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1,307 results on '"Kim, Jung Hoon"'

7. Investigating the Association of School Type and Gender with Adolescent's Mindsets

Author

Kim, Jung Hoon, Kwon, Jongkyum, and Ryoo, Ji Hoon

Abstract

Mindset is a key factor affecting personality and learning for adolescents, especially those living in countries focusing heavily on human resources, such as Korea, Singapore, and Hong Kong. We examined associations of fixed intelligence mindset, fixed creative mindset, students' gender and school type via a structural equation modeling. The sample included 301 Korean adolescents: students in their second year at a gifted high school, general high school students, and vocational high school students. To confirm construct validity and examine the associations of gender and school type with both fixed intelligent and creative mindsets, we fit a two-factor model within the structural equation model. We found significant associations of school type and gender with students' fixed intelligence mindset and fixed creative mindset. Three paths--"gender" [right arrow] "fixed intelligence mindset, school type" [right arrow] "fixed intelligence mindset," and "school type" [right arrow] "fixed creative mindset"--were significant. In Western countries where mindset research originated and developed, there have already been efforts to manage students' mindsets with policies. However, Asian countries still lack understanding of and basic research on mindset management. Therefore, these countries should pay closer attention to policies regarding students' mindset management. Simultaneously, a practical school-level program is required to address the associations of school type and gender with student's mindsets revealed through this study.

Published
2023
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46. Characterizing ℒ1$$ {\mathcal{L}}_1 $$ output‐feedback controller for nonlinear systems: Existence conditions via output controlled invariance domain.

Author

Choi, Hyung Tae, Kim, Jung Hoon, and Hagiwara, Tomomichi

Subjects
*NONLINEAR systems, *ARGUMENT, *MOTIVATION (Psychology)
Abstract

Summary: Motivated by existing works on the ℒ1$$ {\mathcal{L}}_1 $$ state‐feedback controller for nonlinear systems, in which the ℒ∞$$ {\mathcal{L}}_{\infty } $$ norm of the output for the worst disturbance with a unit magnitude is required to be bounded by 1, this paper considers an extension of those works to an output‐feedback form. More precisely, the existence of an ℒ1$$ {\mathcal{L}}_1 $$ output‐feedback controller for nonlinear systems is characterized by developing output regulation map and output controlled invariance domain, which are extended versions of the conventional regulation map and controlled invariance domain in the previous works. We first lead to a sufficient condition for the existence of an ℒ1$$ {\mathcal{L}}_1 $$ output‐feedback controller by ensuring the lower‐semicontinuity of the corresponding output regulation map. It is also shown in this paper that there exists an ℒ1$$ {\mathcal{L}}_1 $$ output‐feedback controller only if there exists an output controlled invariance domain. Based on these conditions, we further introduce algorithmic guidelines for verifying the existence of ℒ1$$ {\mathcal{L}}_1 $$ output‐feedback controller. Finally, a numerical example is provided to verify the validity of the overall arguments developed in this paper. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Selection of Convolutional Neural Network Model for Bladder Tumor Classification of Cystoscopy Images and Comparison with Humans.

Author

Lee, Ju Young, Lee, Yong Seong, Tae, Jong Hyun, Chang, In Ho, Kim, Tae-Hyoung, Myung, Soon Chul, Nguyen, Tuan Thanh, Lee, Jae Hyeok, Choi, Joongwon, Kim, Jung Hoon, Kim, Jin Wook, and Choi, Se Young

Subjects
ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, MACHINE learning, MEDICAL students, ARTIFICIAL intelligence
Abstract

Purpose: An investigation of various convolutional neural network (CNN)-based deep learning algorithms was conducted to select the appropriate artificial intelligence (AI) model for calculating the diagnostic performance of bladder tumor classification on cystoscopy images, with the performance of the selected model to be compared against that of medical students and urologists. Methods: A total of 3,731 cystoscopic images that contained 2,191 tumor images were obtained from 543 bladder tumor cases and 219 normal cases were evaluated. A total of 17 CNN models were trained for tumor classification with various hyperparameters. The diagnostic performance of the selected AI model was compared with the results obtained from urologists and medical students by using the receiver operating characteristic (ROC) curve graph and metrics. Results: EfficientNetB0 was selected as the appropriate AI model. In the test results, EfficientNetB0 achieved a balanced accuracy of 81%, sensitivity of 88%, specificity of 74%, and an area under the curve (AUC) of 92%. In contrast, human-derived diagnostic statistics for the test data showed an average balanced accuracy of 75%, sensitivity of 94%, and specificity of 55%. Specifically, urologists had an average balanced accuracy of 91%, sensitivity of 95%, and specificity of 88%, while medical students had an average balanced accuracy of 69%, sensitivity of 94%, and specificity of 44%. Conclusions: Among the various AI models, we suggest that EfficientNetB0 is an appropriate AI classification model for determining the presence of bladder tumors in cystoscopic images. EfficientNetB0 showed the highest performance among several models and showed high accuracy and specificity compared to medical students. This AI technology will be helpful for less experienced urologists or nonurologists in making diagnoses. Image-based deep learning classifies bladder cancer using cystoscopy images and shows promise for generalized applications in biomedical image analysis and clinical decision making. [ABSTRACT FROM AUTHOR]

Published
2024
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