• Journal of the Ergonomics Society of Korea
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• v.35 no.4
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• pp.225-235
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• 2016

Objective: The aim of this study is to model all levels of situation awareness (SA), which would be able to predict situation awareness quantitatively. Background: When measuring situation awareness, directly measuring SA methods such as SAGAT and SART have been utilized. Several approaches (cognitive modeling approaches) were introduced to model SA but level 3 SA was not completed. For real-life situation, however, it is necessary to detect the problematic level of SA rather than overall SA. Therefore, we proposed a new model of all levels of SA in this study. Method: In order to model all levels of SA, this study chose factors in ACT-R architecture through literature review. ATC (Air Traffic Control)-related simulation task was video-taped to analyze human behaviors in order to model all levels of SA including level 3. Results: As a result, regression analyses show that cognitive activities (neural activations) represented for all levels of SA were highly correlated with SAGAT. Conclusion: In conclusion, neural activations in ACT-R could be proved to be effective to model all levels of SA. Application: Our SA model could be used to predict all levels of SA quantitatively without directly measuring the SA of operators.

• Journal of Korean Society for Quality Management
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• v.51 no.3
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• pp.363-379
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• 2023

Purpose: The main objective of this research is to construct an AI-based Composite Supplementary Index (ACSI) model to achieve accurate predictions of the Composite Index of Business Indicators. By incorporating various economic indicators as independent variables, the ACSI model enables the prediction and analysis of both the leading index (CLI) and coincident index (CCI). Methods: This study proposes an AI-based Composite Supplementary Index (ACSI) model that leverages diverse economic indicators as independent variables to forecast leading and coincident economic indicators. To evaluate the model's performance, advanced machine learning techniques including MLP, RNN, LSTM, and GRU were employed. Furthermore, the study explores the potential of employing deep learning models to train the weights associated with the independent variables that constitute the composite supplementary index. Results: The experimental results demonstrate the superior accuracy of the proposed composite supple- mentary index model in predicting leading and coincident economic indicators. Consequently, this model proves to be highly effective in forecasting economic cycles. Conclusion: In conclusion, the developed AI-based Composite Supplementary Index (ACSI) model successfully predicts the Composite Index of Business Indicators. Apart from its utility in management, economics, and investment domains, this model serves as a valuable indicator supporting policy-making and decision-making processes related to the economy.

• Archives of Craniofacial Surgery
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• v.15 no.2
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• pp.89-93
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• 2014

Background: To refine facial transplantation techniques and achieve sound results, it is essential to develop a suitable animal model. Rat is a small animal and has many advantages over other animals that have been used as transplantation models. The purpose of this study was to describe a rat hemifacial transplantation model and to verify its convenience and reproducibility. Methods: Animals used in this study were Lewis rats (recipients) and Lewis-Brown Norway rats (donors). Nine transplantations were performed, requiring 18 animals. The hemifacial flap that included the ipsilateral ear was harvested based on the unilateral common carotid artery and external jugular vein and was transferred as a single unit. Cyclosporine A therapy was initiated 24 hours after transplantation and lasted for 2 weeks. Signs of rejection responses were evaluated daily. Results: The mean transplantation time was 1 hour 20 minutes. The anatomy of common carotid artery and external jugular vein was consistent, and the vessel size was appropriate for anastomosis. Six of nine allografts remained good viable without vascular problems at the conclusion of study (postoperative 2 weeks). Conclusion: The rat hemifacial transplantation model is suitable as a standard transplantation training model.

• Journal of Korean Ophthalmic Optics Society
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• v.12 no.1
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• pp.75-84
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• 2007

In this study, I developed a teaching-learning model using project learning model which makes the most of PIM(Peer & Instructor Mentoring), Presentation contest, and unification of courses on the field ophthalmic optics. There were several conclusion as followings;. The teaching-learning model considering the unification and organic connections among subjects was efficient to the students' academic achievement. Peer & instructor mentoring system was helpful for students to accomplish their own learning projects. Project learning model with collaboration was useful for the development of students' self-controled learning ability and communicative ability. Project learning model gave its driving force to the better motivation and to the goal achievement. Project learning model was instructive for building up the related theories and concepts on the students' major. In conclusion, project learning model mixed subjects with festival, can be a alternative teaching-learning model.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.56.6-56
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• 2002

1. Introduction 2. Process description 2.1 Process description 2.2 Assumption 3. Mathematical model 3.1 MILP model for cintinuous part 3.2 LP model for batch part 4. Exapmles and Results 5. Conclusion. Acknowledgement. Reference.

