List of English Publication

@article{nokey,

title = {A Distributionally Robust Optimal Control Approach for Differentially Private Dynamical Systems},

author = {Yeongjun Jang and Kaoru Teranishi and Junsoo Kim},

doi = { 10.1109/LCSYS.2026.3708730},

issn = {2475-1456},

year  = {2026},

date = {2026-06-30},

urldate = {2026-06-30},

journal = {IEEE CONTROL SYSTEMS LETTERS},

volume = {10},

pages = {1567-1572},

abstract = {We develop a distributionally robust optimal control approach for differentially private dynamical

systems, enabling a plant to securely outsource control

computation to an untrusted remote server. We consider a

plant that ensures differential privacy of its state trajectory

by injecting calibrated noise into its output measurements.

Unlike prior works, we assume that the server only has

access to an ambiguity set consisting of admissible noise

distributions, rather than the exact distribution. To account

for this uncertainty, the server formulates a distributionally

robust optimal control problem to minimize the worst-case

expected cost over all admissible noise distributions. However, the formulated problem is computationally intractable

due to the nonconvexity of the ambiguity set. The proposed relaxation replaces the ambiguity set with a convex

Kullback–Leibler divergence ball, so that the reformulated

problem admits a tractable closed-form solution.},

keywords = {Homomorphic encryption, optimal control},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Spectral Trade-off for Measurement Sparsification of Pose-graph SLAM},

author = {Jiyeon Nam and Soojeong Hyeon and Youngjun Joo and DongKi Noh and Hyungbo Shim},

url = {https://ieeexplore.ieee.org/document/10333265},

doi = {10.1109/LRA.2023.3337590},

issn = {2377-3766},

year  = {2024},

date = {2024-01-02},

urldate = {2024-01-02},

journal = {IEEE Robotics and Automation Letters},

volume = {9},

issue = {1},

pages = {723 - 730},

abstract = {In this paper, we propose a trade-off optimization algorithm to compute an appropriate number of edges for measurement (edge) sparsification in pose-graph SLAM. The greater the amount of measurement data, the larger is the computational burden. To reduce computational burden, one can remove a portion of measurements. However, reliable data, such as odometric measurements, can be lost if measurements are removed without any principle. To remove measurements which is redundant, we propose a trade-off optimization algorithm between maximization of the Fiedler value and minimization of the largest eigenvalue of adjacency matrix for measurement graph. This problem formulation gives virtues twofold. First, it is scalable. For any dataset, when a weight for trade-off is given, this algorithm determines the appropriate number of edges since this is a trade-off optimization problem. Second, the edges of the measurement graph can be distributed evenly. The algorithm considers the minimization of the largest eigenvalue of the adjacency matrix, so it suppresses the upper bound of the maximum degree of the measurement graph. It removes the redundant information concentrated on a few nodes, and improves the estimation accuracy of the sparsified graph. To validate the performance of the proposed trade-off optimization algorithm, we apply our approach to CSAIL, Intel, and Manhattan datasets.},

keywords = {optimal control, simultaneous localization and mapping},

pubstate = {published},

tppubtype = {article}

}