九州大学 研究者情報
論文一覧
西井 龍映(にしい りゆうえい) データ更新日:2018.06.01

教授 /  マス・フォア・インダストリ研究所 数学テクノロジー先端研究部門


原著論文
1. Koda,S. , Melgani,F. , Zeggada,A. and Nishii,R., Spatial and structured SVM for multilabel image classification, IEEE Transactions on Geoscience and Remote Sensing, 2018.01.
2. Koda,S., Onda,Y., Matsui,H., Takahagi,K., Uehara-Yamaguchi,Y., Shimizu,M., Inoue,K., Yoshida,T., Sakurai,T., Honda,H.,Eguchi,S.,Nishii,R. and Mochida,K., Diurnal transcriptome and gene network represented through sparse modeling in brachypodium distachyon, Frontiers in plant science, 10.3389/fpls.2017.02055, 2017.11, [URL], 試験植物ブラキポディウムの4時間ごとに計測した遺伝子発現量の時系列データに対し,時間遅れを伴う他の遺伝子発現量および自己回帰項を含む回帰モデルを想定した.母数推定にはグループSCADによるスパース推定,および安定的なネットワーク推定を行い,ハブ遺伝子を検出した.これにより遺伝子間ネットワークができた..
3. Shojiro Tanaka, Ryuei Nishii, Incorporation of gridded data into the analysis of remotely-sensed images:: Basic quantitative strategy to analyze defoerestation by population growth., IEEE Geosciece and Remote Sensing Society, 10.1109/IGARSS.2015.7326332, 2015.07.
4. Ryuei Nishii, Shojiro Tanaka, Unified modeling based on SVM and SVR for prediction of forest area ratio by human population density and relief energy., IEEE Geosciece and Remote Sensing Society, 10.1109/IGARSS.2015.7326331, 2015.07.
5. Pan Qin, Ryuei Nishii, Statistical prediction of Dst index by solar wind data and t-distributions., IEEE Transactions on Plasma Sciences, 43, 11, 3915, 10.1109/TPS.2015.2485661, 2015.11.
6. Pan Qin, T. Yamasaki, Ryuei Nishii, Statistical detection of the influence of solar activities to weak earthquakes, Pacific Journal of Math-for-Industry, 6:6, , 10.1186/s40736-014-0006-9, 2014.07.
7. Ryuei Nishii, Pan Qin, Daisuke Uchi, Contextual Bayesian unmixing of geospatial imagery based on logistic regression, IEEE Geosciece and Remote Sensing Society, , 10.1109/IGARSS.2014.6946760, 2014.07.
8. Shojiro Tanaka, Ryuei Nishii, Re-evaluation of topographic attributes with human population in deforestation framework with spatal dependency, IEEE Geosciece and Remote Sensing Society, , 10.1109/IGARSS.2014.6947237, 2014.07, .
9. Ryuei Nishii, Pan Qin, Daisuke Uchi, Nonlinear Bayesian unmixing of geospatial data based on Gibbs sampling, IEEE Geosciece and Remote Sensing Society, , 10.1109/IGARSS.2013.6723483, 2013.07, Image classification has a long history for estimating land-cover categories by feature vectors, and various methods have been proposed from many viewpoints; statistics, machine learning and others. Multivariate normal distributions are frequently used to model feature distributions.Also, it is known that contextual classification methods based on Markov random fields (MRF) improve non-contextual classifiers successfully.
If low-spatial resolution images are given, a pixel may be covered by two or more land-cover categories. Thus, we are required to estimate fractions of categories covering the pixel.This issue is called unmixing, and it is usually solved by the linear equationderived by the assumption such thatthe observed feature vector is composed by a convex combination of the category reflectance signatures.
In the recent years, several Bayesian approaches were proposed for the unmixing problem. Markov chain Monte Carlo (MCMC) methods were applied to linear unmixing of hyperspectral images. A hierarchical Bayesian algorithm proposed by combining bilinear models with MCMC was also discussed for nonlinear unmixing to handle scattering effects.
