• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.746-764
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• 2022

Communication breakdown has been mentioned causing a heavy burden for dementia caregivers. This study aims to inspect and structure the process and results of communication between people with dementia and their caregivers. The impeding/facilitating elements of communication are also extracted. Interviews with 21 of dementia care experts about the direct and indirect experiences of communication with people with dementia were analyzed based on the grounded theory. Results show that combination of the cognitive and communication decline of the people with dementia, confusing environment and caregivers' inappropriate attitude and lack of communication skills leads to communication breakdown and relations severance. Minimal contacts and task-oriented conversation results in conflicts and people with dementia's increasing agitation, anxiety and violent behaviors while understanding of individuality and listening with heart lead to recovered lucidity in the state of serious dementia, recovered pleasure and voluntary participation in the daily activities for people with dementia. Core paradigm was defined as 'Person Centered Care through relation formation'. There are 4 types of communication with people with dementia : partnering, patronizing, conflicting, avoiding types. Researchers suggest that Person Centered based communication skills be educated and trained for dementia caregivers.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.407-419
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• 2022

The semiconductor fabrication process is complex and time-consuming. There are sometimes errors in the process, which results in defective die on the wafer bin map (WBM). We can detect the faulty WBM by finding some patterns caused by dies. When one manually seeks the failure on WBM, it takes a long time due to the enormous number of WBMs. We suggest a two-step approach to discover the probable pattern on the WBMs in this paper. The first step is to separate the normal WBMs from the defective WBMs. We adapt a hierarchical clustering for de-noising, which nicely performs this work by wisely tuning the number of minimum points and the cutting height. Once declared as a faulty WBM, then it moves to the next step. In the second step, we classify the patterns among the defective WBMs. For this purpose, we extract features from the WBM. Then machine learning algorithm classifies the pattern. We use a real WBM data set (WM-811K) released by Taiwan semiconductor manufacturing company.

• Journal of Platform Technology
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• v.10 no.4
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• pp.47-55
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• 2022

Recently, diverse devices using different wireless technologies are gradually increasing in the IoT environment. In particular, it is essential to design an efficient feature extraction approach and detect the exact types of radio signals in order to accurately identify various radio signal modulation techniques. However, it is difficult to gather labeled wireless signal in a real environment due to the complexity of the process. In addition, various learning techniques based on deep learning have been proposed for wireless signal classification. In the case of deep learning, if the training dataset is not enough, it frequently meets the overfitting problem, which causes performance degradation of wireless signal classification techniques using deep learning models. In this paper, we propose a generative adversarial network(GAN) based on data augmentation techniques to improve classification performance when various wireless signals exist. When there are various types of wireless signals to be classified, if the amount of data representing a specific radio signal is small or unbalanced, the proposed solution is used to increase the amount of data related to the required wireless signal. In order to verify the validity of the proposed data augmentation algorithm, we generated the additional data for the specific wireless signal and implemented a CNN and LSTM-based wireless signal classifier based on the result of balancing. The experimental results show that the classification accuracy of the proposed solution is higher than when the data is unbalanced.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.471-480
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• 2023

Purpose: This study intends to present a future research direction for military education by identifying and analyzing research trends in the field of education related to soldiers in academic research published in Korea. Method: In domestic academic papers published from 2000 to 2022, 269 final papers were selected from among the papers conducted on the subjects of 'soldiers' and 'education'. The final selected literature was analyzed by categorizing it into year, research subject, research method type, number of researchers, research topic, and research content. Result: First, since 2010, the number of studies has gradually increased, and 31 articles were published in 2021, with the largest number of studies. Second, the largest number of studies were conducted on the entire military, followed by the Army and Nursing Officers. Third, qualitative research methods were used a little more in research methods. Fourth, the number of researchers with two or more researchers was steadily increasing. Fifth, most of the research topics were development, design, and improvement. Sixth, in terms of detailed research contents, most of the studies were related to soldiers' spirit(mental force) education and military education and training. Conclusion: Through this study, it was confirmed that military education was studied in various subjects. The direction of future research should be in-depth research from various perspectives with a lot of interest in military education and safety.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.355-364
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• 2023

