arXiv subjects

Jaemyung Ahn

Publications and source records attributed to Jaemyung Ahn.

At least 19 recordsLinked to original sources

arXiv2026

Korean Space Collision Environment Assessment Framework Based on 3D-Cell Model

Space situational awareness (SSA) requires purpose-matched models across spatial, temporal, and fidelity scales. Building on our previously reported three-dimensional (3D) cell formulation and implementation, this study establishes a reproducible, resolution-aware, catalog-conditioned framework for macroscopic assessment of the low Earth orbit (LEO) collision environment. The framework maps supplied catalog or scenario populations to time-averaged spatial density and target-specific impact metrics while retaining individual-object information. Using a 2025 Space-Track snapshot, we evaluate radial, declination, and right-ascension resolution sensitivity and computational performance for six targets, including two Korean space assets. Normalized expected impact counts range from 0.615 to 1.599 and vary nonmonotonically; for a synthetic 500-km circular target, the result at a 0.25-km radial width is 38.5\% below the 10-km reference. Runtime and memory show direction-dependent trade-offs. Ten annual snapshots show catalog growth from 15,723 objects in 2016 to 28,540 in 2025 and a 7.86-fold increase in the 500-km target metric, driven primarily by Starlink, other payloads, and unknown/TBA records. In a conditional stress test of the proposed 998,240-satellite SpaceX Orbital Data Center population, exact annual probabilities of at least one impact reach $3.75\times10^{-3}$ and $1.48\times10^{-3}$ for the 700-km and 1,000-km targets. The framework provides a reproducible, resolution-aware basis for catalog-conditioned environment monitoring, comparative scenario assessment, and prioritization of cases for higher-fidelity follow-up analysis.

astro-ph.EP↗

arXiv2026

Multi-Domain Matrix Framework for Human Resource Decision Support

This paper presents an actionable human resource (HR) decision-support framework for small firms and startups based on a multi-domain matrix (MDM). The framework addresses three key challenges faced by small organizations: complex interdependencies among organizational components; the lack of systematic analytical tools for HR decision-making; and the need for rapid responses in fast-changing organizational environments. The proposed framework formulates startup human resource management as a multi-domain structural modeling problem, where members, skills, and projects are interconnected domains within an integrated MDM. Based on this representation, the framework provides qualitative analysis guidelines and quantitative metrics for diagnosing an organization's HR state and supporting personnel decisions on workload redistribution, hiring, and capability development. A case study of MDM-based HR decisions for an early-stage technology startup is conducted to demonstrate the framework's practical applicability. The application shows that the framework can identify workload imbalances, reveal a key member with an unsustainable workload, and inform a subsequent hiring decision. The framework can be further applied after the hiring of a new member to track changes in the organization's multi-domain structure and support continuous HR diagnosis.

math.OC↗

arXiv2026

On-Orbit Servicing-Integrated Maintenance Strategy for Satellite Constellation

This paper proposes a maintenance strategy for a satellite constellation that utilizes on-orbit servicing (OOS). Under this strategy, the constellation operator addresses satellite failures in two ways: by deploying new satellites and by recovering failed satellites through OOS. We develop an inventory management model with a parametric replenishment policy for the maintenance process, which can evaluate the performance of the satellite constellation system. Based on this model, we formulate the interaction between the constellation operator -- who seeks to maintain the required service level of the constellation while minimizing maintenance cost -- and the OOS provider -- who seeks to maximize profit by selecting service price and performance levels -- as a bi-objective optimization problem and identify the corresponding Pareto-optimal solutions. A case study based on real-world-scale constellation and launch service shows that, relative to the benchmark strategy without OOS, the OOS-integrated solutions can reduce annual maintenance cost by up to 14.5%, while reducing annual launch and manufacturing costs by approximately 25% each and maintaining the required service levels. The results further show that, within a given scenario, the Pareto-optimal set is generally generated by an almost invariant maintenance strategy on the constellation operator side, whereas most variation along the Pareto frontier is driven by pricing decisions of the OOS provider side. Among the OOS-related parameters, the fraction of failures that can be recovered through OOS has the strongest structural effect.

