• Journal of Biomedical Engineering Research
• /
• v.45 no.2
• /
• pp.95-100
• /
• 2024

Vitiligo is a condition characterized by the destruction or dysfunction of melanin-producing cells in the skin, resulting in a loss of skin pigmentation. Facial vitiligo, specifically affecting the face, significantly impacts patients' appearance, thereby diminishing their quality of life. Evaluating the efficacy of facial vitiligo treatment typically relies on subjective assessments, such as the Facial Vitiligo Area Scoring Index (F-VASI), which can be time-consuming and subjective due to its reliance on clinical observations like lesion shape and distribution. Various machine learning and deep learning methods have been proposed for segmenting vitiligo areas in facial images, showing promising results. However, these methods often struggle to accurately segment vitiligo lesions irregularly distributed across the face. Therefore, our study introduces a framework aimed at improving the segmentation of vitiligo lesions on the face and providing an evaluation of vitiligo lesions. Our framework for facial vitiligo segmentation and lesion evaluation consists of three main steps. Firstly, we perform face detection to minimize background areas and identify the face area of interest using high-quality ultraviolet photographs. Secondly, we extract facial area masks and vitiligo lesion masks using a semantic segmentation network-based approach with the generated dataset. Thirdly, we automatically calculate the vitiligo area relative to the facial area. We evaluated the performance of facial and vitiligo lesion segmentation using an independent test dataset that was not included in the training and validation, showing excellent results. The framework proposed in this study can serve as a useful tool for evaluating the diagnosis and treatment efficacy of vitiligo.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.1
• /
• pp.41-49
• /
• 2024

Speech recognition technology is continuously advancing and widely used in various fields. In this study, we aimed to investigate the impact of speech data quality on speech recognition models by dividing the dataset into the entire dataset and the top 70% based on Signal-to-Noise Ratio (SNR). Utilizing Seamless M4T and Google Cloud Speech-to-Text, we examined the text transformation results for each model and evaluated them using the Levenshtein Distance. Experimental results revealed that Seamless M4T scored 13.6 in models using data with high SNR, which is lower than the score of 16.6 for the entire dataset. However, Google Cloud Speech-to-Text scored 8.3 on the entire dataset, indicating lower performance than data with high SNR. This suggests that using data with high SNR during the training of a new speech recognition model can have an impact, and Levenshtein Distance can serve as a metric for evaluating speech recognition models.

• The Journal of the Acoustical Society of Korea
• /
• v.29 no.3
• /
• pp.200-208
• /
• 2010

In this work, the minimum statistics (MS) algorithm is combined with the codebook driven short-term predictor parameter estimation (CDSTP) to design a speech enhancement algorithm that is robust against various background noise environments. The MS algorithm functions well for the stationary noise but relatively not for the non-stationary noise. The CDSTP works efficiently for the non-stationary noise, but not for the noise that was not considered in the training stage. Thus, we propose to combine CDSTP and MS. Compared with the single use of MS and CDSTP, the proposed method produces better perceptual evaluation of speech quality (PESQ) score, and especially works excellent for the mixed background noise between stationary and non-stationary noises.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
• /
• v.30 no.1
• /
• pp.1-13
• /
• 2024

Background: The purpose of this study was to investigate the effects of stabilization exercise performed after abdominal drawing exercise using pressure biofeedback for 8 weeks on pain level, performance of transverse abdominis, back pain disability index, and quality of life in women in their 30s less than one year after giving birth. Methods: A total of 20 women who voluntarily participated less than one year after giving birth were randomly divided into a control group and an experimental group. The control group was subjected to abdominal drawing exercise before lumbar stabilization exercise, and the experimental group was subjected to abdominal drawing exercise using pressure biofeedback before lumbar stabilization exercise thrice a week for eight weeks. The quadruple visual analog scale (QVAS), the performance of transverse abdominis, the Korean version of the Oswestry disability index (KDOI), the inventory of functional status after childbirth (IFSAC), and the Short Form-12 item (SF-12) were evaluated before and after the intervention. Results: Except for the Physical Components Summary Scale of SF-12, after the intervention, the experimental group showed significant improvement in QVAS, performance of Transverse abdominis , KDOI, and Mental Components Summary Scale of SF-12 compared to the control group. Conclusion: Selective deep muscle activation through abdominal drawing exercises using pressure biofeedback can help rehabilitation for women after postpartum.

