Výsledky vyhledávání - "Inoue, Tomoki"

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Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology

Autor: Inoue, Tomoki, Kubota, Koyo, Ikami, Tsubasa, Egami, Yasuhiro, Nagai, Hiroki, Kashikawa, Takahiro, Kimura, Koichi, Matsuda, Yu

Time-series clustering serves as a powerful data mining technique for time-series data in the absence of prior knowledge about clusters. A large amount of time-series data with large size has been acquired and used in various research fields. Hence,

Externí odkaz: http://arxiv.org/abs/2305.16656

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Invariant measures for random piecewise convex maps

Autor: Inoue, Tomoki, Toyokawa, Hisayoshi

We show the existence of Lebesgue-equivalent conservative and ergodic $\sigma$-finite invariant measures for a wide class of one-dimensional random maps consisting of piecewise convex maps. We also estimate the size of invariant measures around a sma

Externí odkaz: http://arxiv.org/abs/2303.10873

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Comparison theorems for invariant measures of random dynamical systems

Autor: Inoue, Tomoki

We study a random dynamical system such that one transformation is randomly selected from a family of transformations and then applied on each iteration. For such random dynamical systems, we consider estimates of absolutely continuous invariant meas

Externí odkaz: http://arxiv.org/abs/2303.09784

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Data-Driven Optimal Sensor Placement for High-Dimensional System Using Annealing Machine

Autor: Inoue, Tomoki, Ikami, Tsubasa, Egami, Yasuhiro, Nagai, Hiroki, Naganuma, Yasuo, Kimura, Koichi, Matsuda, Yu

Publikováno v: Mechanical Systems and Signal Processing, 188 (2023)

We propose a novel method for solving optimal sensor placement problem for high-dimensional system using an annealing machine. The sensor points are calculated as a maximum clique problem of the graph, the edge weight of which is determined by the pr

Externí odkaz: http://arxiv.org/abs/2205.05430

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Data-Driven Approach for Noise Reduction in Pressure-Sensitive Paint Data Based on Modal Expansion and Time-Series Data at Optimally Placed Points

Autor: Inoue, Tomoki, Matsuda, Yu, Ikami, Tsubasa, Nonomura, Taku, Egami, Yasuhiro, Nagai, Hiroki

We propose a noise reduction method for unsteady pressure-sensitive paint (PSP) data based on modal expansion, the coefficients of which are determined from time-series data at optimally placed points. In this study, the proper orthogonal decompositi

Externí odkaz: http://arxiv.org/abs/2103.00931

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