math.OC↗

arXiv2026

Families of Two-Impulse Optimal Rendezvous Transfers Between Elliptic Orbits

The classical fuel-optimal two-impulse rendezvous problem between Keplerian orbits is revisited from a family-based perspective. Conventional approaches often yield isolated optimal solutions whose mutual relationships remain unclear; yet, when re-parameterized appropriately, seemingly unrelated optima are revealed to be connected members of continuous solution families. To expose this structure, the proposed framework enforces a subset of first-order necessary optimality conditions and traces the resulting one-parameter families via numerical continuation. The families are classified using Hessian-based criteria and Primer Vector Theory, and are projected onto porkchop plots to connect the angular and temporal domains. Representative case studies reveal the emergence, merging, and disappearance of locally optimal branches under variations in orbital geometry, supplying a global map of the solution landscape. This complementary perspective clarifies the robustness of optimal solutions and identifies alternative near-optimal transfers in the vicinity of a nominal trajectory.

math.OC↗

arXiv2025

System Architecting for GEO Communication Satellite Considering On-Orbit Refueling

This paper introduces the problem of selecting a satellite system architecture considering commercial on-orbit refueling (OOR). This problem answers two questions: "What design lifetime should the satellite have?" and "How much propellant should be carried at launch?" We formulate this as a mathematical optimization problem by adopting design lifetime and initial propellant mass as design variables and considering two objective functions to balance the returns and risks. To solve this problem, we develop a surrogate model-based framework grounded in a satellite lifecycle simulation. The framework captures various uncertainties and operational flexibility, and integrates a modified satellite sizing and cost model by adjusting traditional models with OOR. Based on the developed framework, we conduct a case study of GEO communication satellites to examine current target service performance and explore the potential of a new system architecture that diverges from traditional design trends.

math.OC↗

arXiv2025

Joint Replenishment Strategy for Multiple Satellite Constellations with Shared Launch Opportunities

This paper proposes a novel replenishment strategy that can jointly support multiple satellite constellations. In this approach, multiple constellations share launch opportunities and parking orbits to address the operational satellite failures and ensure the desired service level of the constellations. We develop an inventory management model based on parametric replenishment policies, considering the launch vehicle's capacity and the shipping size of satellites. Based on this model, we introduce two decision-making scenarios and propose their corresponding solution frameworks. We conduct two case studies to provide valuable insights into the proposed strategy and demonstrate its applicability to supply chain management for maintaining multiple satellite constellations.

math.OC↗

arXiv2024

Long-Term Earth Magnetosphere Science Orbit via Earth-Moon Resonance Orbit

This article investigates long-term orbits within the Earth's magnetosphere, specifically focusing on orbits where the argument of periapsis is synchronized with changes induced by lunar gravity assists and the Earth's argument of latitude over a complete orbital period in Earth-Moon resonance. In the Earth-Moon rotating frame, resonance orbits appear repetitive; however, the argument of periapsis shifts due to the third-body effects from lunar flybys. The extent of this shift is influenced by the Jacobi integral associated with the resonance orbit. To identify feasible resonance orbits and the optimal Jacobi integral, we map the argument of periapsis change against the Jacobi integral for each prospective orbit. This synchronization allows the spacecraft to remain within a confined region in space when observed from the Sun-Earth rotating frame. Finally, the article discusses the applications of these long-term Earth magnetosphere science orbits, including orbit-orientation reconfiguration (station keeping) and stability.

astro-ph.EP↗

arXiv2024

Optimal Replenishment Strategy for Satellite Constellation with Dual Supply Modes

This paper proposes a novel inventory management model for the replenishment strategy of a satellite mega-constellation incorporating dual supply modes: normal and auxiliary. The proposed framework employs an indirect channel for normal supply, wherein spare satellites are initially injected into a parking orbit before transferring to the target orbital plane via propulsion systems and orbital perturbations. Conversely, the auxiliary supply mode utilizes a direct channel, injecting spare satellites immediately into their designated orbital planes. The inventory management model is constructed using parametric replenishment policies: $\left(s,Q\right)$ and $\left(R_1,R_2,Q_1,Q_2\right)$ with a time window. Following this model, two optimization problems are addressed to construct the supply chain for satellite mega-constellation replenishment and to evaluate its performance. These are decision-making contexts from the perspectives of constellation operators and launch service providers. The case study showcases the practical applicability of the proposed model and optimization problems, yielding valuable insights for stakeholders in the spaceborne industry.