• Quality Improvement in Health Care
• /
• v.29 no.2
• /
• pp.2-14
• /
• 2023

Purpose: This study aimed to identify strategies to enhance the competencies of patient safety coordinators in Korea. Methods: Fourteen participants from nine hospitals were interviewed between May and November 2022. Qualitative content analysis was used to analyze the data. Results: As for the strategies to enhance patient safety management competency, 3 themes and 11 sub-themes were derived. The first theme was 'Having individual competence as a patient safety coordinator', and the sub-themes were 'Communication skills with members', 'Flexible thinking from multiple perspectives', and 'Preparing for administrative work competencies that they had not experienced as a nurse.' The second theme was 'Responding strategically to promote improvement activities', and the sub-themes for it were 'Multi-angle approach to the problem', 'A careful approach so as not to be taken as criticism in the field', 'Increasing the possibility of improvement activities through awareness', 'Activating the network between patient safety coordinators', and 'Expanding learning opportunities through patient safety case analysis.' The third theme was 'Obtaining support to facilitate patient safety activities', and the sub-themes for this were 'Improving staff awareness of patient safety', 'Providing a training course for nurse professional of patient safety', and 'Expanding the manpower allocation standard of patient safety coordinators.' Conclusion: This study explored personal competencies such as document writing and computer utilization capabilities, focused on ways to improve the field of patient safety management, and emphasized the need for organizational and political support.

• The Journal of the Acoustical Society of Korea
• /
• v.43 no.2
• /
• pp.253-259
• /
• 2024

This paper proposes a new speech enhancement model based on a complex nested U-Net with a dual-branch decoder. The proposed model consists of a complex nested U-Net to simultaneously estimate the magnitude and phase components of the speech signal, and the decoder has a dual-branch decoder structure that performs spectral mapping and time-frequency masking in each branch. At this time, compared to the single-branch decoder structure, the dual-branch decoder structure allows noise to be effectively removed while minimizing the loss of speech information. The experiment was conducted on the VoiceBank + DEMAND database, commonly used for speech enhancement model training, and was evaluated through various objective evaluation metrics. As a result of the experiment, the complex nested U-Net-based speech enhancement model using a dual-branch decoder increased the Perceptual Evaluation of Speech Quality (PESQ) score by about 0.13 compared to the baseline, and showed a higher objective evaluation score than recently proposed speech enhancement models.

• Smart Media Journal
• /
• v.13 no.1
• /
• pp.9-17
• /
• 2024

This study aims to develop an integrated agricultural distribution network management system to improve the quality, profit, and decision-making efficiency of agricultural products. We adopt two key techniques: crop maturity detection based on the YOLOX target detection algorithm and market price prediction based on the Prophet model. By training the target detection model, it was possible to accurately identify crops of various maturity stages, thereby optimizing the shipment timing. At the same time, by collecting historical market price data and predicting prices using the Prophet model, we provided reliable price trend information to shipping decision makers. According to the results of the study, it was found that the performance of the model considering the holiday factor was significantly superior to that of the model that did not, proving that the effect of the holiday on the price was strong. The system provides strong tools and decision support to farmers and agricultural distribution managers, helping them make smart decisions during various seasons and holidays. In addition, it is possible to optimize the distribution network of agricultural products and improve the quality and profit of agricultural products.

