• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2018년도 제30회 한글 및 한국어 정보처리 학술대회
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• pp.443-448
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• 2018

웹검색 결과의 품질 향상을 위해서는 질의의 정확한 매칭 뿐만이 아니라, 서로 같은 대상을 지칭하는 한글 문자열과 영문 문자열(예: 네이버-naver)의 매칭과 같은 유연한 매칭 또한 중요하다. 본 논문에서는 문장대문장 학습을 통해 영문 문자열을 한글 문자열로 음차변환하는 방법론을 제시한다. 또한 음차변환 결과로 얻어진 한글 문자열을 동일 영문 문자열의 다양한 음차변환 결과와 매칭시킬 수 있는 발음 유사성 기반 부분 매칭 방법론을 제시하고, 위키피디아의 리다이렉트 키워드를 활용하여 이들의 성능을 정량적으로 평가하였다. 이를 통해 본 논문은 문장대문장 학습 기반의 음차 변환 결과가 복잡한 문맥을 고려할 수 있으며, Damerau-Levenshtein 거리의 계산에 자모 유사도를 활용하여 기존에 비해 효과적으로 한글 키워드들 간의 부분매칭이 가능함을 보였다.

• ETRI Journal
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• 제43권5호
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• pp.775-786
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• 2021

To translate in real time, a simultaneous translation system should determine when to stop reading source tokens and generate target tokens corresponding to a partial source sentence read up to that point. However, conventional attention-based neural machine translation (NMT) models cannot produce translations with adequate latency in online scenarios because they wait until a source sentence is completed to compute alignment between the source and target tokens. To address this issue, we propose a reinforced learning (RL)-based attention mechanism, the reinforced attention mechanism, which allows a neural translation model to jointly train the stopping criterion and a partial translation model. The proposed attention mechanism comprises two modules, one to ensure translation quality and the other to address latency. Different from previous RL-based simultaneous translation systems, which learn the stopping criterion from a fixed NMT model, the modules can be trained jointly with a novel reward function. In our experiments, the proposed model has better translation quality and comparable latency compared to previous models.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.489-489
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• 2000

Recently, Reinforcement Learning(RL) methods have been used far teaming problems in Partially Observable Markov Decision Process(POMDP) environments. Conventional RL-methods, however, have limited applicability to POMDP To overcome the partial observability, several algorithms were proposed [5], [7]. The aim of this paper is to extend our previous algorithm for POMDP, called Labeling Q-learning(LQ-learning), which reinforces incomplete information of perception with labeling. Namely, in the LQ-learning, the agent percepts the current states by pair of observation and its label, and the agent can distinguish states, which look as same, more exactly. Labeling is carried out by a hash-like function, which we call Labeling Function(LF). Numerous labeling functions can be considered, but in this paper, we will introduce several labeling functions based on only 2 or 3 immediate past sequential observations. We introduce the basic idea of LQ-learning briefly, apply it to maze problems, simple POMDP environments, and show its availability with empirical results, look better than conventional RL algorithms.

• 대한안전경영과학회지
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• 제16권2호
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• pp.175-184
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• 2014

This paper examines how learning orientation and self-efficacy contributed to explaining innovation behaviors. In order to verify the relationships and mediating effect, data were collected from 368 individuals in employees working in small and medium-sized firms at Gyeongnam region to test theoretical model and its hypotheses. All data collected from the survey were analyzed using with SPSS 18.0. This study reports findings as follows: first, the relationship between the learning orientation and the employee's self-efficacy is positively related. Second, there was also a positive correlation between the employee's self-efficacy and the innovation behaviors. Third, the relationship between the learning orientation and the innovation behaviors is positively related. Finally, the employee's self-efficacy played as a partial mediator on the relationship between learning orientation and innovation behaviors. Based on these findings, the implications and the limitations of the study were presented including some directions for future studies.

• 한국전기전자재료학회논문지
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• 제19권2호
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• pp.162-168
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• 2006

Recently, diagnosis techniques have been investigated to detect a Partial Discharge associated with a dielectric material defect in a high voltage electrical apparatus, However, the properties of detection technique of Partial Discharge aren't completely understood because the physical process of Partial Discharge. Therefore, this paper analyzes the process on surface discharge of polymer insulator using wavelet transform. Wavelet transform provides a direct quantitative measure of spectral content in the time~frequency domain. As it is important to develop a non-contact method for detecting the kaolin contamination degree, this research analyzes the electromagnetic waves emitted from Partial Discharge using wavelet transform. This result experimentally shows the process of Partial Discharge as a two-dimensional distribution in the time-frequency domain. Feature extraction parameter namely, maximum and average of wavelet coefficients values, wavelet coefficients value at the point of $95\%$ in a histogram and number of maximum wavelet coefficient have used electromagnetic wave signals as input signals in the preprocessing process of neural networks in order to identify kaolin contamination rates. As result, root sum square error was produced by the test with a learning of neural networks obtained 0.00828.

