• Korean Journal of Community Nutrition
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• v.8 no.5
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• pp.744-754
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• 2003

At present, health care industries throughout the world are struggling with the challenges to set up financial structures as cost-effective ways and means of satisfying customer needs for health care services. Many hospitals consign foodservice management to foodservice companies for the purpose of efficiency. The companies taking charge of hospital foodservice are also striving to gain an advantage over keen competitions. This study applied Quality Function Deployment(QFD) to one hospital (which will be shown as $\ulcorner$A hospital$\lrcorner$ below) managed by a contract foodservice company for the purpose of strategy planning to provide sustainable competitive advantage. First of all, this study scanned internal and external environment of $\ulcorner$A hospital$\lrcorner$ by means of a Quality Measurement Tool and a fieldwork study. With the result of environment scanning, this study elicited 20 strategies through SWOT analysis, which were categorized by 4 perspectives such as financial, customer, internal process, learning and growth perspectives. Finally, the priorities of 20 strategies were extracted from QFD methodology. According to the results obtained by applying QFD to $\ulcorner$A hospital$\lrcorner$'s foodservice, the strategies which $\ulcorner$A hospital$\lrcorner$ foodservice was obliged to introduce and implement were : the specialization of Children's hospital foodservice, scientific foodservice management through the standardization of foodservice operations, the maintenance of sanitary quality through sanitary system, the remodeling of facilities, the introduction of new equipment, the prompt and accurate response to customer needs, the development of appropriate patient menus, the provision of competitively priced meals for patient selection, the development of a demand forecast model by considering the characteristics of a children's hospital, improvement of productivity and the reduction of labor costs through the employment of experienced employees based on their seniority.

• Smart Media Journal
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• v.13 no.4
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• pp.48-56
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• 2024

In the livestock industry, detecting environmental outliers and predicting data are crucial tasks. Outliers in livestock environment data, typically gathered through time-series methods, can signal rapid changes in the environment and potential unexpected epidemics. Prompt detection and response to these outliers are essential to minimize stress in livestock and reduce economic losses for farmers by early detection of epidemic conditions. This study employs two methods to experiment and compare performances in setting thresholds that define outliers in livestock environment data outlier detection. The first method is an outlier detection using Mean Squared Error (MSE), and the second is an outlier detection using a Dynamic Threshold, which analyzes variability against the average value of previous data to identify outliers. The MSE-based method demonstrated a 94.98% accuracy rate, while the Dynamic Threshold method, which uses standard deviation, showed superior performance with 99.66% accuracy.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.105-111
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• 2024

In this paper, we propose methods for the named entity normalization of traditional herbal formula found in medical texts. Specifically, we developed methodologies to determine whether mentions, such as full names of herbal formula and their abbreviations, refer to the same concept. Two different approaches were attempted. First, we built a supervised classification model that uses BERT-based contextual vectors and character similarity features of herbal formula mentions in medical texts to determine whether two mentions are identical. Second, we applied a prompt-based querying method using GPT-4o mini and GPT-4o to perform the same task. Both methods achieved over 0.9 in Precision, Recall, and F1-score, with the GPT-4o-based approach demonstrating the highest Precision and F1-Score. The results of this study demonstrate the effectiveness of machine learning-based approaches for named entity normalization in traditional medicine texts, with the GPT-4o-based method showing superior performance. This suggests its potential as a valuable foundation for the development of intelligent information extraction systems in the traditional medicine domain.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.152-159
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• 2024

Cracks in structural materials present a critical challenge to infrastructure safety and long-term durability. Timely and precise crack detection is essential for proactive maintenance and the prevention of catastrophic structural failures. This study introduces an innovative approach to tackle this issue using U-Net deep learning architecture. The primary objective of the intended research is to explore the potential of U-Net in enhancing the precision and efficiency of crack detection across various concrete crack detection under various environmental conditions. Commencing with the assembling by a comprehensive dataset featuring diverse images of concrete cracks, optimizing crack visibility and facilitating feature extraction through advanced image processing techniques. A wide range of concrete crack images were collected and used advanced techniques to enhance their visibility. The U-Net model, well recognized for its proficiency in image segmentation tasks, is implemented to achieve precise segmentation and localization of concrete cracks. In terms of accuracy, our research attests to a substantial advancement in automated of 95% across all tested concrete materials, surpassing traditional manual inspection methods. The accuracy extends to detecting cracks of varying sizes, orientations, and challenging lighting conditions, underlining the systems robustness and reliability. The reliability of the proposed model is measured using performance metrics such as, precision(93%), Recall(96%), and F1-score(94%). For validation, the model was tested on a different set of data and confirmed an accuracy of 94%. The results shows that the system consistently performs well, even with different concrete types and lighting conditions. With real-time monitoring capabilities, the system ensures the prompt detection of cracks as they emerge, holding significant potential for reducing risks associated with structural damage and achieving substantial cost savings.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.455-462
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• 2024

