arXiv subjects

Naoki Kita

Publications and source records attributed to Naoki Kita.

4 recordsLinked to original sources

arXiv2025

Computational Design and Fabrication of Protective Foam

This paper proposes a method to design protective foam for packaging 3D objects. Users first load a 3D object and define a block-based design space by setting the block resolution and the size of each block. The system then constructs a block map in the space using depth textures of the input object, separates the map into two regions, and outputs the regions as foams. The proposed method is fast and stable, allowing the user to interactively make protective foams. The generated foam is a height field in each direction, so the foams can easily be fabricated using various materials, such as LEGO blocks, sponge with slits, glass, and wood. This paper shows some examples of fabrication results to demonstrate the robustness of our system. In addition, we conducted a user study and confirmed that our system is effective for manually designing protective foams envisioned by users.

cs.GR↗

arXiv2021

Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning

Millimeter wave (mmWave) beam-tracking based on machine learning enables the development of accurate tracking policies while obviating the need to periodically solve beam-optimization problems. However, its applicability is still arguable when training-test gaps exist in terms of environmental parameters that affect the node dynamics. From this skeptical point of view, the contribution of this study is twofold. First, by considering an example scenario, we confirm that the training-test gap adversely affects the beam-tracking performance. More specifically, we consider nodes placed on overhead messenger wires, where the node dynamics are affected by several environmental parameters, e.g, the wire mass and tension. Although these are particular scenarios, they yield insight into the validation of the training-test gap problems. Second, we demonstrate the feasibility of \textit{zero-shot adaptation} as a solution, where a learning agent adapts to environmental parameters unseen during training. This is achieved by leveraging a robust adversarial reinforcement learning (RARL) technique, where such training-and-test gaps are regarded as disturbances by adversaries that are jointly trained with a legitimate beam-tracking agent. Numerical evaluations demonstrate that the beam-tracking policy learned via RARL can be applied to a wide range of environmental parameters without severely degrading the received power.

cs.LG↗

arXiv2020

Optical trapping of the transversal motion for an optically levitated mirror

Optomechanical systems are suitable for elucidating quantum phenomena at the macroscopic scale in the sense of the mass scale. The systems should be well-isolated from the environment to avoid classical noises, which conceal quantum signals. Optical levitation is a promising way to isolate optomechanical systems from the environment. To realize optical levitation, all degrees of freedom need to be trapped. Until now, longitudinal trapping and rotational trapping of a mirror with optical radiation pressure have been studied in detail and validated with various experiments. However, less attention has been paid to the transversal trapping of a mirror. Herein, we report a pioneering result where we experimentally confirmed transversal trapping of a mirror of a Fabry-Pérot cavity using a torsional pendulum. Through this demonstration, we experimentally proved that optical levitation is realizable with only two Fabry-Pérot cavities that are aligned vertically. This work paves the way toward optical levitation and realizing a macroscopic quantum system.

quant-ph↗

arXiv2020

Millimeter Wave Communications on Overhead Messenger Wire: Deep Reinforcement Learning-Based Predictive Beam Tracking

This paper discusses the feasibility of beam tracking against dynamics in millimeter wave (mmWave) nodes placed on overhead messenger wires, including wind-forced perturbations and disturbances caused by impulsive forces to wires. Our main contribution is to answer whether or not historical positions and velocities of a mmWave node is useful to track directional beams given the complicated on-wire dynamics. To this end, we implement beam-tracking based on deep reinforcement learning (DRL) to learn the complicated relationships between the historical positions/velocities and appropriate beam steering angles. Our numerical evaluations yielded the following key insights: Against wind perturbations, an appropriate beam-tracking policy can be learned from the historical positions and velocities of a node. Meanwhile, against impulsive forces to the wire, the use of the position and velocity of the node is not necessarily sufficient owing to the rapid displacement of the node. To solve this, we propose to take advantage of the positional interaction on the wire by leveraging the positions/velocities of several points on the wire as state information in DRL. The results confirmed that this results in the avoidance of beam misalignment, which would not be possible by using only the position/velocity of the node.

cs.NI↗