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Kouichi Taji

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8 papers

2 author rows

TMLR Journal 2025 Journal Article

Change Point Detection in the Frequency Domain with Statistical Reliability

Akifumi Yamada
Tomohiro Shiraishi
Shuichi Nishino
Teruyuki Katsuoka
Kouichi Taji
Ichiro Takeuchi

Effective condition monitoring in complex systems requires identifying change points (CPs) in the frequency domain, as the structural changes often arise across multiple frequencies. This paper extends recent advancements in statistically significant CP detection, based on Selective Inference (SI), to the frequency domain. The proposed SI method quantifies the statistical significance of detected CPs in the frequency domain using $p$-values, ensuring that the detected changes reflect genuine structural shifts in the target system. We address two major technical challenges to achieve this. First, we extend the existing SI framework to the frequency domain by appropriately utilizing the properties of discrete Fourier transform (DFT). Second, we develop an SI method that provides valid $p$-values for CPs where changes occur across multiple frequencies. Experimental results demonstrate that the proposed method reliably identifies genuine CPs with strong statistical guarantees, enabling more accurate root-cause analysis in the frequency domain of complex systems.

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NeurIPS Conference 2025 Conference Paper

Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inference

Mizuki Niihori
Shuichi Nishino
Teruyuki Katsuoka
Tomohiro Shiraishi
Kouichi Taji
Ichiro Takeuchi

In real-world applications, anomaly detection (AD) often operates without access to anomalous data, necessitating semi-supervised methods that rely solely on normal data. Among these methods, deep $k$-nearest neighbor (deep $k$NN) AD stands out for its interpretability and flexibility, leveraging distance-based scoring in deep latent spaces. Despite its strong performance, deep $k$NN lacks a mechanism to quantify uncertainty—an essential feature for critical applications such as industrial inspection. To address this limitation, we propose a statistical framework that quantifies the significance of detected anomalies in the form of $p$-values, thereby enabling control over false positive rates at a user-specified significance level (e. g. ,0. 05). A central challenge lies in managing selection bias, which we tackle using Selective Inference—a principled method for conducting inference conditioned on data-driven selections. We evaluate our method on diverse datasets and demonstrate that it provides reliable AD well-suited for industrial use cases.

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ICML Conference 2024 Conference Paper

Statistical Test for Attention Maps in Vision Transformers

Tomohiro Shiraishi
Daiki Miwa
Teruyuki Katsuoka
Vo Nguyen Le Duy
Kouichi Taji
Ichiro Takeuchi

The Vision Transformer (ViT) demonstrates exceptional performance in various computer vision tasks. Attention is crucial for ViT to capture complex wide-ranging relationships among image patches, allowing the model to weigh the importance of image patches and aiding our understanding of the decision-making process. However, when utilizing the attention of ViT as evidence in high-stakes decision-making tasks such as medical diagnostics, a challenge arises due to the potential of attention mechanisms erroneously focusing on irrelevant regions. In this study, we propose a statistical test for ViT’s attentions, enabling us to use the attentions as reliable quantitative evidence indicators for ViT’s decision-making with a rigorously controlled error rate. Using the framework called selective inference, we quantify the statistical significance of attentions in the form of p-values, which enables the theoretically grounded quantification of the false positive detection probability of attentions. We demonstrate the validity and the effectiveness of the proposed method through numerical experiments and applications to brain image diagnoses.

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IROS Conference 2011 Conference Paper

Development and experiment of a kneed biped walking robot based on parametric excitation principle

Yoshihisa Banno
Yuji Harata
Kouichi Taji
Yoji Uno

Parametric excitation walking is one of methods to realize dynamic walking on a level ground. This method has first applied to a biped robot with telescopic legs and later to a robot with actuated knee joints. In parametric excitation walking, mechanical energy is increased by periodic up-and-down motion of the center of mass. While parametric excitation walking with telescopic legs has verified by an experimental robot, that with actuated knees has not yet as far as we know. The purpose of this paper is to present demonstration experiment of parametric excitation walking with a kneed biped robot. To do this, we develop an experimental kneed biped robot having four parallel legs with semicircular feet. In the experiment, the robot achieves walking on a level ground more than 15 steps. We also measure the movements of the robot during walking by a 3D motion capture and compare with simulation results.

