2 0 0 0 OA 皮膚色の評価におけるCIEDE2000色差式の有用性
- 著者
- 秋本 眞喜雄 池田 光里 中野 慎也 ボーマン サムエル 早津 勇一 畑 三恵子 前田 憲寿
- 出版者
- 自動制御連合講演会
- 雑誌
- 自動制御連合講演会講演論文集 第57回自動制御連合講演会
- 巻号頁・発行日
- pp.1115-1118, 2014 (Released:2016-03-02)
皮膚色の違いを定量的に評価する方法はCIELAB空間の色差式が利用されているが,色差式から予測される色差と視感で感じられる色差とは必ずしも一致しない.この問題を解決するために,CIEはCIEDE2000色差式を推奨しているが,十分に活用されていない.我々は皮膚色の評価に新しい色差式を利用した場合の有用性について検討した.CIEDE2000色差式はCIELAB色差式に色相,明度,彩度に対応する係数を付加して色差を算出する方法である.CIE2000色差式は視感的な評価と比較的よく対応することが示唆され,皮膚色評価の標準化に寄与できると考える.
The thirteenth generation of International Geomagnetic Reference Field (IGRF-13) was released by International Association of Geomagnetism and Aeronomy (IAGA) in December, 2019. Prior to the release, we submitted a secular variation (SV) candidate model for IGRF-13 using a data assimilation scheme and a magnetohydrodynamic (MHD) dynamo simulation code (Minami et al. submitted to EPS special issue for IGRF-13). Our candidate SV model was evaluated by IAGA Division V Working Group V-MOD and contributed to the final IGRF-13SV model with the optimized weight. This became the first contribution to the IGRF community from research groups in Japan. This was enabled by bilateral corroboration between Japan and France; in our data assimilation scheme, we used the French main field model (Ropp et al. 2020), which was developed from magnetic observatory hourly means, and CHAMP and Swarm-A satellite data. We adopted an iterative assimilation algorithm based on four-dimensional ensemble-based variational method (4DEnVar) (Nakano 2020), which linearizes outputs of our MHD dynamo simulation (Takahashi 2012; 2014) with respect to the deviation from a dynamo state vector at an initial condition. The data vector for the assimilation consists of the poloidal scalar potential of the geomagnetic field at the Earth’s core surface, and flow velocity field slightly below the core surface, which was calculated by presuming magnetic diffusion in the boundary layer and tangentially magnetostrophic flow below it (Matsushima 2020). Dimensionless time of numerical geodynamo was adjusted to the actual time by comparison of secular variation time scales. For estimation of our IGRF-13SV candidate model, we first generated an ensemble of dynamo simulation results from a free dynamo run. We then assimilated the ensemble to the data with a 10-year assimilation window from 2009.50 to 2019.50 through iterations, and finally forecasted future SV by linear combination of the future extension parts of the ensemble members. We generated our final SV candidate model by linear fitting for the best linear combination of the ensemble MHD dynamo simulation members from 2019.50 to 2025.00. We derived errors of our SV candidate model by one standard deviation of SV histograms based on all the ensemble members. In the presentation, we plan to report our IGRF project through the bilateral corroboration with France, and describe our SV candidate model.
The International Geomagnetic Reference Field (IGRF) is a standard mathematical description in terms of spherical harmonic coefficients, known as the Gauss coefficients, for the Earth’s main magnetic field and its secular variation. On December 19, 2019, the working group V-MOD of the International Association of Geomagnetism and Aeronomy (IAGA) released the 13th generation of IGRF, which consists of three constituents; a Definitive IGRF (DGRF) for 2015, an IGRF for 2020, and a secular variation (SV) model from 2020 to 2025. We submitted a candidate model for SV from 2020 to 2025, relying on our strong points, such as geodynamo numerical simulation, data assimilation, and core surface flow modeling.We can estimate core flow near the core-mantle boundary (CMB)from distribution of geomagnetic field and its secular variation. Such a flow model can be obtained for actual physical parameters of the Earth. However, numerical simulations of geodynamo were carried out for physical parameters far from actual ones. Therefore, a core flow model to be used for data assimilation had to be obtained on a condition relevant to the numerical simulations. To obtain the candidate model for SV, we adjusted time-scale of a geodynamo model (Takahashi 2012, 2014) to that of actual SV of geomagnetic field as given by Christensen and Tilgner (2004).In this presentation, we first investigate temporal variations of geomagnetic field due to the magnetic diffusion only. Next, we investigate temporal variations of geomagnetic field due to the motional induction caused by some core flow models as well as the magnetic diffusion. Then we compare secular variations of geomagnetic field forecasted by these methods.
