SearcharxivSearch

arXiv subjects

Sangkyun Kim

Publications and source records attributed to Sangkyun Kim.

2 recordsLinked to original sources

Low-Thrust Trajectory Optimization for Cubesat Lunar Mission: HORYU-VI

This paper presents a low-thrust trajectory optimization strategy to achieve a near-circular lunar orbit for a CubeSat injected into a lunar flyby trajectory. The 12U CubeSat HORYU-VI is equipped with four Hall-effect thrusters and designed as a secondary payload on NASA's Space Launch System under the Artemis program. Upon release, the spacecraft gains sufficient energy to escape the Earth-Moon system after a lunar flyby. The proposed trajectory is decomposed into three phases: (1) pre-flyby deceleration to avoid heliocentric escape, (2) lunar gravitational capture, and (3) orbit circularization to the science orbit. For each phase, an impulsive-burn solution is first computed as an initial guess, which is then refined through finite-burn optimization using Sequential Quadratic Programming (SQP). The dynamical model incorporates Earth-Moon-Sun-Jupiter gravitational interactions and a high-fidelity lunar gravity field. All trajectories are independently verified with NASA's General Mission Analysis Tool (GMAT). Results demonstrate that HORYU-VI achieves lunar capture within 200 days, establishes a stable science orbit at 280 days, and can spiral down to a near-circular 100 km orbit by 450 days, using a total Delta-V of 710 m/s, well within the capability of the electric propulsion system.

eess.SY

Covariate-dependent control limits for the detection of abnormal price changes in scanner data

Currently, large-scale sales data for consumer goods, called scanner data, are obtained by scanning the bar codes of individual products at the points of sale of retail outlets. Many national statistical offices use scanner data to build consumer price statistics. In this process, as in other statistical procedures, the detection of abnormal transactions in sales prices is an important step in the analysis. Popular methods for conducting such outlier detection are the quartile method, the Hidiroglou-Berthelot method, the resistant fences method, and the Tukey algorithm. These methods are based solely on information about price changes and not on any of the other covariates (e.g., sales volume or types of retail shops) that are also available from scanner data. In this paper, we propose a new method to detect abnormal price changes that takes into account an additional covariate, namely, sales volume. We assume that the variance of the log of the price change is a smooth function of the sales volume and estimate the function from previously observed data. We numerically show the advantages of the new method over existing methods. We also apply the methods to real scanner data collected at weekly intervals by the Korean Chamber of Commerce and Industry between 2013 and 2014 and compare their performance.

stat.ME