be installed indiscriminately because the power system can be negatively influenced by a variable and uncertain nature of the wind energy. It is the reason why it has to be limited to install the WTGS thoughtlessly mentioned above that support the importance of the maximum penetration limit of wind power. It may required that power system operators suggests a new way of power system operation as percentage of the WTGS increase in the existing power system. The wind power is fixed in a limited area, so using rate of the wind power will be increased by installing additional WTGS. In this paper, we have studied on economic evaluation of the wind capacity increased by restricting the output of the WTGS as the way to increase the wind capacity.

• Journal of Information Science Theory and Practice
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• v.2 no.1
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• pp.6-21
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• 2014

This paper proposes a novel knowledge extraction system, TAKES (Two-step Approach for Knowledge Extraction System), which integrates advanced techniques from Information Retrieval (IR), Information Extraction (IE), and Natural Language Processing (NLP). In particular, TAKES adopts a novel keyphrase extraction-based query expansion technique to collect promising documents. It also uses a Conditional Random Field-based machine learning technique to extract important biological entities and relations. TAKES is applied to biological knowledge extraction, particularly retrieving promising documents that contain Protein-Protein Interaction (PPI) and extracting PPI pairs. TAKES consists of two major components: DocSpotter, which is used to query and retrieve promising documents for extraction, and a Conditional Random Field (CRF)-based entity extraction component known as FCRF. The present paper investigated research problems addressing the issues with a knowledge extraction system and conducted a series of experiments to test our hypotheses. The findings from the experiments are as follows: First, the author verified, using three different test collections to measure the performance of our query expansion technique, that DocSpotter is robust and highly accurate when compared to Okapi BM25 and SLIPPER. Second, the author verified that our relation extraction algorithm, FCRF, is highly accurate in terms of F-Measure compared to four other competitive extraction algorithms: Support Vector Machine, Maximum Entropy, Single POS HMM, and Rapier.

• Journal of Chest Surgery
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• v.31 no.4
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• pp.402-408
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• 1998

A 56-year old female underwent total aortic arch replacement March 1995, because of an expanding chronic Debakey type I aortic dissection. This aortic dissection had an intimal tear at the origin of the right carotid artery. Retrograde and antegrade propagation of dissection resulted in aortic arch blood flow separation and expanding pseudolumen to the abdominal aorta. Sudden anuria(ARF) developed 3 hours later postoperatively and renal doppler ultrasonography and aortography showed diminished blood flow of renal arteries. We performed balloon aortic dilatation but failed. She could be restored good renal flow after intimal flap fenestration resection and thrombectomy of the abdominal aorta. This patient could be discharged in a state of mild CRF after 2 months of ICU care for respiratory and renal failure.

• Smart Media Journal
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• v.10 no.2
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• pp.55-60
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• 2021

In this research, we design and perform experiment to investigate whether neuronal activity patterns elicited while solving game tasks are spontaneously reactivated in during sleep. In order to recognize human activity EEG-fMRI signals are used at the same time. Experimental results shows that reward for the success of tasks performed before sleeping have an effect on sleep brain activity. The study uncovers a neural mechanism whereby rewarded life experiences are preferentially replayed and consolidated while we sleep.

• ETRI Journal
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• v.44 no.5
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• pp.794-804
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• 2022

Transformer models have had a great impact on natural language processing (NLP) in recent years by realizing outstanding and efficient contextualized language models. Recent studies have used transformer-based language models for various NLP tasks, including Persian named entity recognition (NER). However, in complex tasks, for example, NER, it is difficult to determine which contextualized embedding will produce the best representation for the tasks. Considering the lack of comparative studies to investigate the use of different contextualized pretrained models with sequence modeling classifiers, we conducted a comparative study about using different classifiers and embedding models. In this paper, we use different transformer-based language models tuned with different classifiers, and we evaluate these models on the Persian NER task. We perform a comparative analysis to assess the impact of text representation and text classification methods on Persian NER performance. We train and evaluate the models on three different Persian NER datasets, that is, MoNa, Peyma, and Arman. Experimental results demonstrate that XLM-R with a linear layer and conditional random field (CRF) layer exhibited the best performance. This model achieved phrase-based F-measures of 70.04, 86.37, and 79.25 and word-based F scores of 78, 84.02, and 89.73 on the MoNa, Peyma, and Arman datasets, respectively. These results represent state-of-the-art performance on the Persian NER task.

• International Journal of Contents
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• v.17 no.4
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.617-621
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• 2021

