arXiv subjects

Hisaichi Shibata

Publications and source records attributed to Hisaichi Shibata.

10 recordsLinked to original sources

arXiv2025

On the numerical simulations of electric field-enhanced solid-state electro-aerodynamic thrusters enabled with an insulated electrode

A solid-state electro-aerodynamic propulsion system applies a high electric potential difference between two electrodes to ionize air within the resulting electric field, accelerating the ions to generate thrust. Previously, the so-called ``decoupled thrusters'' have been proposed, in which ion generation and acceleration are spatially decoupled. Here, we argue that such decoupling is infeasible from the perspective of electric field lines if the diffusion effects are ignored, and consequently the driving method (e.g., dielectric barrier discharge) for the additional electrodes can be improved. Specifically, the use of auxiliary electrodes that do not discharge but induce ionization and attachment can be appropriate. This concept was named the electric field-enhanced solid-state electro-aerodynamic propulsion system. Furthermore, through numerical simulations, this concept showed improvement in thrust density while maintaining the thrust-to-power ratio.

physics.plasm-ph↗

arXiv2025

ANEMONE: a fully three-dimensional solid-state electro-aerodynamic propulsion system simulator

Solid-state electro-aerodynamic propulsion systems are devices that utilize atmospheric pressure corona discharge and have been actively researched in recent years as a means of achieving silent drones. However, these systems contain multiple, widely disparate time and spatial scales. Therefore, the governing equations of the systems, a three-component plasma fluid model that considers the presence of electrons, positive ions, and negative ions, constitute a stiff non-linear system of partial differential equations, challenging to solve. Here, we have developed an ANEMONE simulator capable of numerically estimating the corona inception voltage and energy conversion efficiency in three-dimensional solid-state electro-aerodynamic propulsion systems. Specifically, on the basis of the governing equations, we adopted the method of characteristics and the perturbation method to obtain the sub-problems. Furthermore, we have successfully obtained the integral equations, making the sub-problems easier to solve. Finally, we validated the prediction results based on the theoretical results in a previous study. Remarkably, ANEMONE is the first simulator in the world which predicted the two representative performance of fully three-dimensional propulsion systems.

physics.plasm-ph↗

arXiv2024

Theory of Hallucinations based on Equivariance

This study aims to acquire knowledge for creating very large language models that are immune to hallucinations. Hallucinations in contemporary large language models are often attributed to a misunderstanding of real-world social relationships. Therefore, I hypothesize that very large language models capable of thoroughly grasping all these relationships will be free from hallucinations. Additionally, I propose that certain types of equivariant language models are adept at learning and understanding these relationships. Building on this, I have developed a specialized cross-entropy error function to create a hallucination scale for language models, which measures their extent of equivariance acquisition. Utilizing this scale, I tested language models for their ability to acquire character-level equivariance. In particular, I introduce and employ a novel technique based on T5 (Text To Text Transfer Transformer) that efficiently understands permuted input texts without the need for explicit dictionaries to convert token IDs (integers) to texts (strings). This T5 model demonstrated a moderate ability to acquire character-level equivariance. Additionally, I discovered scale laws that can aid in developing hallucination-free language models at the character level. This methodology can be extended to assess equivariance acquisition at the word level, paving the way for very large language models that can comprehensively understand relationships and, consequently, avoid hallucinations.

cs.CL↗

arXiv2023

Playing the Werewolf game with artificial intelligence for language understanding

The Werewolf game is a social deduction game based on free natural language communication, in which players try to deceive others in order to survive. An important feature of this game is that a large portion of the conversations are false information, and the behavior of artificial intelligence (AI) in such a situation has not been widely investigated. The purpose of this study is to develop an AI agent that can play Werewolf through natural language conversations. First, we collected game logs from 15 human players. Next, we fine-tuned a Transformer-based pretrained language model to construct a value network that can predict a posterior probability of winning a game at any given phase of the game and given a candidate for the next action. We then developed an AI agent that can interact with humans and choose the best voting target on the basis of its probability from the value network. Lastly, we evaluated the performance of the agent by having it actually play the game with human players. We found that our AI agent, Deep Wolf, could play Werewolf as competitively as average human players in a villager or a betrayer role, whereas Deep Wolf was inferior to human players in a werewolf or a seer role. These results suggest that current language models have the capability to suspect what others are saying, tell a lie, or detect lies in conversations.

cs.AI↗

arXiv2022

Local Differential Privacy Image Generation Using Flow-based Deep Generative Models

Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To release and use medical images, we need an algorithm that can simultaneously protect privacy and preserve pathologies in medical images. To develop such an algorithm, here, we propose DP-GLOW, a hybrid of a local differential privacy (LDP) algorithm and one of the flow-based deep generative models (GLOW). By applying a GLOW model, we disentangle the pixelwise correlation of images, which makes it difficult to protect privacy with straightforward LDP algorithms for images. Specifically, we map images onto the latent vector of the GLOW model, each element of which follows an independent normal distribution, and we apply the Laplace mechanism to the latent vector. Moreover, we applied DP-GLOW to chest X-ray images to generate LDP images while preserving pathologies.

cs.CV↗

arXiv2022

Aging prediction using deep generative model toward the development of preventive medicine