math.OC↗

arXiv2024

Visibility Analysis of the Sun as Viewed from Multiple Spacecraft at the Sun-Earth Lagrange Points

Beyond the Sun-Earth line, spacecraft equipped with various solar telescopes are intended to be deployed at several different vantage points in the heliosphere to carry out coordinated, multi-view observations of the Sun and its dynamic activities. In this context, we investigate solar visibility by imaging instruments onboard the spacecraft orbiting the Sun-Earth Lagrange points L1, L4 and L5, respectively. An optimal arrival time for vertical periodic orbits stationed at L4 and L5 is determined based on geometric considerations that ensure maximum visibility of solar poles or higher latitudes per year. For a different set of orbits around the three Lagrange points (L1, L4 and L5), we calculate the visibility of the solar surface (i.e., observation days per year) as a function of the solar latitude. We also analyze where the solar limb viewed from one of the three Sun-Earth Lagrange points under consideration is projected onto the solar surface visible to the other two. This analysis particularly aims at determining the feasibility of studying solar eruptions, such as flares and coronal mass ejections, with coordinated observations of off-limb erupting coronal structures and their on-disk magnetic footpoints. In addition, visibility analysis of a feature (such as sunspots) on the solar surface is made for multiple spacecraft in various types of orbits with different inclinations to quantify the improvement in continuous tracking of the target feature for studying its long-term evolution from emergence, growth and to decay. A comprehensive comparison of observations from single (L1), double (L1 and L4) and multi-space missions (L1, L4 and L5) is carried out through our solar visibility analysis, and this may help us to design future space missions of constructing multiple solar observatories at the Sun-Earth Lagrange points.

astro-ph.SR↗

arXiv2023

Simultaneous Optimization of Launch Vehicle Stage and Trajectory Considering Operational Safety Constraints

A conceptual design of a launch vehicle involves the optimization of trajectory and stages considering its launch operations. This process encompasses various disciplines, such as structural design, aerodynamics, propulsion systems, flight control, and stage sizing. Traditional approaches used for the conceptual design of a launch vehicle conduct the stage and trajectory designs sequentially, often leading to high computational complexity and suboptimal results. This paper presents an optimization framework that addresses both trajectory optimization and staging in an integrated way. The proposed framework aims to maximize the payload-to-liftoff mass ratio while satisfying the constraints required for safe launch operations (e.g., the impact points of burnt stages and fairing). A case study demonstrates the advantage of the proposed framework compared to the traditional sequential optimization approach.

math.OC↗

arXiv2023

Multi-Start Team Orienteering Problem for UAS Mission Re-Planning with Data-Efficient Deep Reinforcement Learning

In this paper, we study the Multi-Start Team Orienteering Problem (MSTOP), a mission re-planning problem where vehicles are initially located away from the depot and have different amounts of fuel. We consider/assume the goal of multiple vehicles is to travel to maximize the sum of collected profits under resource (e.g., time, fuel) consumption constraints. Such re-planning problems occur in a wide range of intelligent UAS applications where changes in the mission environment force the operation of multiple vehicles to change from the original plan. To solve this problem with deep reinforcement learning (RL), we develop a policy network with self-attention on each partial tour and encoder-decoder attention between the partial tour and the remaining nodes. We propose a modified REINFORCE algorithm where the greedy rollout baseline is replaced by a local mini-batch baseline based on multiple, possibly non-duplicate sample rollouts. By drawing multiple samples per training instance, we can learn faster and obtain a stable policy gradient estimator with significantly fewer instances. The proposed training algorithm outperforms the conventional greedy rollout baseline, even when combined with the maximum entropy objective.

cs.LG↗

arXiv2019

Task Scheduling of Multiple Agile Satellites with Transition Time and Stereo Imaging Constraints

This paper proposes a framework for scheduling the observation and download tasks of multiple agile satellites with practical considerations such as attitude transition time, onboard data capacity, and stereoscopic image acquisition. A mixed integer linear programming (MILP) formulation for optimal scheduling that can address these practical considerations is introduced. A heuristic algorithm to obtain a near-optimal solution of the formulated MILP based on the time windows pruning procedure is proposed. A comprehensive case study demonstrating the validity of the proposed formulation and heuristic is presented.