As advanced cyber threats continue to increase in recent years, it is difficult to detect new types of cyber attacks with existing pattern or signature-based intrusion detection method. Therefore, research on anomaly detection methods using data learning-based artificial intelligence technology is increasing. In addition, supervised learning-based anomaly detection methods are difficult to use in real environments because they require sufficient labeled data for learning. Research on an unsupervised learning-based method that learns from normal data and detects an anomaly by finding a pattern in the data itself has been actively conducted. Therefore, this study aims to extract a latent vector that preserves useful sequence information from sequence log data and develop an anomaly detection learning model using the extracted latent vector. Word2Vec was used to create a dense vector representation corresponding to the characteristics of each sequence, and an unsupervised autoencoder was developed to extract latent vectors from sequence data expressed as dense vectors. The developed autoencoder model is a recurrent neural network GRU (Gated Recurrent Unit) based denoising autoencoder suitable for sequence data, a one-dimensional convolutional neural network-based autoencoder to solve the limited short-term memory problem that GRU can have, and an autoencoder combining GRU and one-dimensional convolution was used. The data used in the experiment is time-series-based NGIDS (Next Generation IDS Dataset) data, and as a result of the experiment, an autoencoder that combines GRU and one-dimensional convolution is better than a model using a GRU-based autoencoder or a one-dimensional convolution-based autoencoder. It was efficient in terms of learning time for extracting useful latent patterns from training data, and showed stable performance with smaller fluctuations in anomaly detection performance.

• Journal of Music and Human Behavior
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• v.19 no.2
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• pp.81-103
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• 2022

The effects of therapeutic instrumental music performance (TIMP) using a ukulele were examined in adults with central nervous system damage and impaired hand functions. The participants were three adults with neurological damage who participated in 30-min sessions twice a week over 6 weeks. Changes in hand function was measured by the Box and Block Test (BBT), the 9-Hole Peg Test (9-HPT), and the Jebsen-Taylor Hand Function Test (JTHFT). Following the intervention, all three participants showed increases in the BBT and 9-HPT scores, indicating positive changes in fine motor coordination and dexterity. In terms of the JTHFT, all three participants showed increases in the "writing" and "card flipping" subtask scores, indicating that the intervention was effective in improving more coordinated finger movements. All participants reported the satisfaction with the intervention. They also pointed out that they were motivated to play the ukulele and that following the intervention used their affected hand more frequently in daily activities. These findings suggest that TIMP with a ukulele for patients with central nervous system damage can have positive effects on their functional hand movements and motivate these patients to practice their rehabilitation exercises.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.930-938
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• 2023

Maritime English is a specialized language system designed for ship operations, maritime safety, and external and internal communication onboard. According to the International Maritime Organization's (IMO) International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW), it is imperative that navigational officers engaged in international voyages have a thorough understanding of Maritime English including the use of Standard Marine Communication Phrases (SMCP). This study measured students' proficiency in Maritime English using a learning and testing platform that includes voice recognition, translation, and word entry tasks to evaluate the resulting improvement in Maritime English exam scores. Furthermore, the study aimed to investigate the level of platform use needed for cadets to qualify as junior navigators. The experiment began by examining the correlation between students' overall English skills and their proficiency in SMCP through an initial test, followed by the evaluation of improvements in their scores and changes in exam duration during the mid-term and final exams. The initial test revealed a significant dif erence in Maritime English test scores among groups based on individual factors, such as TOEIC scores and self-assessment of English ability, and both the mid-term and final tests confirmed substantial score improvements for the group using the platform. This study confirmed the efficacy of a learning platform that could be extensively applied in maritime education and potentially expanded beyond the scope of Maritime English education in the future.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.55-58
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• 2024

