45 citations · 94 across the 5 of their papers we have counts for
5 papers
Time-series forecasting for nonlinear high-dimensional system using hybrid method combining autoencoder and multi-parallelized quantum long short-term memory and gated recurrent unit
Makoto Takagi, Ryuji Kokubo, Misato Kurosawa +7
A time-series forecasting method for high-dimensional spatial data is proposed. The method involves optimal selection of sparse sensor positions to efficiently represent the spatia…
Triple decomposition and sparse representation for noisy pressure-sensitive paint data
Koyo Kubota, Makoto Takagi, Tsubasa Ikami +5
Triple decomposition is a useful analytical method for extracting the mean value, organized coherent motion, and stochastic part from a fluctuating quantity. Although the pressure-…
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology
Tomoki Inoue, Koyo Kubota, Tsubasa Ikami +5
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 lar…
Data-Driven Optimal Sensor Placement for High-Dimensional System Using Annealing Machine
Tomoki Inoue, Tsubasa Ikami, Yasuhiro Egami +4
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…
Data-Driven Approach for Noise Reduction in Pressure-Sensitive Paint Data Based on Modal Expansion and Time-Series Data at Optimally Placed Points
Tomoki Inoue, Yu Matsuda, Tsubasa Ikami +3
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 opt…