From birth to death, we all experience surprisingly ubiquitous changes over time due to aging. If we can predict aging in the digital domain, that is, the digital twin of the human body, we would be able to detect lesions in their very early stages, thereby enhancing the quality of life and extending the life span. We observed that none of the previously developed digital twins of the adult human body explicitly trained longitudinal conversion rules between volumetric medical images with deep generative models, potentially resulting in poor prediction performance of, for example, ventricular volumes. Here, we establish a new digital twin of an adult human body that adopts longitudinally acquired head computed tomography (CT) images for training, enabling prediction of future volumetric head CT images from a single present volumetric head CT image. We, for the first time, adopt one of the three-dimensional flow-based deep generative models to realize this sequential three-dimensional digital twin. We show that our digital twin outperforms the latest methods of prediction of ventricular volumes in relatively short terms.

eess.IV↗

arXiv2021

X2CT-FLOW: Maximum a posteriori reconstruction using a progressive flow-based deep generative model for ultra sparse-view computed tomography in ultra low-dose protocols

Ultra sparse-view computed tomography (CT) algorithms can reduce radiation exposure of patients, but those algorithms lack an explicit cycle consistency loss minimization and an explicit log-likelihood maximization in testing. Here, we propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruction of a three-dimensional (3D) chest CT image from a single or a few two-dimensional (2D) projection images using a progressive flow-based deep generative model, especially for ultra low-dose protocols. The MAP reconstruction can simultaneously optimize the cycle consistency loss and the log-likelihood. The proposed algorithm is built upon a newly developed progressive flow-based deep generative model, which is featured with exact log-likelihood estimation, efficient sampling, and progressive learning. We applied X2CT-FLOW to reconstruction of 3D chest CT images from biplanar projection images without noise contamination (assuming a standard-dose protocol) and with strong noise contamination (assuming an ultra low-dose protocol). With the standard-dose protocol, our images reconstructed from 2D projected images and 3D ground-truth CT images showed good agreement in terms of structural similarity (SSIM, 0.7675 on average), peak signal-to-noise ratio (PSNR, 25.89 dB on average), mean absolute error (MAE, 0.02364 on average), and normalized root mean square error (NRMSE, 0.05731 on average). Moreover, with the ultra low-dose protocol, our images reconstructed from 2D projected images and the 3D ground-truth CT images also showed good agreement in terms of SSIM (0.7008 on average), PSNR (23.58 dB on average), MAE (0.02991 on average), and NRMSE (0.07349 on average).

eess.IV↗

arXiv2020

On the Matrix-Free Generation of Adversarial Perturbations for Black-Box Attacks

In general, adversarial perturbations superimposed on inputs are realistic threats for a deep neural network (DNN). In this paper, we propose a practical generation method of such adversarial perturbation to be applied to black-box attacks that demand access to an input-output relationship only. Thus, the attackers generate such perturbation without invoking inner functions and/or accessing the inner states of a DNN. Unlike the earlier studies, the algorithm to generate the perturbation presented in this study requires much fewer query trials. Moreover, to show the effectiveness of the adversarial perturbation extracted, we experiment with a DNN for semantic segmentation. The result shows that the network is easily deceived with the perturbation generated than using uniformly distributed random noise with the same magnitude.

cs.LG↗

arXiv2018

Dynamic mode decomposition using a Kalman filter for parameter estimation

A novel dynamic mode decomposition (DMD) method based on a Kalman filter is proposed. This paper explains the fast algorithm of the proposed Kalman filter DMD (KFDMD) in combination with truncated proper orthogonal decomposition for many-degree-of-freedom problems. Numerical experiments reveal that KFDMD can estimate eigenmodes more precisely compared with standard DMD or total least-squares DMD (tlsDMD) methods for the severe noise condition if the nature of the observation noise is known, though tlsDMD works better than KFDMD in the low and medium noise level. Moreover, KFDMD can track the eigenmodes precisely even when the system matrix varies with time similar to online DMD, and this extension is naturally conducted owing to the characteristics of the Kalman filter. In summary, the KFDMD is a promising tool with strong antinoise characteristics for analyzing sequential datasets.

physics.flu-dyn↗

arXiv2018

Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising

A new dynamic mode decomposition (DMD) method is introduced for simultaneous online system identification and denoising in conjunction with the adoption of an extended Kalman filter algorithm\color{black}. The present paper explains the extended-Kalman-filter-based DMD (EKFDMD) algorithm and illustrates that EKFDMD requires significant numerical resources for many-degree-of-freedom (many-DoF) problems and that the combination with truncated proper orthogonal decomposition (trPOD) helps us to apply the EKFDMD algorithm to many-DoF problems. The numerical experiments of the present study illustrate that EKFDMD can estimate eigenvalues from a noisy dataset with a few DoFs better than or as well as the existing algorithms, whereas EKFDMD can also denoise the original dataset online. In particular, EKFDMD performs better than existing algorithms for the case in which system noise is present. The EKFDMD with trPOD can be successfully applied to many-DoF problems, including a fluid-problem example, and the results reveal the superior performance of system identification and denoising. Note that these superior results are obtained despite being an online procedure.

physics.flu-dyn↗