최근 각 기업의 AI 면접시스템 도입이 증가하고 있으며, AI 면접에 대한 실효성 논란 또한 많은 상황이다. 본 논문에서는 AI 면접 과정에서 지원자를 평가하는 방식을 시각, 음성, 자연어처리 3영역에서 구현함으로써, 면접 지원자를 다방면으로 분석 방법론의 적절성에 대해 평가하고자 한다. 첫째, 시각적 측면에서, 면접 지원자의 감정을 인식하기 위해, 합성곱 신경망(CNN) 기법을 활용해, 지원자 얼굴에서 6가지 감정을 인식했으며, 지원자가 카메라를 응시하고 있는지를 시계열로 도출하였다. 이를 통해 지원자가 면접에 임하는 태도와 특히 얼굴에서 드러나는 감정을 분석하는 데 주력했다. 둘째, 시각적 효과만으로 면접자의 태도를 파악하는 데 한계가 있기 때문에, 지원자 음성을 주파수로 환산해 특성을 추출하고, Bidirectional LSTM을 활용해 훈련해 지원자 음성에 따른 6가지 감정을 추출했다. 셋째, 지원자의 발언 내용과 관련해 맥락적 의미를 파악해 지원자의 상태를 파악하기 위해, 음성을 STT(Speech-to-Text) 기법을 이용하여 텍스트로 변환하고, 사용 단어의 빈도를 분석하여 지원자의 언어 습관을 파악했다. 이와 함께, 지원자의 발언 내용에 대한 감정 분석을 위해 KoBERT 모델을 적용했으며, 지원자의 성격, 태도, 직무에 대한 이해도를 파악하기 위해 객관적인 평가지표를 제작하여 적용했다. 논문의 분석 결과 AI 면접의 다면적 평가시스템의 적절성과 관련해, 시각화 부분에서는 상당 부분 정확도가 객관적으로 입증되었다고 판단된다. 음성에서 감정분석 분야는 면접자가 제한된 시간에 모든 유형의 감정을 드러내지 않고, 또 유사한 톤의 말이 진행되다 보니 특정 감정을 나타내는 주파수가 다소 집중되는 현상이 나타났다. 마지막으로 자연어처리 영역은 면접자의 발언에서 나오는 말투, 특정 단어의 빈도수를 넘어, 전체적인 맥락과 느낌을 이해할 수 있는 자연어처리 분석모델의 필요성이 더욱 커졌음을 판단했다.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.321-332
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• 2024

Understanding the status of surface cover in riparian zones is essential for river management and flood disaster prevention. Traditional survey methods rely on expert interpretation of vegetation through vegetation mapping or indices. However, these methods are limited by their ability to accurately reflect dynamically changing river environments. Against this backdrop, this study utilized satellite imagery to apply the Random Forest method to assess the distribution of vegetation in rivers over multiple years, focusing on the Naeseong Stream as a case study. Remote sensing data from Sentinel-2 imagery were combined with ground truth data from the Naeseong Stream surface cover in 2016. The Random Forest machine learning algorithm was used to extract and train 1,000 samples per surface cover from ten predetermined sampling areas, followed by validation. A sensitivity analysis, annual surface cover analysis, and accuracy assessment were conducted to evaluate their applicability. The results showed an accuracy of 85.1% based on the validation data. Sensitivity analysis indicated the highest efficiency in 30 trees, 800 samples, and the downstream river section. Surface cover analysis accurately reflects the actual river environment. The accuracy analysis identified 14.9% boundary and internal errors, with high accuracy observed in six categories, excluding scattered and herbaceous vegetation. Although this study focused on a single river, applying the surface cover classification method to multiple rivers is necessary to obtain more accurate and comprehensive data.

• Journal of Yeungnam Medical Science
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• v.6 no.2
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• pp.173-181
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• 1989

The effect of interpregnancy interval on birthweight of the subsequent child was investigated for the 1,347 womens of 25 to 40 years old age who visited OBGY and Pediatric department of the general hospital in Taegu city. Questions in designed questionnaire were asked by student interviewers who were trained in nursing school. Mean birth weight by interpregnancy intervals were compared by the intervals of 6 months. Mean birth weight increased from 3,250 grams for intervals of 6 months to 3,357 grams for intervals of 25-30 months, hut the difference was not statistically significant(=0.47). Correlations between the continuous variables which were suspected as con founders and interpregnancy interval and birth weight were investigated. The coefficient of correlation between maternal age and interpregnancy interval was 0.39, between gestational period and birth weight 0.30 and between prepregnant weight and birth weight 0.16 and between birth weight of first baby and birth weight(of second baby) 0.44. But maternal age, gestational period and prepregnant weight were not considered as confounder, because they were not correlated simultaneously with birth interval and birth weight. Associations between the discrete variables which were suspected as confounders, and interpregnancy interval were investigated by Chi-square test. Associations between interpregnancy interval and educational level of mothers, types of husband's occupation, types of medical security, sex were not significant(P-values were 0.59, 0.75, 0.75, 0.82 respectively), so we did not considered these variables as confounding variables. In multiple regression analysis of birth weight, significant variables were birth weight of first baby, gestational period, sex of neonate and prepregnancy body weight of mother. Of the 1,347 births, the rate of low birth weight was 2% (27 birth). The rate for interpregnancy interval 7-12 months was highest as 3.6% and that for 13-18 months was lowest as 0.6%, but there was no regular tendency related with interpregnancy interval.