In this paper, we propose new Bayesian models for unmixing, and fraction vectors will be estimated by Gibbs sampling. Section 2 gives data specification observed by the grid-cell system, and Section 3 reviews logistic regression.Section 4 provides two Bayesian models, and posterior distributions are derived for Gibbs sampling. Section 5 gives a numerical example. Section 6 concludes the paper..
10. Shojiro Tanaka, Ryuei Nishii, Effect evaluation of topographic attributes on forest coverage ratios based on digital elevation model
, IEEE Geosciece and Remote Sensing Society, , 10.1109/IGARSS.2013.6723370, 2013.07, .
11. Ryuei Nishii, Shojiro Tanaka, Modeling and inference of forest coverage ratio using zero-one inflated distributions with spatial dependence., Environmental and Ecological Statistics, 10.1007/s10651-012-0227-y, 2013.06, This paper explores statistical modeling of forest area with two covariates. The forest coverage ratio of grid-cell data was modeled by taking human population density and relief energy into account. The likelihood of the forest ratios was decomposed into the product of two likelihoods. The first likelihood was due to trinomial logistic distributions on three categories: the forest ratios take zero, or one, or values between zero and one. The second one was due to a logistic-normal regression model for the ratios between zero and one. This model was applied to real grid-cell data and resulted in remarkably interesting implications..
12. 小平 剛央, 中本 尊元, 小池 真人, 天野 浩平, 西井 龍映, 秦 攀, 逐次実験計画法による車体構造の複合領域最適化手法, 自動車技術会, 44, 2, 541, 2013.03, [URL], Multidisciplinary Design Optimization (MDO) using Design of Experiments (DoE) and approximation model is a very efficient methodology for reducing weight of car body structure. However, one of the important issues on MDO is reduction of a large number of samples of DoE to generate high accurate approximation model, especially for non-linear phenomena such as crashworthiness. This paper proposes a Sequential DoE to reduce the number of samples. It is an additional sampling method and puts appropriate sample points in discrete design space for multi-approximation model. It can finally reduce the number of sample by half of the conventional method..
13. Ryuei Nishii, Daiki Miyata, Shojiro Tanaka, Statistical frameworking of deforestation models based on human population density and relief energy , SPIE Proceedings , 8538, 10.1117/12.974583, 2012.11, [URL].
14. Pan Qin, Ryuei Nishii, Kojiro Yanase, Multi-hour-ahead Prediction of The Disturbance Storm Time Index using Nonlinear Autoregressive Models with Exogenous Variables, Remote Sensing for Environmental Sciences, 2012.08.
15. 宮田 大毅, 西井 龍映,田中 章司郎, 森林被覆率の非線形回帰モデリング, 統計数理, 60, 1, 109-119, 2012.06, 森林減少の定量的評価のため,森林被覆率を人口密度および土地の起伏量により説明する精密な回帰モデルを考察した.
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16. Ryuei Nishii, Statistical modeling of hyper-dimensional data and spatio-temporal data, COE Lecture Note , 32, 69, 2011.09, [URL].
17. Q. Pan and R. Nishii, Selection of ARX models estimated by the penalized weighted least squares method. , Bulletin of Informatics and Cybernetics, 42, 35-43, 2010.12, 時系列データに基づく回帰モデルにおいて,ある範囲の目的変数(例:絶対値が大きい)について高精度な予測がしたい場合を考える.この場合の回帰係数の推定では,最小二乗法より重み付き最小2乗法がふさわしい.さらに罰則付の重み付き最小2乗法を考えると安定した予測が得られる.ここでは,自己回帰(AR)の時間遅れ次数,外部説明変数(X)の時間遅れ次数,さらには説明変数自体の選択のための一般化モデル評価基準を導出した.提案手法はシミュレーションデータや実データに対して有効に機能することが示された..
