• 문화기술의 융합
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• 제10권1호
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• pp.617-623
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• 2024

언어와 감정 사이의 복잡한 관계의 특징을 보이며, 우리의 말을 통해 감정을 식별하는 것은 중요한 과제로 인식된다. 이 연구는 음성 및 텍스트 데이터를 모두 포함하는 다중 모드 분류 작업을 통해 음성 언어의 감정을 식별하기 위해 속성 엔지니어링을 사용하여 이러한 과제를 해결하는 것을 목표로 한다. CNN(Convolutional Neural Networks)과 LSTM(Long Short-Term Memory)이라는 두 가지 분류기를 BERT 기반 사전 훈련된 모델과 통합하여 평가하였다. 논문에서 평가는 다양한 실험 설정 전반에 걸쳐 다양한 성능 지표(정확도, F-점수, 정밀도 및 재현율)를 다룬다. 이번 연구 결과는 텍스트와 음성 데이터 모두에서 감정을 정확하게 식별하는 두 모델의 뛰어난 능력을 보인다.

• 대한지역사회영양학회지
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• 제14권2호
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• pp.229-235
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• 2009

This study was carried out to determine whether a short-term zinc supplementation could improve the zinc status without adverse changes in copper status among type 2 diabetic patients. Seventy-six diabetic subjects and 72 normal adults participated in this study. Subjects were randomly divided into supplemented and control groups. Forty-four diabetic patients and 34 normal subjects were supplemented with 50 mg zinc gluconate daily for 4 weeks. Dietary intakes of participants were measured for two non-consecutive days by 24-hour recall method. Nutritional status of zinc and copper were also evaluated by biochemical measurement of fasting plasma samples and spot urinary collection. At baseline, diabetic patients showed significantly lower levels of dietary zinc intake and higher urinary zinc excretion than the normal adult group(p<0.05, p<0.0001). Plasma level of zinc was not significantly different between diabetic and normal adults at baseline. However, plasma zinc level increased significantly in both diabetic patients and normal adults after zinc supplementation. The changes in plasma copper levels following zinc supplementation were not statistically significant in diabetic subjects as well as in normal adults. These results indicated that four weeks of zinc supplementation did not influence Cu status and that it may contribute to improving the zinc status. Therefore, we suggest that Zn supplementation for a short-term period may improve marginal zinc status of diabetic patients without interfering with their copper status

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.246-256
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• 2022

Recently, parents and teachers consider physical education as a minor subject for students in elementary and secondary schools. Physical education performance has become increasingly significant as parents and schools pay more attention to physical schooling. The sports mining with distribution analysis model considers different factors, including the games, comments, conversations, and connection made on numerous sports interests. Using different machine learning/deep learning approach, children's athletic and academic interests can be tracked over the course of their academic lives. There have been a number of studies that have focused on predicting the success of students in higher education. Sports interest prediction research at the secondary level is uncommon, but the secondary level is often used as a benchmark to describe students' educational development at higher levels. An Automated Student Interest Prediction on Sports Mining using DL Based Bi-directional Long Short-Term Memory model (BiLSTM) is presented in this article. Pre-processing of data, interest classification, and parameter tweaking are all the essential operations of the proposed model. Initially, data augmentation is used to expand the dataset's size. Secondly, a BiLSTM model is used to predict and classify user interests. Adagrad optimizer is employed for hyperparameter optimization. In order to test the model's performance, a dataset is used and the results are analysed using precision, recall, accuracy and F-measure. The proposed model achieved 95% accuracy on 400^th instances, where the existing techniques achieved 93.20% accuracy for the same. The proposed model achieved 95% of accuracy and precision for 60%-40% data, where the existing models achieved 93% for accuracy and precision.

• 한국수자원학회논문집
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• 제54권9호
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• pp.667-680
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• 2021

이 연구의 목적은 서리 발생일과 무상일 기간의 특성을 분석하고 로지스틱 회귀, 랜덤 포레스트, Long-short Term Memory (LSTM) 기법을 활용하여 서리발생 예측모델을 개발하고 평가하는데 있다. 수원, 청주, 광주 지점에서 봄철과 가을철 서리발생 예측모델 개발을 위한 기상변수들을 수집하였으며, 수집기간은 1973년부터 2019년까지이다. 프리시전(precision), 리콜(Recall), f-1 스코어와, AUC 및 Reliability Diagram과 같은 그래피컬 평가기법을 이용해 서리발생 예측모델을 평가하였다. 봄철과 가을철 모두 서리발생일이 줄어드는 경향성(유의수준: 0.01)을 보였다. 0.9 이상의 높은 AUC 값에도 불구하고, 신뢰도는 일정한 값을 보여주지는 않았다. 서리발생일 측뿐만 아니라, 초상일과 종상일을 정확히 예측할 수 있도록 모형 개선이 필요해 보이며, 다른 지역의 더 많은 지점에서 동일한 기법을 적용해 보는 연구가 필요해 보인다.

