arXiv subjects

Taichi Tanaka

Publications and source records attributed to Taichi Tanaka.

5 recordsLinked to original sources

arXiv2026

ERG Kernels on Multiply Connected Configuration Spaces

In the functional-integral formulation of Euclidean field theory, exact renormalization group (ERG) transformations are realized by functional-integral kernels. Unlike the ERG flow equations that describe infinitesimal ERG transformations, these ERG kernels explicitly depend on the global topology of the configuration space. This paper explores this topology dependence for multiply connected configuration spaces. We show that the ERG kernel is in general given by a weighted sum of kernels on its universal covering space, where the weight factors are determined by a one-dimensional representation of the fundamental group. These weight factors are shown never to be renormalized under the ERG. We also show that these factors can be interpreted as Aharonov-Bohm phases with respect to a background magnetic flux penetrating the infinite-dimensional configuration space. From this viewpoint, a normalization condition for ERG transformations corresponds to a flux-quantization condition, which is equivalent to the level-quantization condition for Wess-Zumino-Witten terms in nonlinear sigma models. Finally, we present an alternative gauge-equivalent form of the ERG flow equation that incorporates this topological information locally.

hep-th↗

arXiv2025

Mitigating the Impact of Electrode Shift on Classification Performance in Electromyography-Based Motion Prediction Using Sliding-Window Normalization

Electromyography (EMG) signals are used in many applications, including prosthetic hands, assistive suits, and rehabilitation. Recent advances in motion estimation have improved performance, yet challenges remain in cross-subject generalization, electrode shift, and daily variations. When electrode shift occurs, both transfer learning and adversarial domain adaptation improve classification performance by reducing the performance gap to -1\% (eight-class scenario). However, additional data are needed for re-training in transfer learning or for training in adversarial domain adaptation. To address this issue, we investigated a sliding-window normalization (SWN) technique in a real-time prediction scenario. This method combines z-score normalization with a sliding-window approach to reduce the decline in classification performance caused by electrode shift. We validated the effectiveness of SWN using experimental data from a target trajectory tracking task involving the right arm. For three motions classification (rest, flexion, and extension of the elbow) obtained from EMG signals, our offline analysis showed that SWN reduced the differential classification accuracy to -1.0\%, representing a 6.6\% improvement compared to the case without normalization (-7.6\%). Furthermore, when SWN was combined with a strategy that uses a mixture of multiple electrode positions, classification accuracy improved by an additional 2.4\% over the baseline. These results suggest that SWN can effectively reduce the performance degradation caused by electrode shift, thereby enhancing the practicality of EMG-based motion estimation systems.

eess.SP↗

arXiv2024

Net-Zero Energy House-oriented Linear Programming for the Sizing Problem of Photovoltaic Panels and Batteries

The global drive towards carbon neutrality has led to a significant increase in the number of power plants based on renewable energy sources (RES). Concurrently, numerous households are adopting RES to generate their own energy, aiming to decrease both electricity costs and carbon footprints. To support these users, many papers have been devoted to developing optimal investment strategies for residential energy systems. However, there is still a significant gap as these studies often neglect important aspects like carbon neutrality. For this reason, in this paper, we explore the concept of net-zero energy houses (ZEHs) -- houses designed to have an annual net energy consumption around zero -- by presenting a constrained optimization problem to find the optimal number of photovoltaic panels and the optimal size of the battery system for home integration. Solving this constrained optimization problem is difficult due to its nonconvex constraints. Nevertheless, by applying a series of transformations, we reveal that it is possible to find an equivalent linear programming (LP) problem which is computationally tractable. The attainment of ZEH can be tackled by introducing a single constraint in the optimization problem. Additionally, we propose a sharing economy approach to the investment problem, offering a strategy that could potentially reduce investment costs and facilitate the attainment of ZEH more efficiently. Finally, we apply the proposed frameworks to a neighborhood in Japan as a case study, demonstrating the potential for long-term ZEH attainment. The results show that, under the right incentive, users can achieve ZEH, reduce their electricity costs and have a minimal impact on the main grid.

math.OC↗

arXiv2023

Stochastic Optimal Investment Strategy for Net-Zero Energy Houses

In this research, we investigate Net-Zero Energy Houses (ZEH), which harness regionally produced electricity from photovoltaic(PV) panels and fuel cells, integrating them into a local power system in pursuit of achieving carbon neutrality. This paper examines the impact of electricity sharing among users who are working towards attaining ZEH status through the integration of PV panels and battery storage devices. We propose two potential scenarios: the first assumes that all users individually invest in storage devices, hence minimizing their costs on a local level without energy sharing; the second envisions cost minimization through the collective use of a shared storage device, managed by a central manager. These two scenarios are formulated as a stochastic convex optimization and a cooperative game, respectively. To tackle the stochastic challenges posed by multiple random variables, we apply the Monte Carlo sample average approximation (SAA) to the problems. To demonstrate the practical applicability of these models, we implement the proposed scenarios in the Jono neighborhood in Kitakyushu, Japan.

econ.GN↗

arXiv2022

Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography

Many researchers have used machine learning models to control artificial hands, walking aids, assistance suits, etc., using the biological signal of electromyography (EMG). The use of such devices requires high classification accuracy of machine learning models. One method for improving the classification performance of machine learning models is normalization, such as z-score. However, normalization is not used in most EMG-based motion prediction studies, because of the need for calibration and fluctuation of reference value for calibration (cannot re-use). Therefore, in this study, we proposed a normalization method that combines sliding-window analysis and z-score normalization, that can be implemented in real-time processing without need for calibration. The effectiveness of this normalization method was confirmed by conducting a single-joint movement experiment of the elbow and predicting its rest, flexion, and extension movements from the EMG signal. The proposed normalization method achieved a mean accuracy of 64.6%, an improvement of 15.0% compared to the non-normalization case (mean of 49.8%). Furthermore, to improve practical applications, recent research has focused on reducing the user data required for model learning and improving classification performance in models learned from other people's data. Therefore, we investigated the classification performance of the model learned from other's data. Results showed a mean accuracy of 56.5% when the proposed method was applied, an improvement of 11.1% compared to the non-normalization case (mean of 44.1%). These two results showed the effectiveness of the simple and easy-to-implement method, and that the classification performance of the machine learning model could be improved.

eess.SP↗