arXiv subjects

Jaehyun Kim

Publications and source records attributed to Jaehyun Kim.

31 records · Page 2Linked to original sources

arXiv2023

Efficient Graduated Non-Convexity for Pose Graph Optimization

We propose a novel approach to Graduated Non-Convexity (GNC) and demonstrate its efficacy through its application in robust pose graph optimization, a key component in SLAM backends. Traditional GNC methods often rely on heuristic methods for GNC schedule, updating control parameter μ for escalating the non-convexity. In contrast, our approach leverages the properties of convex functions and convex optimization to identify the boundary points beyond which convexity is no longer guaranteed, thereby eliminating redundant optimization steps in existing methodologies and enhancing both speed and robustness. We show that our method outperforms the state-of-the-art method in terms of speed and accuracy when used for robust back-end pose graph optimization via GNC. Our work builds upon and enhances the open-source riSAM framework. Our implementation can be accessed from: https://github.com/SNU-DLLAB/EGNC-PGO

cs.RO↗

arXiv2023

Adaptive Graduated Non-Convexity for Pose Graph Optimization

We present a novel approach to robust pose graph optimization based on Graduated Non-Convexity (GNC). Unlike traditional GNC-based methods, the proposed approach employs an adaptive shape function using B-spline to optimize the shape of the robust kernel. This aims to reduce GNC iterations, boosting computational speed without compromising accuracy. When integrated with the open-source riSAM algorithm, the method demonstrates enhanced efficiency across diverse datasets. Accompanying open-source code aims to encourage further research in this area. https://github.com/SNU-DLLAB/AGNC-PGO

cs.RO↗

arXiv2022

Controllable Missingness from Uncontrollable Missingness: Joint Learning Measurement Policy and Imputation

Due to the cost or interference of measurement, we need to control measurement system. Assuming that each variable can be measured sequentially, there exists optimal policy choosing next measurement for the former observations. Though optimal measurement policy is actually dependent on the goal of measurement, we mainly focus on retrieving complete data, so called as imputation. Also, we adapt the imputation method to missingness varying with measurement policy. However, learning measurement policy and imputation requires complete data which is impossible to be observed, unfortunately. To tackle this problem, we propose a data generation method and joint learning algorithm. The main idea is that 1) the data generation method is inherited by imputation method, and 2) the adaptation of imputation encourages measurement policy to learn more than individual learning. We implemented some variations of proposed algorithm for two different datasets and various missing rates. From the experimental results, we demonstrate that our algorithm is generally applicable and outperforms baseline methods.

cs.LG↗

arXiv2021

Spatially and Seamlessly Hierarchical Reinforcement Learning for State Space and Policy space in Autonomous Driving

Despite advances in hierarchical reinforcement learning, its applications to path planning in autonomous driving on highways are challenging. One reason is that conventional hierarchical reinforcement learning approaches are not amenable to autonomous driving due to its riskiness: the agent must move avoiding multiple obstacles such as other agents that are highly unpredictable, thus safe regions are small, scattered, and changeable over time. To overcome this challenge, we propose a spatially hierarchical reinforcement learning method for state space and policy space. The high-level policy selects not only behavioral sub-policy but also regions to pay mind to in state space and for outline in policy space. Subsequently, the low-level policy elaborates the short-term goal position of the agent within the outline of the region selected by the high-level command. The network structure and optimization suggested in our method are as concise as those of single-level methods. Experiments on the environment with various shapes of roads showed that our method finds the nearly optimal policies from early episodes, outperforming a baseline hierarchical reinforcement learning method, especially in narrow and complex roads. The resulting trajectories on the roads were similar to those of human strategies on the behavioral planning level.

cs.LG↗

arXiv2021

Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning

Diffusion-weighted MRI is nowadays performed routinely due to its prognostic ability, yet the quality of the scans are often unsatisfactory which can subsequently hamper the clinical utility. To overcome the limitations, here we propose a fully unsupervised quality enhancement scheme, which boosts the resolution and removes the motion artifact simultaneously. This process is done by first training the network using optimal transport driven cycleGAN with stochastic degradation block which learns to remove aliasing artifacts and enhance the resolution, then using the trained network in the test stage by utilizing bootstrap subsampling and aggregation for motion artifact suppression. We further show that we can control the trade-off between the amount of artifact correction and resolution by controlling the bootstrap subsampling ratio at the inference stage. To the best of our knowledge, the proposed method is the first to tackle super-resolution and motion artifact correction simultaneously in the context of MRI using unsupervised learning. We demonstrate the efficiency of our method by applying it to both quantitative evaluation using simulation study, and to in vivo diffusion-weighted MR scans, which shows that our method is superior to the current state-of-the-art methods. The proposed method is flexible in that it can be applied to various quality enhancement schemes in other types of MR scans, and also directly to the quality enhancement of apparent diffusion coefficient maps.

eess.IV↗

arXiv2020

Placement Retargeting of Virtual Avatars to Dissimilar Indoor Environments

Rapidly developing technologies are realizing a 3D telepresence, in which geographically separated users can interact with each other through their virtual avatars. In this paper, we present novel methods to determine the avatar's position in an indoor space to preserve the semantics of the user's position in a dissimilar indoor space with different space configurations and furniture layouts. To this end, we first perform a user survey on the preferred avatar placements for various indoor configurations and user placements, and identify a set of related attributes, including interpersonal relation, visual attention, pose, and spatial characteristics, and quantify these attributes with a set of features. By using the obtained dataset and identified features, we train a neural network that predicts the similarity between two placements. Next, we develop an avatar placement method that preserves the semantics of the placement of the remote user in a different space as much as possible. We show the effectiveness of our methods by implementing a prototype AR-based telepresence system and user evaluations.