• 생물정신의학
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• 제8권2호
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• pp.258-265
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• 2001

Objectives : Chronic alcohol consumption has been known to result in various neurocognitive deficits. Many neuropsychological studies revealed that the major disturbances occurred in the executive function, learning and short-term memory, visuospatial performance function, perceptuo-motor skills, and abstraction and problem solving abilities. This study was done to identify which cognitive areas might be mainly affected. Methods : The cognitive disturbance was evaluated using the Korean Version of the Mini Mental State Examination(MMSEK) and the 7 Minute Screen(7MS) in male inpatients with alcohol dependence(N=3 : as well as in age and education level matched healthy male controls(N=30). Four individual tests of the 7MS were consisted of the Benton Temporal Orientation Test, the Enhanced Cued Recall, the Clock Drawing and the Category Fluency. Results : 1) The average scores of four individual test of the 7MS for the alcoholics were $2.77{\pm}4.38$ for the Benton Temporal Orientation Test, $13.90{\pm}2.02$ for the Memory Test(the Cued Recall $6.77{\pm}1.94$, the Uncued Recall $7.10{\pm}2.45$), $5.84{\pm}1.86$ for the Clock Drawing, and $12.58{\pm}3.29$ for the Category Fluency. Except the Benton Temporal Orientation Test, there were statistically significant differences between test scores of alcoholics and those of controls(p<0.01). 2) The alcoholics who had MMSE-K score <24 were 9.68%. The average(${\pm}S.D.$) score of the MMSE-K for the patient group($27.23{\pm}2.62$) was significantly(p<0.001) lower than that of the healthy controls($29.20{\pm}1.24$). There were no statistically significant differences between four individual test scores of the 7MS of alcoholics with the MMSE-K score <24(N=3) and those of alcoholics with the MMSE-K score ${\geq}24$(N=28). 3) Four individual test scores of the 7MS seemed to have statistically significant association with such variables as MMSE-K, duration of alcohol drinking, blood magnesium concentration, liver function and thyroid function. Conclusion : Mild deficits of cognitive areas such as orientation, memory, visuospatial abilities and verbal fluency could be found in alcohol dependence.

• 대한간호학회지
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• 제39권1호
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• pp.107-115
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• 2009

Purpose: The purpose of this study was to identify the effects of an exercise program on frontal lobe cognitive function in seniors. Methods: The participants were 42 seniors using a health center in Seoul (experimental group) and 28 seniors using a facility for elders in Seoul (control group). The exercise program was carried out for 16 weeks from April to August 2007. The frontal lobe cognitive function, which includes short term memory, attention, immediate memory, delayed memory, verbal fluency and motor function, was measured by the Digit Span Forward test, Trail Making test, Immediate recall words test, Delayed recall words, Controlled oral word association test and Finger tapping test. The collected data were analyzed by Fisher's exact test, Chi-square, t-test, and ANCOVA using the SAS program. Results: The major findings of this study were as follows: Attention (p=.009), immediate memory (p=.005), delayed memory (p=.009), and verbal fluency (p=.004) improved after the exercise program. Conclusion: In this study, the exercise program was effective in improving frontal lobe cognitive function in elders. So it provides basic information for further nursing education on exercise programs which will be effective for prevention of early cognitive function decline in normally aging elders.

