Search Results - "Hirofumi Nonaka"

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  1. 1

    Improving work detection by segmentation heuristics pre-training on factory operations video by Kataoka, Shotaro, Ito, Tetsuro, Iwaka, Genki, Oba, Masashi, Nonaka, Hirofumi

    Published in PloS one (07-06-2022)
    “…The measurement of work time for individual tasks by using video has made a significant contribution to a framework for productivity improvement such as value…”
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    Journal Article
  2. 2

    Magneto-optical diffractive deep neural network by Fujita, Takumi, Sakaguchi, Hotaka, Zhang, Jian, Nonaka, Hirofumi, Sumi, Satoshi, Awano, Hiroyuki, Ishibashi, Takayuki

    Published in Optics express (26-09-2022)
    “…We propose a magneto-optical diffractive deep neural network (MO-D 2 NN). We simulated several MO-D 2 NNs, each of which consists of five hidden layers made of…”
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    Journal Article
  3. 3

    Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem by Yamashiro, Hirochika, Nonaka, Hirofumi

    Published in Operations Research Perspectives (2021)
    “…Traditionally, mathematical optimization methods have been applied in manufacturing industries where production scheduling is one of the most important…”
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    Journal Article
  4. 4

    Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems by Tsuchida, Shuto, Nonaka, Hirofumi, Yamada, Noboru

    Published in Energies (Basel) (01-11-2022)
    “…An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and…”
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    Journal Article
  5. 5

    Metadata-Based Clustering and Selection of Metadata Items for Similar Dataset Discovery and Data Combination Tasks by Sakumoto, Takeshi, Hayashi, Teruaki, Sakaji, Hiroki, Nonaka, Hirofumi

    Published in IEEE access (2024)
    “…Data integration, which aims to solve problems and create new services by combining datasets, has attracted considerable attention. The discovery of similar…”
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    Journal Article
  6. 6

    Exploration of an Inflection Point of Ventilation Parameters with Anaerobic Threshold Using Strucchange by Aida, Takenori, Shionoya, Akira, Nonaka, Hirofumi, Hayami, Kouji, Uchiyama, Hisashi, Nagamori, Masahiro, Ohhashi, Satoshi, Kobayashi, Mai, Takayama, Tsugumi, Kimura, Shinji

    Published in Sensors (Basel, Switzerland) (31-03-2022)
    “…(1) Background: When measuring anaerobic work threshold (AT), the conventional V-slope method includes the subjectivity of the examiner, which cannot be…”
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    Journal Article
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  8. 8

    An analytical investigation of body parts more susceptible to aging and composition changes using statistical hypothesis testing by Mori, Masaya, Flores, Roberto Gonzalez, Kamimura, Hiroteru, Yamaura, Kentaro, Nonaka, Hirofumi

    Published in Healthcare analytics (New York, N.Y.) (01-06-2024)
    “…In recent years, age-related changes in body composition in the elderly are attracting attention. This is associated with a decline in physical functions and…”
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    Journal Article
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  10. 10

    Differences in Chinese and Western tourists faced with Japanese hospitality: a natural language processing approach by Alemán Carreón, Elisa Claire, Mendoza España, Hugo Alberto, Nonaka, Hirofumi, Hiraoka, Toru

    Published in Information technology & tourism (01-09-2021)
    “…Since culture influences expectations, perceptions, and satisfaction, a cross-culture study is necessary to understand the differences between Japan’s biggest…”
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    Journal Article
  11. 11

    Use of a Deep Learning Approach for the Sensitive Prediction of Hepatitis B Surface Antigen Levels in Inactive Carrier Patients by Kamimura, Hiroteru, Nonaka, Hirofumi, Mori, Masaya, Kobayashi, Taichi, Setsu, Toru, Kamimura, Kenya, Tsuchiya, Atsunori, Terai, Shuji

    Published in Journal of clinical medicine (13-01-2022)
    “…Deep learning is a subset of machine learning that can be employed to accurately predict biological transitions. Eliminating hepatitis B surface antigens…”
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    Journal Article
  12. 12

    Thermal design optimization of electronic circuit board layout with transient heating chips by using Bayesian optimization and thermal network model by Otaki, Daiki, Nonaka, Hirofumi, Yamada, Noboru