1 0 0 0 OA Inferring geomagnetic secular variation using MHD/kinematic dynamo modeling with data assimilation
Secular variation (SV) of the Earth's magnetic field is governed by the advection and diffusion processes of the magnetic field within the fluid outer core. The IGRF (International Geomagnetic Reference Field) offers the average SV for the next five years to come, which has been estimated in various methods. In general, forecasting the evolution of a non-linear system like the geodynamo in the Earth's core is an extremely difficult task, because the magnetic field generation processes are controlled by the complex interaction of the core flows and the generated magnetic field. Data assimilation has been a promising scheme forecasting the geomagnetic SV as demonstrated in literatures (Kuang 2010, Fournier et al. 2015), where time dependency is controlled by a numerical dynamo model. While Ensemble Kalman Filter (EnKF) has been a popular method for data assimilation in geomagnetism, we apply a different data assimilation procedure, that is, four-dimensional, ensemble-based variational scheme, 4DEnVar. Applying the 4DEnVar scheme iteratively, we have derived a candidate SV model for the latest version of the IGRF. In evaluating SV, two forecasting strategies are tested, in which core flows are assumed to be steady or time-dependent. The former approach is favored in Fournier et al. (2015), where the magnetic field evolves kinematically by the flows prescribed to be time-independent in the initialization step. On the other hand, we have adopted linear combination of magnetohydrodynamic (MHD) models to construct a candidate as the best forecast (Minami et al. 2020). It is likely that which strategy is more suitable to forecasting SV depends on assimilation scheme and/or numerical dynamo model. However, we have little knowledge on the issue at present. In this study, we investigate results of MHD and kinematic dynamo runs with a 4DEnVar scheme in order to have a grasp of the properties of the scheme in the 5-year forecast process. Also, MHD and kinematic runs are compared to infer internal dynamics responsible for SV in the geomagnetic field.
1 0 0 0 OA ROISデータサイエンス事業の取り組みにおける図書館員の役割
1 0 0 0 IR 難聴者に対する態度と失敗観 : 価値観の変容から他者の評価へ
- 著者
- 栗田 季佳 中野 慎也 荒川 哲郎 名畑 康之 勝谷 紀子 KURITA Tokika NAKANO Shinya ARAKAWA Tetsuro NABATA Yasuyuki KATSUYA Noriko
- 出版者
- 三重大学教育学部
- 雑誌
- 三重大学教育学部研究紀要. 自然科学・人文科学・社会科学・教育科学・教育実践 (ISSN:18802419)
- 巻号頁・発行日
- vol.67, pp.149-160, 2016-03-22
本研究は、難聴者への否定的な態度にどのような価値観が関わっているかを探索的に調べた。研究1では、難聴の子を取り巻く周囲の子ども達が変容した学級について、担任教師へインタビューを行った。インタビューより、周囲の子ども達は、難聴の子どもに対する態度だけでなく、自閉症スペクトラム障害の子どもへの態度や、授業での様子など、学級全体の雰囲気が変わっていったことが語られた。担任教師の教育観から、子ども達の中で失敗に対する価値観に変化が生じていたことが推測された。研究2では、直接そのことを調べるため、質問紙調査によって失敗観と他者への態度に関連があるかを調べた。その際、難聴者だけでなく、健常者、吃音者も対象とした。その結果、否定的な失敗観が強い人ほど、他者との交流に対して抵抗感を感じており、その傾向は相手が健常者である場合よりも難聴者や吃音者である場合の方が強かった。これらより、難聴者を含む障害者への偏見や他者への寛容さの問題に対して、自己の価値観と向き合うことが重要であることが示唆された。
1 0 0 0 OA 大地震に伴う微気圧変動へのデータ同化の応用
- 著者
- 長尾 大道 中野 慎也 樋口 知之
- 出版者
- 日本学術会議 「機械工学委員会・土木工学・建築学委員会合同IUTAM分科会」
- 雑誌
- 理論応用力学講演会 講演論文集 第59回理論応用力学講演会
- 巻号頁・発行日
- pp.173, 2010 (Released:2011-01-21)
2008年6月14日に発生した岩手・宮城内陸地震による微気圧変動が、震源域から400km以上離れたCTBT夷隅微気圧観測点で検出された。この音波波形は、適切な固体地球と大気の1次元結合モデルおよび断層モデルを設定することにより、30秒以上の長周期帯では十分に説明できることが分かっている(Nagao et al. [2008])。本研究では、データ同化の手法を用いてこれらのモデルパラメータの事後分布を精密に推定し、地震データとは異なる情報を持つと考えられる微気圧データが、固体地球科学にどのような貢献をもたらすことができるかを検討する。具体的には、地震と微気圧の観測波形および固体地球-大気モデルのノーマルモードから求めたシミュレーション波形とを、粒子フィルタ法およびMCMC法を組み合わせたハイブリッド法により比較し、モデルパラメータの事後分布を推定する。特に微気圧データを含めた場合と含めない場合とで比較し、微気圧データが震源パラメータの事後分布に与える影響を調べる。