• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.4
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• pp.491-505
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• 2019

This study analyzes global fashion clusters to provide insight for the domestic fashion market to form a competitive fashion cluster. We examine formation factors and operation types of the global fashion clusters to understand their characteristics. We also explore the effectiveness of each global fashion cluster by investigating the stage of development in fashion clusters. Fourteen cases of global fashion clusters are collected and analyzed. First, global fashion clusters show three types of formation and operation: self-formation, self-formation & government based development, and government, institute, and enterprise based formation & development. Second, the characteristics of global fashion clusters are based on functions related to space, learning, innovation, network, and knowledge. Third, there are four steps in the development stage of global fashion clusters: professional clusters, industrial clusters, learning clusters and innovative industrial clusters. In particular, innovative industrial clusters, the final stage of development, have high levels of effectiveness in terms of co-growth and collaboration among fashion-related businesses in fashion clusters. The results of this study can help guide the development of local fashion cluster in Korea.

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.61-66
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• 2022

Object classification is one of the main fields in neural networks and has attracted the interest of many researchers. Although there have been vast advancements in this area, still there are many challenges that are faced even in the current era due to its inefficiency in handling large data, linguistic and dimensional complexities. Powerful hardware and software approaches in Neural Networks such as Deep Neural Networks present efficient mechanisms and contribute a lot to the field of object recognition as well as to handle time series classification. Due to the high rate of accuracy in terms of prediction rate, a neural network is often preferred in applications that require identification, segmentation, and detection based on features. Neural networks self-learning ability has revolutionized computing power and has its application in numerous fields such as powering unmanned self-driving vehicles, speech recognition, etc. In this paper, the experiment is conducted to implement a neural approach to identify numbers in different formats without human intervention. Measures are taken to improve the efficiency of the machines to classify and identify numbers. Experimental results show the importance of having training sets to achieve better recognition accuracy.

• Journal of Agricultural Extension & Community Development
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• v.29 no.1
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• pp.19-31
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• 2022

This study aims to examine the moderating effect of regional agricultural leader's capacity between the sense of community of female farmers and the resident participation. A survey was conducted on 312 female farmers from October 20 to November 19, 2020. The main results of the analysis are as follows. First, it showed that the higher the sense of community, the higher the awareness of resident participation. Second, it was found that the sense of community had a positive effect on resident participation as self-directed learning capability was higher, and that self-directed learning capability had a moderating effect on the relationship between the sense of community and the resident participation. Third, regional agricultural leadership capacity was found to have a moderating effect in the relationship between the sense of community and the resident participation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2787-2800
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• 2022

The aging society increases emergency situations of the elderly living alone and a variety of social crimes. In order to prevent them, techniques to detect emergency situations through voice are actively researched. This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data. In the proposed method, an audio file is converted into a spectrogram. In this way, it is possible to use an algorithm for image data, such as CNN. After that, mutli-scale feature extraction is applied. Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged. In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved. Finally, CutPaste-based anomaly detection is conducted. Since the model is trained through self-supervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling. Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations. Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.353-360
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• 2021

음성 대화 시스템에서는 사용자가 잘못된 슬롯명을 말하거나 음성인식 오류가 발생해 사용자의 의도에 맞지 않는 응답을 하는 경우가 있다. 이러한 문제를 해결하고자 말뭉치나 사전 데이터를 활용한 질의 교정 방법들이 제안되지만, 이는 지속적으로 사람이 개입하여 데이터를 주입해야하는 한계가 있다. 본 논문에서는 축적된 로그 데이터를 활용하여 사람의 개입 없이 음악 재생에 필요한 슬롯을 교정하는 자기 학습(Self-learning) 기반의 모델을 제안한다. 이 모델은 사용자가 특정 음악을 재생하고자 유사한 질의를 반복하는 상황을 이용하여 비지도 학습 기반으로 학습하고 음악 재생에 실패한 슬롯을 교정한다. 그리고, 학습한 모델 결과의 정확도에 대한 불확실성을 해소하기 위해 질의 슬롯 관계 유사도 모델을 이용하여 교정 결과에 대한 검증을 하고 슬롯 교정 결과에 대한 안정성을 보장한다. 모델 학습을 위한 데이터셋은 사용자가 연속으로 질의한 세션 데이터로부터 추출하며, 음악 재생 슬롯 세션 데이터와 질의 슬롯 관계 유사도 데이터를 각각 구축하여 슬롯 교정 모델과 질의 슬롯 관계 유사도 모델을 학습한다. 교정된 슬롯을 분석한 결과 발음 정보가 유사한 슬롯 뿐만 아니라 의미적인 관계가 있는 슬롯으로도 교정하여 사전 기반 방식보다 다양한 유형의 교정이 가능한 것을 보였다. 3 개월 간 수집된 로그 데이터로 학습한 음악 재생 슬롯 교정 모델은 일주일 동안 반복한 고유 질의 기준, 음악 재생 실패의 12%를 개선하는 성능을 보였다.

