Searcharxiv⌕ Search

arXiv subjects

Chang-Hun Lee

Publications and source records attributed to Chang-Hun Lee.

17 recordsLinked to original sources
arXiv2025

Customized Interior-Point Methods Solver for Embedded Real-Time Convex Optimization

This paper presents a customized second-order cone programming (SOCP) solver tailored for embedded real-time optimization, which frequently arises in modern guidance and control (G&C) applications. The solver employs a practically efficient predictor-corrector type primal-dual interior-point method (PDIPM) combined with a homogeneous embedding framework for infeasibility detection. Unlike conventional homogeneous self-dual embedding formulations, the adopted approach can directly handle quadratic cost functions without requiring problem reformulation. This capability allows the solver to directly address quadratic objective SOCP problems, while avoiding unnecessary performance degradation caused by the loss of sparsity due to problem reformulation. To support a systematic workflow, we also develop a code generation tool that analyzes the sparsity pattern of the problem to be solved and generates customized solver code using a predefined code template. The generated solver code is written in C with no external dependencies other than the standard library math.h, and it supports complete static allocation of all data. Additionally, it provides parsing information to facilitate the use of the solver by end users. Finally, benchmark and numerical experiments on an embedded platform demonstrate that the developed solver outperforms the existing solvers on problem scales typical of G&C applications.

math.OC↗
arXiv2024

Osteoporosis Prediction from Hand X-ray Images Using Segmentation-for-Classification and Self-Supervised Learning

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.

eess.IV↗
arXiv2024

Model Predictive Guidance for Fuel-Optimal Landing of Reusable Launch Vehicles

This paper introduces a landing guidance strategy for reusable launch vehicles (RLVs) using a model predictive approach based on sequential convex programming (SCP). The proposed approach devises two distinct optimal control problems (OCPs): planning a fuel-optimal landing trajectory that accommodates practical path constraints specific to RLVs, and determining real-time optimal tracking commands. This dual optimization strategy allows for reduced computational load through adjustable prediction horizon lengths in the tracking task, achieving near closed-loop performance. Enhancements in model fidelity for the tracking task are achieved through an alternative rotational dynamics representation, enabling a more stable numerical solution of the OCP and accounting for vehicle transient dynamics. Furthermore, modifications of aerodynamic force in both planning and tracking phases are proposed, tailored for thrust-vector-controlled RLVs, to reduce the fidelity gap without adding computational complexity. Extensive 6-DOF simulation experiments validate the effectiveness and improved guidance performance of the proposed algorithm.

eess.SY↗
arXiv2024

On the Reachability of 3-Dimensional Paths with a Prescribed Curvature Bound

This paper presents the reachability analysis of curves in $\mathbb{R}^3$ with a prescribed curvature bound. Based on Pontryagin Maximum Principle, we leverage the existing knowledge on the structure of solutions to minimum-time problems, or Markov-Dubins problem, to reachability considerations. Based on this development, two types of reachability are discussed. First, we prove that any boundary point of the reachability set, with the directional component taken into account as well as geometric coordinates, can be reached via curves of H, CSC, CCC, or their respective subsegments, where H denotes a helicoidal arc, C a circular arc with maximum curvature, and S a straight segment. Second, we show that the reachability set when directional component is not considered\textemdash{}the position reachability set\textemdash{}is simply a solid of revolution of its two-dimensional counterpart, the Dubins car. These findings extend the developments presented in literature on Dubins car into spatial curves in $\mathbb{R}^3$.

math.OC↗
arXiv2023

Osteoporosis Prediction from Hand and Wrist X-rays using Image Segmentation and Self-Supervised Learning

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.

eess.IV↗
arXiv2023

Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning

One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task. To address this issue, a recursive online sparse Gaussian Process (GP)-based learning strategy for attitude takeover control of noncooperative targets with maneuverability is proposed, where the unknown dynamics are online compensated based on the learnt GP model in a semi-feedforward manner. The method enables the continuous use of on-orbit data to successively improve the learnt model during online operation and has reduced computational load compared to standard GP regression. Next to the GP-based feedforward, a feedback controller is proposed that varies its gains based on the predicted model confidence, ensuring robustness of the overall scheme. Moreover, rigorous theoretical proofs of Lyapunov stability and boundedness guarantees of the proposed method-driven closed-loop system are provided in the probabilistic sense. A simulation study based on a high-fidelity simulator is used to show the effectiveness of the proposed strategy and demonstrate its high performance.

eess.SY↗
arXiv2020

Generation of quasiparticles by particle-antiparticle mixing and limitations of quantum mechanics

The mixing of a neutral unstable particle and its antiparticle has been usually discussed in the framework of quantum mechanics in the literature. In this paper, the particle-antiparticle mixing is discussed fully in the quantum field theory, and it is shown that quantum mechanics is not the proper non-relativistic limit of the quantum field theory in the presence of such mixing. Moreover, it is also shown that a discrepancy indeed exists between the results from quantum mechanics and the quantum field theory, which can sometimes make big differences in theoretical predictions of observables such as decay widths. This result has an implication for the mixing of neutral mesons and the CP violation in their decays.

hep-ph↗
arXiv2020

Flavor mixing and renormalization in a perturbation theory

The renormalization of theories with flavor mixing is discussed, and it is shown that the physical unstable particles should be interpreted as quasiparticles which cannot be regarded as external states. Several popular beliefs on renormalization are disproved accordingly, and the limitations of physical renormalization schemes are discussed. In addition, the properties of unstable particles with flavor mixing such as decay widths are studied from scattering mediated by them.

hep-ph↗
arXiv2017

Construction of lepton mass matrices and TeV-scale phenomenology in the minimal left-right symmetric model