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IROS Conference 2009 Conference Paper

Efficient parametric excitation walking with delayed feedback control

Yuji Harata
Fumihiko Asano
Kouichi Taji
Yoji Uno

In the passive dynamic walking proposed by McGeer, mechanical energy lost by heel strike is restored by transporting potential energy to kinetic energy as walking down a slope. When energy input is large such as an angle of slope is steep, bifurcation of walking period occurs. In parametric excitation walking, which is one method to realize passive dynamic-like walking on level ground, bifurcation has also been observed when walking speed is fast. Asano et al. have shown that bifurcation exerts an adverse influence upon walking performance by using rimless wheel model. In this paper, we apply delayed feedback control (DFC) originally used in chaos control to parametric excitation walking to suppress bifurcation. We show in numerical simulation that the proposed method makes two-period walking to one-period walking, and energy efficiency is improved. The analyses using Poincare¿ map reveal that the one-period walking with DFC is unstable periodic orbit and that the robot dealt in this paper satisfies the sufficient condition of applicability of DFC.

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IROS Conference 2009 Conference Paper

Optimal trajectory design for parametric excitation walking

Yoshihisa Banno
Yuji Harata
Kouichi Taji
Yoji Uno

Parametric excitation walking is one of methods that realize a passive dynamic like walking on the level ground. In parametric excitation walking, up-and-down motion of the center of mass restores mechanical energy and sustainable gait is generated. Walking ability and walking performance strongly depend on the reference trajectory of the center of mass. In this paper, we propose an optimization method for the reference trajectory of parametric excitation walking. There are two problems for optimization. One is that search space of a reference trajectory is inherently infinite dimensional. Another is that it takes long simulation time to generate steady gait for a given reference trajectory. Therefore, the proposing optimization method adopts the following strategy. For the former, we confine the reference trajectory to the quartic spline curve and take the parameter of spline curve as decision variables. For the latter, we discretize the search space and adopt a local search method usually used in combinational optimization problems. We apply the proposed method to a kneed biped robot, and optimize the reference trajectory of its swing leg.

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IROS Conference 2008 Conference Paper

Parametric excitation based gait generation for ornithoid walking

Yuji Harata
Fumihiko Asano
Kouichi Taji
Yoji Uno

The parametric excitation based gait generation method proposed by Asano et al. restores mechanical energy lost by heel-strike collisions. Harata et. al. applied this method to a kneed biped robot which is proper for the parametric excitation, and show that sustainable gait has been generated with only knee torque. A swing-leg of a kneed biped robot has similar mechanism to an acrobot, and many acrobots bends a joint in inverse direction like ornithoid walking. This suggests that inverse bending a knee restores more mechanical energy than forward bending like human walking, and hence, inverse bending may be more efficient. In this paper, we propose a parametric excitation based ornithoid gait generation method for a kneed biped robot, and show that it can walk sustainably by numerical simulation. We also show that parametric excitation based inverse bending walking is more efficient than parametric excitation based forward bending walking with respect to performance indices in our model.

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IROS Conference 2007 Conference Paper

Biped gait generation based on parametric excitation by knee-joint actuation

Yuji Harata
Fumihiko Asano
Zhiwei Luo
Kouichi Taji
Yoji Uno

Restoring mechanical energy lost by heel-strike collisions is necessary for stable gait generation. One principle to realize this is parametric excitation. Recently, Asano et al. applied this principle to a biped robot with telescopic-legs, and succeeded in generating a sustainable biped gait by computer simulation. In this paper, we deal with a model of a biped robot that has not only semicircular feet but also actuated knees. Though this robot has no actuator at the hip, knee actuators can sustain gait by parametric excitation. We first verify that an actuated knee can cause parametric excitation, and then show by computer simulation that the proposed biped robot can walk continuously with actuated knees only.

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