의미역 결정은 문장 속에서 서술어와 그 논항의 관계를 파악하며, '누가, 무엇을, 어떻게, 왜' 등과 같은 의미역 관계를 찾아내는 자연어 처리 기법이다. 최근 수행되고 있는 의미역 결정 연구는 주로 말뭉치를 활용하여 딥러닝 학습을 하는 방식으로 연구가 이루어지고 있다. 최근 구글에서 개발한 사전 훈련된 Bidirectional Encoder Representations from Transformers (BERT) 모델이 다양한 자연어 처리 분야에서 상당히 높은 성능을 보이고 있다. 본 논문에서는 한국어 의미역 결정 성능 향상을 위해 한국어의 언어적 특징을 고려하며 사전 학습된 SNU KR-BERT를 사용하면서 한국어 의미역 결정 모델의 성능을 살펴보였다. 또한, 본 논문에서는 BERT 모델에서 과연 어떤 히든 레이어(hidden layer)에서 한국어 의미역 결정을 더 잘 수행하는지 알아보고자 하였다. 실험 결과 마지막 히든 레이어 임베딩을 활용하였을 때, 언어 모델의 성능은 66.4% 였다. 히든 레이어 별 언어 모델 성능을 비교한 결과, 마지막 4개의 히든 레이어를 이었을 때(concatenated), 언어 모델의 성능은 67.9% 이였으며, 11번째 히든 레이어를 사용했을 때는 68.1% 이였다. 즉, 마지막 히든 레이어를 선택했을 때보다 더 성능이 좋았다는 것을 알 수 있었다. 하지만 각 언어 모델 별 히트맵을 그려보았을 때는 마지막 히든 레이어 임베딩을 활용한 언어 모델이 더 정확히 의미역 판단을 한다는 것을 알 수 있었다.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.190-194
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• 2019

개체명 인식이란 주어진 문서에서 개체명의 범위를 찾고 개체명을 분류하는 것이다. 최근 많은 연구는 신경망 모델을 이용하며 하나 이상의 단어로 구성된 개체명을 BIO 표기법으로 표현한다. BIO 표기법은 개체명이 시작되는 단어의 표지에 B(Beginning)-를 붙이고, 개체명에 포함된 그 외의 단어의 표지에는 I(Inside)-를 붙이며, 개체명과 개체명 사이의 모든 단어의 표지를 O로 간주하는 방법이다. BIO 표기법으로 표현된 말뭉치는 O 표지가 90% 이상을 차지하므로 O 표지에 대한 혼잡도가 높아지는 문제와 불균형 학습 문제가 발생된다. 본 논문에서는 BIO 표기법 대신에 BIT 표기법을 제안한다. BIT 표기법이란 BIO 표기법에서 O 표지를 T(Tag) 표지로 변환하는 방법이며 본 논문에서 T 표지는 품사 표지를 나타낸다. 실험을 통해서 BIT 표기법이 거의 모든 경우에 성능이 향상됨을 확인할 수 있었다.

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.7
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• pp.858-868
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• 2008

This study was carried out to investigate the effects of Gastrodiae Rhizoma powder on plasma lipid profiles in elderly volunteers with hyperlipidemia, hypertension, diabetes or heart disease. 32 elderly people, 11 males and 21 females aged $60{\sim}77$ years, were given Gastrodiae Rhizoma powder 15 g twice daily for 6 months. We investigated the antheropometric data, general characteristics and dietary habit by using questionnaires. Fasting blood samples were collected from the subjects before and after this 6 months intervention study. Blood pressure, glucose, hemoglobin and lipid levels of plasma, atherogenic index (AI) and cardiac risk factors (CRF, LHR, HTR) were determined before and after consumption of Gastrodiae Rhizoma powder. The mean body mass index (BMI) of the male and female subjects were 22.4 and 23.6, respectively. The percent of ideal body weight (PIBW) of males and females were 105.6% and 122.3%, respectively. The subjects had decreased intake frequency of fish and meat in their dietary habit. After consumption of Gastrodiae Rhizoma powder, there were no significant differences in blood pressure; however, the blood glucose significantly decreased with Gastrodiae Rhizoma intake in the males. In the subjects, the levels of plasma total cholesterol, triglyceride, and LDL-cholesterol were decreased by the consumption of Gastrodiae Rhizoma powder; while the levels of plasma LDL-cholesterol was significantly decreased in female. Blood pressure and biochemical assessment (blood glucose, hemoglobin, triglyceride, total cholesterol, LDL and HDL-cholesterol) of the subjects were within the normal range. It was found that AI, CRF and LHR were significantly decreased by Gastrodiae Rhizoma intake. The present results indicate that dietary supplementation of Gastrodiae Rhizoma improved lipid metabolism and cardiac risk factor in cardiovascular disease.

• The Korea Journal of Herbology
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• v.31 no.2
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• pp.39-45
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• 2016

Objectives : Abnormal regulation of glucose and impaired lipid metabolism that result from a defective or deficient insulin are the key etiological factor in type 2 diabetes mellitus. The our study investigated the effects of Gamioknyeo-jeon (GO) on blood glucose and lipid metabolism improved by it in db/db mice (a murine model of type 2 diabetes mellitus).Methods : The animals were divided into 3 groups: Normal groups were not-treated C57BL/6 mice; Control groups were treated orally with DW in db/db mice; GO groups were treated orally with GO (200 ㎎/㎏/day) in db/db mice. After mice were treated with GO for 5 weeks, we measured AST, ALT, creatinine, BUN, body weight, food intake, blood glucose, insulin and lipid levels (total cholesterol, HDL cholesterol, and LDL cholesterol and atherogenic index(AI) and cardiac risk factor(CRF).Results : Serum AST, ALT, creatinine, BUN levels were not changed by GO do not show any toxic effects. GO groups were decreased in body weight, food intake and blood glucose level among compared to Control groups. Also, GO groups were found to have atherogenic Index and cardiac risk factor as well as lipid metabolism improvement (total cholesterol and LDL cholesterol decrease). Finally, GO groups were increased the insulin compared to Normal and control groups.Conclusions : We suggest that GO may have the control effects of diabetes mellitus by improving blood glucose control and lipid metabolism.