• Journal of the Ergonomics Society of Korea
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• v.35 no.4
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• pp.277-291
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• 2016

Objective: The aim of this study is to propose a unit touch gesture model, which would be useful to predict the performance time on mobile devices. Background: When estimating usability based on Model-based Evaluation (MBE) in interfaces, the GOMS model measured 'operators' to predict the execution time in the desktop environment. Therefore, this study used the concept of operator in GOMS for touch gestures. Since the touch gestures are comprised of possible unit touch gestures, these unit touch gestures can predict to performance time with unit touch gestures on mobile devices. Method: In order to extract unit touch gestures, manual movements of subjects were recorded in the 120 fps with pixel coordinates. Touch gestures are classified with 'out of range', 'registration', 'continuation' and 'termination' of gesture. Results: As a results, six unit touch gestures were extracted, which are hold down (H), Release (R), Slip (S), Curved-stroke (Cs), Path-stroke (Ps) and Out of range (Or). The movement time predicted by the unit touch gesture model is not significantly different from the participants' execution time. The measured six unit touch gestures can predict movement time of undefined touch gestures like user-defined gestures. Conclusion: In conclusion, touch gestures could be subdivided into six unit touch gestures. Six unit touch gestures can explain almost all the current touch gestures including user-defined gestures. So, this model provided in this study has a high predictive power. The model presented in the study could be utilized to predict the performance time of touch gestures. Application: The unit touch gestures could be simply added up to predict the performance time without measuring the performance time of a new gesture.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.12
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• pp.5107-5111
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• 2016

Background: Epidemiological studies have indicated an increasing incidence of radiation induced secondary cancer (SC) in breast cancer patients after radiotherapy (RT), most commonly in the contra-lateral breast (CLB). The present study was conducted to estimate the SC risk in the CLB following 3D conformal radiotherapy techniques (3DCRT) including wedge field and forward intensity modulated radiotherapy (fIMRT) based on the organ equivalent dose (OED). Material and Methods: RT plans treating the chest wall with conformal wedge field and fIMRT plans were created for 30 breast cancer patients. The risks of radiation induced cancer were estimated for the CLB using dose-response models: a linear model, a linear-plateau model and a bell-shaped model with full dose response accounting for fractionated RT on the basis of OED. Results: The plans were found to be ranked quite differently according to the choice of model; calculations based on a linear dose response model fIMRT predict statistically significant lower risk compared to the enhanced dynamic wedge (EDW) technique (p-0.0089) and a non-significant difference between fIMRT and physical wedge (PW) techniques (p-0.054). The widely used plateau dose response model based estimation showed significantly lower SC risk associated with fIMRT technique compared to both wedge field techniques (fIMRT vs EDW p-0.013, fIMRT vs PW p-0.04). The full dose response model showed a non-significant difference between all three techniques in the view of second CLB cancer. Finally the bell shaped model predicted interestingly that PW is associated with significantly higher risk compared to both fIMRT and EDW techniques (fIMRT vs PW p-0.0003, EDW vs PW p-0.0032). Conclusion: In conclusion, the SC risk estimations of the CLB revealed that there is a clear relation between risk associated with wedge field and fIMRT technique depending on the choice of model selected for risk comparison.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.50 no.4
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• pp.216-221
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• 2024

Objectives: This study aimed to develop and validate a model to predict the need for intensive care unit (ICU) admission in patients with dental infections using an automated machine learning (ML) program called H2O-AutoML. Materials and Methods: Two models were created using only the information available at the initial examination. Model 1 was parameterized with only clinical symptoms and blood tests, excluding contrast-enhanced multi-detector computed tomography (MDCT) images available at the initial visit, whereas model 2 was created with the addition of the MDCT information to the model 1 parameters. Although model 2 was expected to be superior to model 1, we wanted to independently determine this conclusion. A total of 210 patients who visited the Department of Oral and Maxillofacial Surgery at the Dankook University Dental Hospital from March 2013 to August 2023 was included in this study. The patients' demographic characteristics (sex, age, and place of residence), systemic factors (hypertension, diabetes mellitus [DM], kidney disease, liver disease, heart disease, anticoagulation therapy, and osteoporosis), local factors (smoking status, site of infection, postoperative wound infection, dysphagia, odynophagia, and trismus), and factors known from initial blood tests were obtained from their medical charts and retrospectively reviewed. Results: The generalized linear model algorithm provided the best diagnostic accuracy, with an area under the receiver operating characteristic values of 0.8289 in model 1 and 0.8415 in model 2. In both models, the C-reactive protein level was the most important variable, followed by DM. Conclusion: This study provides unprecedented data on the use of ML for successful prediction of ICU admission based on initial examination results. These findings will considerably contribute to the development of the field of dentistry, especially oral and maxillofacial surgery.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.35.5-35
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• 2002

$\textbullet$ Introduction $\textbullet$ Preliminaries $\textbullet$ Switching Problem in 3 hybrid System $\textbullet$ Design of a fuzzy-model-based controller $\textbullet$ An example $\textbullet$ Conclusion

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.2
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• pp.85-93
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• 2009

GOMS model is a cognitive modeling method of human performance based on Goal, Operators, Methods, Selection rules. GOMS model was originally designed for desktop environment so that it is difficult for GOMS model to be implemented into the mobile environment. In addition, GOMS model would be inaccurate because the original GOMS model was based on serial processing, excluding one of most important human information processing characteristics, parallel processing. Therefore this study was designed to propose a modified GOMS model including mobile computing and parallel processing. In order to encompass mobile environment, an operator of 'look for' was divided into 'visual move to' and 'recognize' whereas 'point to' and 'click' were combined into 'tab.' The results showed that newly introduced operators were necessary to estimate more accurate mobile computing behaviors. In conclusion, modified-GOMS model could predict human performance more accurately than the original GOMS model in the mobile computing environment.