eess.SY↗

arXiv2018

Integrated design optimization of structural bending filter and gain schedules for rocket attitude control system

This paper proposes an integrated design optimization framework for the gain schedules and bending filter for the longitudinal control of a rocket during its ascent flight. Dynamic models representing the pitch/yaw motion of a rocket considering the elements such as the rigid body dynamics, aerodynamics, sloshing, bending, sensor/actuator, and flight computer are introduced. The linear proportional and differential (PD) control law with scheduled (time-varying) gains and bending filter parameters are identified as key decision variables for stabilizing the pitch/yaw motion of the rocket. The integrated optimal design problem that determines the decision variables to minimize the worst-case peak associated with the first bending mode with constraints on the stability margins during the flight of the rocket is mathematically formulated. A case study on design of gain schedules and bending filter for an actual sounding rocket using the proposed framework is conducted to demonstrate its effectiveness.

math.OC↗

arXiv2017

Near Time-Optimal Feedback Instantaneous Impact Point (IIP) Guidance Law for Rocket

This paper proposes a feedback guidance law to move the instantaneous impact point (IIP) of a rocket to a desired location. Analytic expressions relating the time derivatives of an IIP with the external acceleration of the rocket are introduced. A near time-optimal feedback-form guidance law to determine the direction of the acceleration for guiding the IIP is developed using the de-rivative expressions. The effectiveness of the proposed guidance law, in comparison with the results of open-loop trajectory optimization, was demonstrated through IIP pointing case studies.

math.OC↗

arXiv2017

Vehicle Routing Problem with Vector Profits (VRPVP) with Max-Min Criterion

This paper introduces a new routing problem referred to as the vehicle routing problem with vector profits. Given a network composed of nodes (depot/sites) and arcs connecting the nodes, the problem determines routes that depart from the depot, visit sites to collect profits, and return to the depot. There are multiple stakeholders interested in the mission and each site is associated with a vector whose k-th element represents the profit value for the k-th stakeholder. The objective of the problem is to maximize the profit sum for the least satisfied stakeholder, i.e., the stakeholder with the smallest total profit value. An approach based on the linear programming relaxation and column-generation to solve this max-min type routing problem was developed. Two cases studies - the planetary surface exploration and the Rome tour cases - were presented to demonstrate the effectiveness of the proposed problem formulation and solution methodology.

math.OC↗

arXiv2017

Geometric Decomposition-Based Formulation for Time Derivatives of Instantaneous Impact Point

A new analytic formulation to express the time derivatives of the instantaneous impact point (IIP) of a rocket is proposed. The geometric relationship on a plane tangential to the IIP is utilized to decompose the inertial IIP rate vector into the downrange and crossrange components, and a systematic procedure to determine the component values is presented. The new formulation shows significant advantages over the existing formulation such that the procedure and final expressions for the IIP derivatives are easy to understand and more compact. The validity of the proposed formulation was demonstrated through numerical simulation.

math.OC↗

arXiv2017

Pareto front generation with knee-point based pruning for mixed discrete multi-objective optimization

This note proposes an algorithm to generate the Pareto front of a mixed discrete multi-objective optimization problem based on the pruning of irrelevant subproblems. An existing pruning-based method for a mixed discrete bi-objective problem is extended for general multi-objective cases by introducing a new reference point for pruning decision - the knee point. The validity of the proposed procedure is demonstrated through case studies.

math.OC↗

arXiv2017

Two-phase framework for optimal multi-target Lambert rendezvous

This paper proposes a two-phase framework to solve an optimal multi-target Lambert rendezvous problem. The first phase solves a series of single-target rendezvous problems for all departure-arrival object pairs to generate the elementary solutions, which provides candidate rendezvous trajectories (elementary solutions). The second phase formulates a variant of traveling salesman problem (TSP) using the elementary solutions prepared in the first phase and determines the best rendezvous sequence and trajectories of the multi-target rendezvous problem. The validity of the proposed optimization framework is demonstrated through an asteroid exploration case study.

math.OC↗