• Journal of Information Science Theory and Practice
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• 제12권2호
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• pp.64-78
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• 2024

Sentiment analysis is one of the promising approaches for developing a point of interest (POI) recommendation system. It uses natural language processing techniques that deploy expert insights from user-generated content such as reviews and feedback. By applying sentiment polarities (positive, negative, or neutral) associated with each POI, the recommendation system can suggest the most suitable POIs for specific users. The proposed study combines two models for POI recommendation. The first model uses bidirectional long short-term memory (BiLSTM) to predict sentiments and is trained on an election dataset. It is observed that the proposed model outperforms existing models in terms of accuracy (99.52%), precision (99.53%), recall (99.51%), and F1-score (99.52%). Then, this model is used on the Foursquare dataset to predict the class labels. Following this, user and POI embeddings are generated. The next model recommends the top POIs and corresponding coordinates to the user using the LSTM model. Filtered user interest and locations are used to recommend POIs from the Foursquare dataset. The results of our proposed model for the POI recommendation system using sentiment analysis are compared to several state-of-the-art approaches and are found quite affirmative regarding recall (48.5%) and precision (85%). The proposed system can be used for trip advice, group recommendations, and interesting place recommendations to specific users.

• 대한원격탐사학회지
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• 제38권6_2호
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• pp.1677-1689
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• 2022

대기오염은 동식물의 건강에 장·단기적으로 해로운 영향을 미치는 사회적 문제이다. 굴뚝은 대기를 오염시키는 대기오염물질의 주배출원으로 그 위치와 종류를 탐지하고 모니터링할 필요가 있다. 대기오염물질을 배출하는 굴뚝이 위치한 발전소 및 산업단지는 접근성이 많이 떨어지고 부지가 넓어 직접 모니터링하기에는 비용적, 시간적으로 비효율적이다. 따라서 최근에는 원격탐사 자료를 이용하여 굴뚝을 탐지하는 연구가 수행되고 있다. 본 연구에서는 중국 베이징, 톈진 허베이 성에 위치한 발전소를 대상으로 구축된 BUAA-FFPP60 오픈 데이터 세트를 활용하여 YOLOv5기반의 굴뚝 탐지 모델을 제작하였다. 탐지 모델의 성능을 향상시키기 위하여 데이터 분할과 데이터 증강기법을 적용하였으며, 최적의 모델 제작을 위한 학습 전략을 세웠다. 학습이 완료된 모델은 precision, recall과 같은 각종 지표를 통해 성능을 확인하였으며, 최종적으로 동일한 데이터 세트를 사용한 기존 연구와의 비교를 통해 모델의 성능을 평가하였다.

• Restorative Dentistry and Endodontics
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• 제46권2호
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• pp.29.1-29.11
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• 2021

Objectives: This study was conducted to compare the post-fracture survival rate of endodontically treated molar endodontically treated teeth (molar ETT) restored with resin composites or crowns and to identify potential risk factors, using a retrospective cohort design. Materials and Methods: Dental records of molar ETT with crowns or composite restorations (recall period, 2015-2019) were collected based on inclusion and exclusion criteria. The incidence of unrestorable fractures was identified, and molar ETT were classified according to survival. Information on potential risk factors was collected. Survival rates and potential risk factors were analyzed using the Kaplan-Meier log-rank test and Cox regression model. Results: The overall survival rate of molar ETT was 87% (mean recall period, 31.73 ± 17.56 months). The survival rates of molar ETT restored with composites and crowns were 81.6% and 92.7%, reflecting a significant difference (p < 0.05). However, ETT restored with composites showed a 100% survival rate if only 1 surface was lost, which was comparable to the survival rate of ETT with crowns. The survival rates of ETT with composites and crowns were significantly different (97.6% vs. 83.7%) in the short-term (12-24 months), but not in the long-term (> 24 months) (87.8% vs. 79.5%). Conclusions: The survival rate from fracture was higher for molar ETT restored with crowns was higher than for ETT restored with composites, especially in the first 2 years after restoration. Molar ETT with limited tooth structure loss only on the occlusal surface could be successfully restored with composite restorations.

• 한국주조공학회지
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• 제41권6호
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• pp.521-527
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• 2021

본 연구는 고압 다이캐스팅 공정에서 제품 결함을 사전에 예측하기 위한 기계 학습 기반의 공정 관리 모델 개발에 관한 연구이다. 모델은 이전 사이클에서의 온도를 입력받고, 사이클에 걸쳐서 나타나는 특징을 인식하여 다음 사이클의 결함 발생 여부를 예측한다. 기어 박스 형상에 대하여 제안된 알고리즘을 적용하여, 3 사이클의 정보를 통해서 98 .9%의 정확도와 96.8 %의 재현율로 제품 수축 결함을 사전에 예측하였다.