We develop a systematic procedure of constructing lepton mass matrices that satisfy all the experimental constraints in the light lepton sector of the minimal left-right symmetric model with type-I seesaw dominance. This method is unique since it is applicable to the most general cases of type-I seesaw with complex electroweak vacuum expectation values in the model. With this method, we investigate the TeV-scale phenomenology in the normal hierarchy without fine-tuning of model parameters, focusing on the charged lepton flavour violation, neutrinoless double beta decay, and electric dipole moments of charged leptons. We examine the predictions for typical ranges of associated observables such as branching ratios of rare lepton decays, and study how those experimental constraints affect the model parameter space. The most notable result is that the regions of parameter space that allow small light neutrino masses have been constrained by the present experimental bounds from charged lepton flavour violation. Furthermore, we also find that the mass of the lightest heavy neutrino should be relatively small in order to satisfy those experimental constraints.

hep-ph↗
arXiv2016

Vector-Like Quarks and Leptons, SU(5) $\otimes$ SU(5) Grand Unification, and Proton Decay

SU(5) $\otimes$ SU(5) provides a minimal grand unification scheme for fermions and gauge forces if there are vector-like quarks and leptons in nature. We explore the gauge coupling unification in a non-supersymmetric model of this type, and study its implications for proton decay. The properties of vector-like quarks and intermediate scales that emerge from coupling unification play a central role in suppressing proton decay. We find that in this model, the familiar decay mode $p \to e^+ π^0$ may have a partial lifetime within the reach of currently planned experiments.

hep-ph↗
arXiv2015

TeV Scale Lepton Number Violation and Baryogenesis

Contrary to the common lore based on naive dimensional analysis, the seesaw scale for neutrino masses can be naturally in the TeV range, with small parameters coming from radiative corrections. We present one such class of type-I seesaw models, based on the left-right gauge group $SU(2)_L\times SU(2)_R\times U(1)_{B-L}$ realized at the TeV scale, which fits the observed neutrino oscillation parameters as well as other low energy constraints. We discuss how the small parameters of this scenario can arise naturally from one loop effects. The neutrino fits in this model use quasi-degenerate heavy Majorana neutrinos, as also required to explain the matter-antimatter asymmetry in our Universe via resonant leptogenesis mechanism. We discuss the constraints implied by the dynamics of this mechanism on the mass of the right-handed gauge boson in this class of models with enhanced neutrino Yukawa couplings compared to the canonical seesaw model and find a lower bound of $m_{W_R}\geq 9.9$ TeV for successful leptogenesis assuming maximal CP asymmetry for each flavor. We also present a model with explicit neutrino mass fit, where the lower bound goes up to 13.1 TeV due to less than maximal primordial CP asymmetry predicted by the model.

hep-ph↗
arXiv2014

Leptogenesis Constraints on the Mass of Right-handed Gauge Bosons

We discuss leptogenesis constraints on the mass of the right-handed $W$-boson ($W_R$) in a TeV-scale Left-Right seesaw model (LRSM) for neutrino masses. For generic Dirac mass of the neutrinos, i.e. with all Yukawa couplings $\lesssim 10^{-5.5}$, it has been pointed out that successful leptogenesis requires a lower bound of 18 TeV on the $W_R$ mass, pushing it beyond the reach of LHC. Such TeV-scale LRSM must, however, be parity-asymmetric for type-I seesaw to give the observed neutrino masses. This class of models can accommodate larger Yukawa couplings, which give simultaneous fits to charged-lepton and neutrino masses, by invoking either cancellations or specific symmetry-textures for Dirac ($M_D$) and Majorana ($M_N$) masses in the seesaw formula. We show that in this case, the leptogenesis bound on $M_{W_R}$ can be substantially weaker, i.e. $M_{W_R}\gtrsim 3$ TeV for $M_N \lesssim M_{W_R}$. This happens due to considerable reduction of the dilution effects from $W_R$-mediated decays and scatterings, while the washout effects due to inverse decays are under control for certain parameter ranges of the Yukawa couplings. We also show that this model is consistent with all other low energy constraints, such as lepton flavor violation and neutrinoless double beta decay. Thus, a discovery of the right-handed gauge bosons alone at the LHC will not falsify leptogenesis as the mechanism behind the matter-antimatter asymmetry in our Universe.

hep-ph↗
arXiv2013

Natural TeV-Scale Left-Right Seesaw for Neutrinos and Experimental Tests

We present a TeV-scale left-right ultraviolet completion of type-I seesaw for neutrino masses based on the $SU(2)_L\times SU(2)_R\times U(1)_{B-L}$ gauge group without parity, which leads to "large" light-heavy neutrino mixing while keeping the neutrino masses small in a natural manner guaranteed by discrete symmetries. We point out specific observable implications of this class of models if the $SU(2)_R$-breaking scale is of order 5 TeV, in searches for lepton flavor violating processes such as $μ\to eγ$, $μ\to 3 e$ and $μ-e$ conversion in nuclei, and lepton number violating processes such as neutrinoless double beta decay as well as at the LHC. In particular, if the upper limit on BR$(μ\to eγ)$ improves by one order of magnitude, a large range of the parameters of the model would be ruled out.

hep-ph↗
arXiv2012

Generalized Formulation of Weighted Optimal Guidance Laws with Impact Angle Constraint

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.

eess.SY↗
arXiv2012

Agile Missile Controller Based on Adaptive Nonlinear Backstepping Control

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.

eess.SY↗
arXiv2012

Missile Acceleration Controller Design using PI and Time-Delay Adaptive Feedback Linearization Methodology

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.

eess.SY↗