• Journal of Korean Critical Care Nursing
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• v.16 no.2
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• pp.54-66
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• 2023

Purpose : Developing infection control capabilities during the COVID-19 pandemic was critical. This study aimed to develop a simulation program to control patients with COVID-19 in nursing students and examine the effects on COVID-19 knowledge, COVID-19 nursing intention, self-efficacy learning, and clinical performance. Methods : The study used nonequivalent control group pretest-posttest design. Sixty nursing students were recruited from two different colleges using purposive sampling. For the intervention group(n=30), the pretest was administered before the simulation program, involving six sessions of online lectures and simulation practices. Immediately, the posttest was conducted following the program. Results : COVID-19 knowledge (t=9.87, p <.001), COVID-19 nursing intention (t=4.45, p <.001), learning self-efficacy (t=6.49, p <.001), and clinical performance (t=6.77, p <.001) increased significantly after the program, revealing the positive effect of the COVID-19 infection control simulation program in nursing students. Conclusion : The results of the study and the curriculum may be used as practical evidence for COVID-19 infection control in nursing schools and medical institutions.

• Journal of Engineering Education Research
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• v.15 no.3
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• pp.12-21
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• 2012

The purpose of this study is to verify the effects of mastery goal orientation and time management ability on job search self-efficacy in the vocational education of engineering college students. A total of 52 samples were analyzed for this research. The result indicated that mastery goal orientation had positive effects on all sub-variables (job ability self-efficacy, career goal setting self-efficacy) of job search self-efficacy significantly. But time management ability had positive effects on career goal setting self-efficacy. And there are no significant differences in mean comparison of mastery goal orientation, time management ability, and job search self-efficacy according to gender and residence area. In addition, the interview results of engineering college students' perception of career, the understanding of vocational education, and job search self-efficacy were analyzed.

• Journal of Korean society of Dental Hygiene
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• v.20 no.5
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• pp.697-706
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• 2020

Objectives: The purpose of this study was to analyze the factors that affect the test anxiety among dental hygiene students, including mental health and academic self-efficacy. Methods: A self-administered questionnaire survey was conducted among dental hygiene students in a metropolitan area from July 21 to July 31, 2020, and finally, 196 copies were statistically analyzed using SPSS 21.0. Results: The mean values of mental health, academic self-efficacy, and test anxiety among the dental hygiene students were 1.47±0.41, 2.99±0.46, and 2.76±0.74, respectively. Mental health was negatively correlated with self-efficacy (r=-0.346, p<0.01) and positively correlated with test anxiety (r=0.405, p<0.01), while academic self-efficacy was negatively correlated with test anxiety (r=-0.424, p<0.001). The factors that affect test anxiety were somatization (p<0.05), anxiety (p<0.05), paranoia (p<0.05), task preference (p<0.05), and confidence (p<0.001), which are the detailed items of academic self-efficacy. Conclusions: It is necessary to develop and apply customized health programs suitable for individual students to improve their mental health, as well as develop teaching and learning methods that can improve academic selfefficacy, as mental health and academic self-efficacy are influential factors in test anxiety among dental hygiene students.

• Human Ecology Research
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• v.55 no.2
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• pp.193-204
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• 2017

This study examines the effect of 5-year-old children's mathematical problem solving ability and their self-esteem based on the Havruta method using math storybooks. The subjects of this study were 40 5-year-old students attending a kindergarten in the Incheon area: 20 students comprised the treatment group and 20 students comprised the control group. An instrument originally created by Ward (1993) but adapted by Hwang (1997) and later modified by Ryu (2003) was used to test the children's mathematical problem solving abilities. A modified version (Kim, 1997) of an instrument developed by Harter and Pike (1984) was used to measure children's self-esteem. Test results were analyzed using SPSS ver. 18.0 for Windows. The findings are as follows. First, the treatment group that had Havruta classes utilizing math story books was found to improve significantly more than the control group in their mathematical problem solving ability. Havruta classes had positive effects on children's mathematical problem solving abilities. Second, there was no significant difference found between the two groups in terms of self-esteem when the children's self-esteem was compared after Havruta classes that utilize math storybooks. It may not be possible to see immediate changes in children's self-esteem because positive parent and teacher feedback had the strongest influence on 5-year-old children's self-esteem, as opposed to self-learning. The results of this study provide meaningful basic data for Havruta classes that focus on questions and discussions through math story books to increase children's mathematical problem solving abilities in the child education field.