Chang-Hun Lee

Chang-Hun Lee, Min-Jea Tahk, Jin-Ik Lee

The purpose of this paper is to investigate the generalized formulation of weighted optimal guidance laws with impact angle constraint. From the generalized formulation, we explicitly find the feasible set of weighting functions that lead to analytical forms of weighted optimal guidance laws. This result has potential significance because it can provide additional degrees of freedom in designing a guidance law that accomplishes the specified guidance objective.

Chang-Hun Lee, Min-Guk Seo, Min-Jea Tahk et al.

A straight forward application of feedback linearization to the missile autopilot design for acceleration control may be limited due to the nonminimum characteristics and the model uncertainties. As a remedy, this paper presents a cascade structure of an acceleration controller based on approximate feedback linearization methodology with a time-delay adaptation scheme. The inner loop controller is constructed by applying feedback linearization to the approximate system which is a minimum phase system and provides the desired acceleration signal caused by the angle-of-attack. This controller is augmented by the time-delay adaptive law and the outer loop PI (proportional-integral) controller in order to adaptively compensate for feedback linearization error because of model uncertainty and in order to track the desired acceleration signal. The performance of the proposed method is examined through numerical simulations. Moreover, the proposed controller is tested by using an intercept scenario in 6DOF nonlinear simulations.

Chang-Hun Lee, Tae-Hun Kim, Min-Jea Tahk

This paper deals with a nonlinear adaptive autopilot design for agile missile systems. In advance of the autopilot design, an investigation of the agile turn maneuver, based on the trajectory optimization, is performed to determine state behaviors during the agile turn phase. This investigation shows that there exist highly nonlinear, rapidly changing dynamics and aerodynamic uncertainties. To handle of these difficulties, we propose a longitudinal autopilot for angle-of-attack tracking based on backstepping control methodology in conjunction with the time-delay adaptation scheme.

Hyungeun Lee, Ung Hwang, Seungwon Yu et al.

Osteoporosis is a widespread and chronic metabolic bone disease that often remains undiagnosed and untreated due to limited access to bone mineral density (BMD) tests like Dual-energy X-ray absorptiometry (DXA). In response to this challenge, current advancements are pivoting towards detecting osteoporosis by examining alternative indicators from peripheral bone areas, with the goal of increasing screening rates without added expenses or time. In this paper, we present a method to predict osteoporosis using hand and wrist X-ray images, which are both widely accessible and affordable, though their link to DXA-based data is not thoroughly explored. Initially, our method segments the ulnar, radius, and metacarpal bones using a foundational model for image segmentation. Then, we use a self-supervised learning approach to extract meaningful representations without the need for explicit labels, and move on to classify osteoporosis in a supervised manner. Our method is evaluated on a dataset with 192 individuals, cross-referencing their verified osteoporosis conditions against the standard DXA test. With a notable classification score (AUC=0.83), our model represents a pioneering effort in leveraging vision-based techniques for osteoporosis identification from the peripheral skeleton sites.

Ung Hwang, Chang-Hun Lee, Kijung Yoon

Osteoporosis is a widespread and chronic metabolic bone disease that often remains undiagnosed and untreated due to limited access to bone mineral density (BMD) tests like Dual-energy X-ray absorptiometry (DXA). In response to this challenge, current advancements are pivoting towards detecting osteoporosis by examining alternative indicators from peripheral bone areas, with the goal of increasing screening rates without added expenses or time. In this paper, we present a method to predict osteoporosis using hand and wrist X-ray images, which are both widely accessible and affordable, though their link to DXA-based data is not thoroughly explored. We employ a sophisticated image segmentation model that utilizes a mixture of probabilistic U-Net decoders, specifically designed to capture predictive uncertainty in the segmentation of the ulna, radius, and metacarpal bones. This model is formulated as an optimal transport (OT) problem, enabling it to handle the inherent uncertainties in image segmentation more effectively. Further, we adopt a self-supervised learning (SSL) approach to extract meaningful representations without the need for explicit labels, and move on to classify osteoporosis in a supervised manner. Our method is evaluated on a dataset with 192 individuals, cross-referencing their verified osteoporosis conditions against the standard DXA test. With a notable classification score, this integration of uncertainty-aware segmentation and self-supervised learning represents a pioneering effort in leveraging vision-based techniques for the early detection of osteoporosis from peripheral skeletal sites.