cs.HC↗

arXiv2018

Characterization and Optical Properties of Erbium doped As2S3 Films Prepared by Multi-layer Magnetron Sputtering

As2S3 film doped with erbium is prepared using multi-layer magnetron sputtering. The optical properties were measured by reflectance spectroscopy, and its chemical composition is examined by x-ray photoelectron, Rutherford backscattering, and Raman spectroscopy. The results show that the refractive index and absorption coefficient follow closely to a sputtered As2S3 film, and there are no detectable Er-S clusters and photo-induced As2O3 in the film. Rutherford backscattering spectroscopy shows that the film is homogeneous, and revealed the concentration level of erbium, and the stoichiometry of the film. The deposition method was used to fabricate an integrated Erdoped As2S3 Mach-Zehnder Interferometer and the presence of active erbium ions in the waveguide is evident from the green luminescence it emitted when it was pumped by 1488 nm diode laser. This method is attractive because the doping process can produce an Er:As2S3 film that is close to the ideal stoichiometry of As2S3 with lower risk of photo-decomposed As2O3 crystals developing on the surface when the as-deposited film is exposed to the environment.

cond-mat.mtrl-sci↗

arXiv2017

Glass-Like Thermal Conductivity in Nanostructures of a Complex Anisotropic Crystal

Size effects on vibrational modes in complex crystals remain largely unexplored, despite their importance in a variety of electronic and energy conversion technologies. Enabled by advances in a four-probe thermal transport measurement method, we report the observation of glass-like thermal conductivity in ~20 nm thick single crystalline ribbons of higher manganese silicide, a complex, anisotropic crystal with a ~10 nm scale lattice constant along the incommensurate c axis. The boundary scattering effect is strong for many vibrational modes because of a strong anisotropy in their group velocities or diffusive nature, while confinement effects are pronounced for acoustic modes with long wavelengths along the c axis. Furthermore, the transport of the non-propagating, diffusive modes is suppressed in the nanostructures by the increased incommensurability between the two substructures as a result of the unusual composition of the nanostructure samples. These unique effects point to diverse, new approaches to suppressing the lattice thermal conductivity in complex materials.

cond-mat.mtrl-sci↗

arXiv2013

Thermal Conductivity and Phonon Transport in Suspended Few-Layer Hexagonal Boron Nitride

The thermal conductivity of suspended few-layer hexagonal boron nitride (h-BN) was measured using a micro-bridge device with built-in resistance thermometers. Based on the measured thermal resistance values of 11-12 atomic layer h-BN samples with suspended length ranging between 3 and 7.5 um, the room-temperature thermal conductivity of a 11-layer sample was found to be about 360 Wm-1K-1, approaching the basal plane value reported for bulk h-BN. The presence of a polymer residue layer on the sample surface was found to decrease the thermal conductivity of a 5-layer h-BN sample to be about 250 Wm-1K-1 at 300 K. Thermal conductivities for both the 5 layer and the 11 layer samples are suppressed at low temperatures, suggesting increasing scattering of low frequency phonons in thin h-BN samples by polymer residue.

cond-mat.mtrl-sci↗

arXiv2001

Storing unitary operators in quantum states

We present a scheme to store unitary operators with self-inverse generators in quantum states and a general circuit to retrieve them with definite success probability. The continuous variable of the operator is stored in a single-qubit state and the information about the kind of the operator is stored in classical states with finite dimension. The probability of successful retrieval is always 1/2 irrespective of the kind of the operator, which is proved to be maximum. In case of failure, the result can be corrected with additional quantum states. The retrieving circuit is almost as simple as that which handles only the single-qubit rotations and CNOT as the basic operations. An interactive way to transfer quantum dynamics, that is, to distribute naturally copy-protected programs for quantum computers is also presented using this scheme.

quant-ph↗

arXiv2000

Implementing unitary operators in quantum computation

We present a general method which expresses a unitary operator by the product of operators allowed by the Hamiltonian of spin-1/2 systems. In this method, the generator of an operator is found first, and then the generator is expanded by the base operators of the product operator formalism. Finally, the base operators disallowed by the Hamiltonian, including more than two-body interaction operators, are replaced by allowed ones by the axes transformation and coupling order reduction technique. This method directly provides pulse sequences for the nuclear magnetic resonance quantum computer, and can be generally applied to other systems.

quant-ph↗

arXiv1999

A Practical Method of Constructing Quantum Combinational Logic Circuits

We describe a practical method of constructing quantum combinational logic circuits with basic quantum logic gates such as NOT and general $n$-bit Toffoli gates. This method is useful to find the quantum circuits for evaluating logic functions in the form most appropriate for implementation on a given quantum computer. The rules to get the most efficient circuit are utilized best with the aid of a Karnaugh map. It is explained which rules of using a Karnaugh map are changed due to the difference between the quantum and classical logic circuits.

quant-ph↗

arXiv1999

Implementation of the refined Deutsch-Jozsa algorithn on a 3-bit NMR quantum computer

We implemented the refined Deutsch-Jozsa algorithm on a 3-bit nuclear magnetic resonance quantum computer, which is the meaningful test of quantum parallelism because qubits are entangled. All of the balanced and constant functions were realized exactly. The results agree well with theoretical predictions and clearly distinguish the balanced functions from constant functions. Efficient refocusing schemes were proposed for the soft z-pulse and J-coupling and it is proved that the thermal equilibrium state gives the same results as the pure state for this algorithm.

quant-ph↗