• 디지털산업정보학회논문지
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• 제16권4호
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• pp.111-121
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• 2020

This study analyzed the effects on system quality, learning commitment, and satisfaction in a mobile environment. A survey was conducted on 192 students enrolled in K University, and the research results are as follows. first, it was found that usefulness had an effect on learning commitment, but connectivity and reliability did not affect learning commitment. second, it was found that the usefulness and connectivity of the system quality had a significant effect on the satisfaction of use. third, the intermediary effect of learning immersion was verified as the connectivity, reliability, and usefulness of the system quality influence the satisfaction of use. connectivity and reliability had no mediating effect of learning commitment, and usefulness was found to play a partial mediating role in affecting user satisfaction. This study is meaningful in that it can provide a plan for improving the quality management of mobile learning and improving the learning effect by analyzing the effects on mobile learning from multiple perspectives.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.302-310
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• 2022

Efficient use of limited blood products is becoming very important in terms of socioeconomic status and patient recovery. To predict the appropriateness of patient-specific transfusions for the intensive care unit (ICU) patients who require real-time monitoring, we evaluated a model to predict the possibility of transfusion dynamically by using the Medical Information Mart for Intensive Care III (MIMIC-III), an ICU admission record at Harvard Medical School. In this study, we developed an explainable machine learning to predict the possibility of red blood cell transfusion for major medical diseases in the ICU. Target disease groups that received packed red blood cell transfusions at high frequency were selected and 16,222 patients were finally extracted. The prediction model achieved an area under the ROC curve of 0.9070 and an F1-score of 0.8166 (LightGBM). To explain the performance of the machine learning model, feature importance analysis and a partial dependence plot were used. The results of our study can be used as basic data for recommendations related to the adequacy of blood transfusions and are expected to ultimately contribute to the recovery of patients and prevention of excessive consumption of blood products.

• Asia pacific journal of information systems
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• 제12권3호
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• pp.45-70
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• 2002

This paper examines the role of distance learning self-efficacy, belief in one's capabilities of using a system in the accomplishment of web-based distance learning, in predicting user intention to use and performance of distance learning system. It used self-efficacy theory and technology acceptance model(TAM) to build a model that predicts relationships between antecedents to students' distance learning self-efficacy assessments and their behavioral and attitudinal consequences. The model was tested using LISREL analysis on the sample of 250 students who have worked with the Distance Learning System. The results indicated partial support for the conceptual model. In accordance with TAM, perceived usefulness had strong direct effects on intention to use and performance, while perceived ease of use had both direct and indirect effects on intention to use, but not performance. Distance learning self-efficacy had only direct effect on perceived ease of use to use. Computer experience was found to have a strong positive effect on distance learning self-efficacy, and computer anxiety had a negative effect on distance learning self-efficacy. Implications of these findings are discussed for researchers and practitioners.

• 한국간호교육학회지
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• 제27권1호
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• pp.80-90
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• 2021

Purpose: This study aimed to investigate nursing students' learning agility and confirm the mediating effect of self-leadership in the relationship between achievement motivation and learning agility. Methods: The study design was a descriptive survey design. The subjects were third- and fourth-year nursing students attending three universities in one region. Data were collected from November 28, 2019, to May 25, 2020, and a total of 202 data were collected using the scale of achievement motivation, self-leadership, and learning agility. Data analysis included frequency analysis, descriptive statistics, and Pearson's correlation coefficient using SPSS 25.0 statistics 25.0 software. The mediating effect of self-leadership was analyzed through regression analysis and bootstrapping using process macro ver. 3.4.1. Results: Self-leadership's partial mediating effect was confirmed in achievement motivation and learning agility. Achievement motivation was found to affect directly learning agility, with an indirect effect through self-leadership. Conclusion: The study results showed that nursing students could increase their learning agility through self-leadership improvement. Future research should focus on identifying the factors influencing nursing students' learning agility and develop and apply programs to improve learning agility.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.73-78
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• 2022

The aim of this paper is to investigate the prevalence of Deep Learning in the literature on Fire & Rescue Service. It is found that deep learning techniques are only beginning to benefit the firefighters. The popular areas where deep learning techniques are making an impact are situational awareness, decision making, mental stress, injuries, well-being of the firefighter such as his sudden fall, inability to move and breathlessness, path planning by the firefighters while getting to an fire scene, wayfinding, tracking firefighters, firefighter physical fitness, employment, prediction of firefighter intervention, firefighter operations such as object recognition in smoky areas, firefighter efficacy, smart firefighting using edge computing, firefighting in teams, and firefighter clothing and safety. The techniques that were found applied in firefighting were Deep learning, Traditional K-Means clustering with engineered time and frequency domain features, Convolutional autoencoders, Long Short-Term Memory (LSTM), Deep Neural Networks, Simulation, VR, ANN, Deep Q Learning, Deep learning based on conditional generative adversarial networks, Decision Trees, Kalman Filters, Computational models, Partial Least Squares, Logistic Regression, Random Forest, Edge computing, C5 Decision Tree, Restricted Boltzmann Machine, Reinforcement Learning, and Recurrent LSTM. The literature review is centered on Firefighters/firemen not involved in wildland fires. The focus was also not on the fire itself. It must also be noted that several deep learning techniques such as CNN were mostly used in fire behavior, fire imaging and identification as well. Those papers that deal with fire behavior were also not part of this literature review.