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T. Nagata

4 papers hereh-index 12456 citations54 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • eess.SP2
  • eess.SY1
  • physics.chem-ph1
same name
  • T. Nagata — 85 papers, h 40
  • T. Nagata — 9 papers, h 14
  • T. Nagata — 7 papers, h 24
  • T. Nagata — 4 papers, h 6
  • T. Nagata — 4 papers, h 26
  • T. Nagata — 2 papers, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedDeterminant-based Fast Greedy Sensor Selection Algorithm

85 citations · 186 across the 4 of their papers we have counts for

collaborators

4 papers

physics.chem-ph2020★ 16 cited

Effect of Oxygen Mole Fraction on Static Properties of Pressure-Sensitive Paint

Tomohiro Okudera, Takayuki Nagata, Miku Kasai +3

The effects of oxygen mole fraction on the static properties of pressure-sensitive paint (PSP) were investigated. Sample coupon tests using a calibration chamber were conducted for…

eess.SY2020★ 40 cited

Data-driven sparse sensor placement based on A-optimal design of experiment with ADMM

Takayuki Nagata, Taku Nonomura, Kumi Nakai +3

The present study proposes a sensor selection method based on the proximal splitting algorithm and the A-optimal design of experiment using the alternating direction method of mult…

eess.SP2020★ 45 cited

Effect of Objective Function on Data-Driven Greedy Sparse Sensor Optimization

Kumi Nakai, Keigo Yamada, Takayuki Nagata +2

The selection problem of an optimal set of sensors estimating the snapshot of high-dimensional data is considered. The objective functions based on various criteria of optimal desi…

eess.SP2019★ 85 cited

Determinant-based Fast Greedy Sensor Selection Algorithm

Yuji Saito, Taku Nonomura, Keigo Yamada +5

In this paper, the sparse sensor placement problem for least-squares estimation is considered, and the previous novel approach of the sparse sensor selection algorithm is extended.…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.