In the construction industry, known for its dynamic and hazardous environments, there exists a crucial demand for effective safety incident prevention. Traditional approaches to monitoring on-site safety, despite their importance, suffer from being laborious and heavily reliant on subjective, paper-based reports, which results in inefficiencies and fragmented data. Additionally, the incorporation of computer vision technologies for automated safety monitoring encounters a significant obstacle due to the lack of suitable training datasets. This challenge is due to the rare availability of safety accident images or videos and concerns over security and privacy violations. Consequently, this paper explores an innovative method to address the shortage of safety-related datasets in the construction sector by employing generative artificial intelligence (AI), specifically focusing on the Stable Diffusion model. Utilizing real-world construction accident scenarios, this method aims to generate photorealistic images to enrich training datasets for safety surveillance applications using computer vision. By systematically generating accident prompts, employing static prompts in empirical experiments, and compiling datasets with Stable Diffusion, this research bypasses the constraints of conventional data collection techniques in construction safety. The diversity and realism of the produced images hold considerable promise for tasks such as object detection and action recognition, thus improving safety measures. This study proposes future avenues for broadening scenario coverage, refining the prompt generation process, and merging artificial datasets with machine learning models for superior safety monitoring.

• Journal of The Korean Association For Science Education
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• v.34 no.3
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• pp.273-283
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• 2014

The purpose of this study was to examine the potential of the use of the Classroom Response System (CRS), a kind of new ICT medium, in a quiz problem-solving oriented high school life science class. To find the usefulness of CRS as a teaching and learning strategy, the CRS group (n=34) sent prompt individual answers to the teachers' questions using the CRS terminal (Clicker), and the teacher then asked additional reasons of the individuals and gave personalized feedback. In the control group (n=35), the CRS was not used while the teacher asked overall questions and gave feedback in an undifferentiated way. As a result, the CRS increased students' interest and concentration during class, but there were no significant differences in study achievement between the two groups. However, there were significant differences between the medium-level groups when the two groups were divided into smaller ones based on their pre-scores. We suggest that, for effective use of the CRS for diagnostic and formative assessment, teachers should develop a teaching and learning strategy that can produce appropriate questions of various levels in advance, investigate the exact reasons for students' answers, and give customized feedback by individual as much as possible.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• The Journal of Bigdata
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• v.7 no.2
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• pp.1-9
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• 2022

In the global era, the importance of imported food safety management is increasing. Address information of overseas food companies is key information for imported food safety management, and must be verified for prompt response and follow-up management in the event of a food risk. However, because each country's address system is different, one verification system cannot verify the addresses of all countries. Also, the purpose of address verification may be different depending on the field used. In this paper, we deal with the problem of classifying a given overseas food business address into the administrative district level of the country. This is because, in the event of harm to imported food, it is necessary to find the administrative district level from the address of the relevant company, and based on this trace the food distribution route or take measures to ban imports. However, in some countries the administrative district level name is omitted from the address, and the same place name is used repeatedly in several administrative district levels, so it is not easy to accurately classify the administrative district level from the address. In this study we propose a deep learning-based administrative district level classification model suitable for this case, and verify the actual address data of overseas food companies. Specifically, a method of training using a label powerset in a multi-label classification model is used. To verify the proposed method, the accuracy was verified for the addresses of overseas manufacturing companies in Ecuador and Vietnam registered with the Ministry of Food and Drug Safety, and the accuracy was improved by 28.1% and 13%, respectively, compared to the existing classification model.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

• Journal of The Korean Association For Science Education
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• v.31 no.5
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• pp.694-708
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• 2011

In this study, we developed tasks including cognitive scaffolding for students to explain scientific phenomena using valid evidences in science classroom and sought to investigate how tasks influence the development of small group scientific argumentation. Heterogeneous small groups in gender and achievement were organized in one classroom and the tasks were applied to the class. Students were asked to write down their own ideas, share individual ideas, and then choose the most plausible opinion in a group. One group was chosen for investigating the effect of tasks on the development of small group argumentation through the analysis of discourse transcripts of the group in 10 lessons, students' semi-structured interview, field note, and students' pre- and post argument tests. The discrepant argument examples were included in the tasks for students to refute an argument presenting evidences. Moreover, comparing opinion within the group and persuading others were included in the tasks to prompt small group argumentation. As a result, students' post-argument test grades were increased than pre-test grades, and they argued involving evidences and reasoning. The high level of arguments has appeared with high ratio of advanced utterances and lengthening of reasoning chain as lessons went on. Students had elaborate claims involving valid evidences and reasoning by reflective and critical thinking while discussing about the tasks. In addition, tasks which could have various warrants based on the data led to students' spontaneous participation. Therefore, this study has significance in understanding the context of developing small group argumentation, providing information about teaching and learning context prompting students to construct arguments in science inquiry lessons in middle school.