• Journal of The Korean Society of Integrative Medicine
• /
• v.12 no.3
• /
• pp.101-120
• /
• 2024

Purpose : This study aimed to carry out a scoping review to investigate the research trends in non-pharmacological interventions for physical rehabilitation following breast cancer treatment. Methods : A scoping review was conducted according to the five steps outlined by Arksey and O'Malley and PRISMA-ScR. We searched three domestic databases (ScienceOn, Riss, KCI) and two international databases (PubMed, Cochrane Central) between January 2014 and April 2024. The keywords used were 'breast cancer surgery', 'breast cancer treatment', 'breast cancer lymphedema', 'intervention', 'management', 'therapy', 'disorder', and 'dysfunction'. Results : In terms of publication, the number of studies in the past five years has increased compared to the previous five years, with most studies focusing on patients aged 41 to 60 and who underwent surgical treatment for breast cancer. A total of 43 different types of non-pharmacological interventions were applied: 21 single interventions and 22 combination interventions. Among the intervention methods, complete decongestive treatment (CDT), resistance training, and manual lymphatic drainage were the most frequently utilized. The most common duration of intervention turned out to be 4~5 weeks and more than 8 weeks, with frequencies of 2~3 sessions per week and more than 4 sessions per week. The most frequently used dependent variables included range of motion (ROM) and disabilities of the arm, shoulder and hand (DASH) for the function and disorder of the upper limb category; arm circumference or volume and bio-impedance for the lymphedema category; visual analogue scale (VAS) and numerical rating scale (NRS) for the pain category; and the European organization for research and treatment of cancer quality of life questionnaire breast cancer module (EORTC QLQ) and functional assessment of cancer therapy-breast (FACT-B) for the quality of life category. Conclusion : The findings of this scoping review provide valuable mapping data for non-pharmacological interventions for physical rehabilitation following breast cancer treatment. We recommend further research, particularly systematic reviews and meta-analyses, to build upon these findings.

• Nutrition Research and Practice
• /
• v.18 no.5
• /
• pp.746-759
• /
• 2024

BACKGROUND/OBJECTIVES: To encourage schools to transform school meal programs to be more educational, it is necessary to evaluate the related environment using a whole school approach. We developed a school food culture evaluation tool to quantitatively evaluate school food culture in Gyeonggi Province, Korea. SUBJECTS/METHODS: Based on a literature review, a school food culture evaluation system consisting of areas, subareas, indicators, and questions (scored on a 5-point scale) was constructed. The validity of the tool was reviewed using focus group interviews, the Delphi technique, and a preliminary survey. Subsequently, evaluation tool was applied to elementary, middle, and high schools in Gyeonggi Province. Data from 115 schools were used for the final analysis. This included 64 elementary schools, 29 middle schools, and 22 high schools. At least one respondent from each group-school administrators, teachers, and nutrition teachers (or dietitians)-participated. The results were compared at the school level. RESULTS: The evaluation tool consisted of 66 questions in 5 areas (institutional environment, physical environment, educational environment, educational governance, and school meal quality). The total average score for school food culture was 3.83 points (elementary school 3.89 points, middle school 3.76 points, and high school 3.76 points) and did not differ significantly among school levels. Among the 5 evaluation areas, scores were highest for institutional environment (4.43 points) and lowest for physical environment (3.07 points). Scores for educational environment, educational governance, and school meal quality were 3.86, 3.85, and 3.97 points, respectively. CONCLUSION: It is necessary to improve the physical environment to create a desirable school food culture in Gyeonggi Province. To effectively promote healthy eating, ongoing investment and interventions by local authorities at improving school food culture are needed, with an emphasis on particular factors, such as the eating environment and staff training.

• Journal of Korean Society for Quality Management
• /
• v.52 no.3
• /
• pp.429-457
• /
• 2024

Purpose: This study aims to provide useful information to real estate investors by developing a profit determination model using artificial intelligence. The model analyzes the real estate markets of six selected cities from multiple perspectives, incorporating characteristics of the real estate market, economic indicators, and policies to determine potential profits. Methods: Data on real estate markets, economic indicators, and policies for five cities were collected and cleaned. The data was then normalized and split into training and testing sets. An AI model was developed using machine learning algorithms and trained with this data. The model was applied to the six cities, and its accuracy was evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared by comparing predicted profits to actual outcomes. Results: The profit determination model was successfully applied to the real estate markets of six cities, showing high accuracy and predictability in profit forecasts. The study provided valuable insights for real estate investors, demonstrating the model's utility for informed investment decisions. Conclusion: The study identified areas for future improvement, suggesting the integration of diverse data sources and advanced machine learning techniques to enhance predictive capabilities.