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158 results on '"Heo, S.‐J."'

13. Machine Learning-Based Predictive Models of Behavioral and Psychological Symptoms of Dementia

Author

Cho, E, Kim, S, Heo, S-J, Shin, J, Ye, BS, Lee, JH, Kang, B, Cho, E, Kim, S, Heo, S-J, Shin, J, Ye, BS, Lee, JH, and Kang, B

Abstract

Models predicting the occurrence of specific types of behavioral and psychological symptoms of dementia (BPSD) can be highly beneficial for its early intervention and individualized care planning. Using a machine learning approach, this study developed and validated predictive models of the occurrence of BPSD, categorized into seven subsyndromes, among community-dwelling older adults with dementia in South Korea. BPSD dairy was used to measure BPSD and the state of unmet needs daily. We measured sleep and activity levels using actigraphy, and stress and fatigue using a portable heart rate variability analyzer. We developed predictive models and conducted cross-validation using training data that consisted of the first two wave dataset, and then validated the models using wave 3 test data. To deal with imbalanced datasets, we used Synthetic Minority Oversampling Technique (SMOTE), an over-sampling method. Categorical variables were pre-processed using target encoding. We then compared the machine-learning models with logistic regression. The area under the receiver operating characteristic curve (AUC) scores of the support vector machine (SVM) models for the wave 3 test data showed a similar or greater value than logistic regression models across all BPSD subsyndromes. The SVM model (AUC = 0.899) had an AUC value greater than that of the logistic regression model (AUC = 0.717), particularly for hyperactivity symptoms. Machine learning algorithms, especially SVM models, can be used to develop BPSD prediction models to help identify at-risk individuals and implement symptom-targeted individualized interventions.

Published
2021

41. Modelling of continuously variable damper for design of semi-active suspension systems

Author

Heo, S.-J., Park, K., and Son, S.-H.

Subjects
Motor industry -- Product information, Motor industry -- Services, Automobile industry
Abstract

Byline: S.-J. Heo, K. Park, S.-H. Son The continuously variable damper is widely used in the semi-active suspension system since it can yield various damping forces at a given damper velocity. By applying an appropriate control scheme to the damper, satisfaction of both ride comfort and driving safety can be realised. For successful development of the semi-active suspension system, a model that accurately describes the complex and nonlinear behaviour of the continuously variable damper is crucial. In this research, a modelling technique for the continuously variable damper has been studied. Various damper components have been analysed and their effects upon damper characteristics have been closely investigated. The effects of the damper characteristics change upon ride comfort and driving safety have also been investigated via simulations.

Published
2003

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