    “…•Bayesian optimization used with a lumped-capacitance thermal network model.•Layout of the unsteady heating chips on the electronic circuit board is…”
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    Journal Article
  13. 13

    Fire Protection Analysis and Potential Improvements for Wooden Cultural Heritage Sites in Japan by Kim, Kwang-Il, Konishi, Tadashi, Ziemba, Tomek, Nonaka, Hirofumi, Nam, Ki-Hun, Tanaka, Takeyoshi

    Published in Journal of disaster research (01-08-2015)
    “…Wooden cultural properties are exposed to risks such as forest fires and arson, due to natural disasters and social change. To protect these heritage sites…”
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    Journal Article
  14. 14

    Prediction of Microcystis Occurrences and Analysis Using Machine Learning in High-Dimension, Low-Sample-Size and Imbalanced Water Quality Data by Mori, Masaya, Gonzalez Flores, Roberto, Suzuki, Yoshihiro, Nukazawa, Kei, Hiraoka, Toru, Nonaka, Hirofumi

    Published in Harmful algae (01-08-2022)
    “…•Prediction and analysis of Microcystis occurrences in Hitotsuse Dam, Japan.•Feature Engineering and Feature Selection Algorithms applied to water quality…”
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    Journal Article
  15. 15

    Development of fabrication techniques for magneto-optical diffractive deep neural networks by Sakaguchi, Hotaka, Fujita, Takumi, Zhang, Jian, Sumi, Satoshi, Awano, Hiroyuki, Nonaka, Hirofumi, Ishibashi, Takayuki

    Published in IEEE transactions on magnetics (01-11-2023)
    “…Magneto-optical diffractive deep neural network (MO-D 2 NN) is a type of diffractive deep neural network that utilizes the magneto-optical effect of magnetic…”
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    Journal Article
  16. 16

    A study on patent term prediction by survival time analysis using neural hazard model by Marusaki, Koji, Nakai, Kensei, Kataoka, Shotaro, Kawano, Seiya, Hentona, Asahi, Sakumoto, Takeshi, Yamamoto, Yuta, Mori, Kaede, Nonaka, Hirofumi

    Published in Technological forecasting & social change (01-06-2024)
    “…Patent term is considered one of the factors that determine the private value of a patent. Predicting it can therefore be used as an indicator of corporate…”
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    Journal Article
  17. 17

    Thermomagnetic recording of highly Bi-substituted iron garnet film using scanning laser for spatial light modulation by Sakaguchi, Hotaka, Honma, Takuma, Zhang, Jian, Awano, Hiroyuki, Sumi, Satoshi, Nonaka, Hirofumi, Chafi, Fatima Zahra, Ishibashi, Takayuki

    Published in Japanese Journal of Applied Physics (01-10-2024)
    “…Abstract Spatial light modulations (SLM) utilizing the magneto-optical (MO) effect of magnetic materials are expected to offer fast switching and small pixel…”
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    Journal Article
  18. 18

    Measuring the influence of mere exposure effect of TV commercial adverts on purchase behavior based on machine learning prediction models by Alemán Carreón, Elisa Claire, Nonaka, Hirofumi, Hentona, Asahi, Yamashiro, Hirochika

    Published in Information processing & management (01-07-2019)
    “…•Models based on exposure time to television adverts have significantly lower predictability of actual purchase in comparison with models using demographic…”
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    Journal Article
  19. 19

    Improving work detection by segmentation heuristics pre-training on factory operations video by Shotaro Kataoka, Tetsuro Ito, Genki Iwaka, Masashi Oba, Hirofumi Nonaka

    Published in PloS one (01-01-2022)
    “…The measurement of work time for individual tasks by using video has made a significant contribution to a framework for productivity improvement such as value…”
    Get full text
    Journal Article
  20. 20

    Correlation analysis between financial data and patent score based on HITS algorithm by Nonaka, Hirofumi, Kubo, Daiki, Makoto, Kimura, Toru Hiraoka, Ota, Takahisa, Masuyama, Shigeru

    “…Patents can defend the inventions of enterprises and bring about economic benefits. Therefore, evaluation of patent value is useful for predicting financial…”
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    Conference Proceeding