18. Ryuei Nishii, Shojiro Tanaka, An application of novel zero-one inflated distributions with spatial dependence for the deforestation modeling, IEEE Geosciece and Remote Sensing Society, 3445, 10.1109/IGARSS.2010.5654397 , 2010.07, This paper considers statistical modeling of deforestation.
Forest coverage ratio of grid-cell data was modeled by two covariates: human population density and relief energy. Conditional likelihood of the forest ratios given the covariates was decomposed by product of two likelihoods. The first one is due to trinomial logistic distributions on three classes: the ratios take zero, one or values between zero and one. The second one is due to a logistic-normal regression model for the ratios between zero and one. This model was applied to the real grid-cell data, and led remarkably interesting implications.
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19. Pan Qin, Ryuei Nishii, Tasashi Nakagawa, Takayoshi Nakamoto, ARX models for time-varying systems estimated by recursive penalized weighted least squares method, Journal of Math-for-Industry, 2, A, 114, 2010.04.
20. S. Tanaka and R. Nishii, Non-linear regression models to identify functional forms of deforestation in East Asia, IEEE Transactions on Geoscience and Remote Sensing (in press), 2009.08.
21. R. Nishii, Y. Sawamura, and T. Ozaki, Semi-supervised contextual unmixing of geospatial data, International Association for Statistical Computing, 2008.12.
22. R. Nishii, c, and S. Kawaguchi, Contextual unmixing of geospatial data based on Gaussian mixture models and Markov random fields, IEEE Geoscience and Remote Sensing Society, 2008.07.
23. 西井龍映, 江口真透, 統計的学習理論による多重分光画像の画素判別, 計算機統計学, 2008.01.
24. S. Kawaguchi, R. Nishii, Hyperspectral image classification by Bootstrap AdaBoost with random decision stumps, IEEE Transactions on Geoscience and Remote Sensing, 2007.11.
25. R. Nishii and S. Eguchi, Supervised image classification of multispectral images based on statistical machine learning, Signal and Image Processing for Remote Sensing, Edited by C. H. Chen, 2006.01.
26. R. Nishii and S. Eguchi, Image classification based on Markov random field models with Jeffreys divergence., Journal of Multivariate Analysis, 2006.01.
27. 川口修治, 山崎謙介, 西井龍映, ミクセルを考慮したマルコフ確率場に基づくリモートセンシング画像の教師なし土地被覆分類, 日本リモートセンシング学会誌, 2006.01.
28. R. Nishii and S. Kawaguchi, AdaBoost with different costs for misclassification and its applications to contextual image classification, SPIE 2006, 2006.01.
29. S. Tanaka and R. Nishii, Extended spatial logit models of deforestation due to population and relief energy in East Asia, SPIE 2006, 2006.01.
30. S. Kawaguchi, K. Yamazaki and R. Nishii, Contextual unsupervised classification of remotely sensed imagery with mixels, SPIE 2006, 2006.01.
31. S. Kawaguchi and R. Nishii, Hyperspectral image classification by AdaBoost with decision stumps based on composed feature variables, IEEE IGARSS 2006, 2006.01.
32. R. Nishii, Contextual image classification based on spatial boosting, IEEE IGARSS 2006, 2006.01.
33. R. Nishii and S. Eguchi, Supervised image classification by contextual AdaBoost based on posteriors in neighborhoods, IEEE Transactions on Geoscience and Remote Sensing, 43, 11, 2554-Vol. 43 (11), 2547-2554, 10.1109/TGRS.2005.848693, 2005.01.
34. R. Nishii and S. Eguchi, Spatio-temporal contextual image classification based on spatial AdaBoost, Proc. of IGARSS 2005, 175-I, 172-175, 2005.01.
35. R. Nishii and S. Eguchi, Robust supervised image classifiers by spatial AdaBoost based on robust loss functions, Proc. of SPIE 2005, 2005.01.
36. S. Tanaka and R. Nishii, Verification of deforestation in East Asia by spatial logit models due to population and relief energy, Proc. of SPIE 2005, 2005.01.
37. S. Tanaka and R. Nishii, Deforestation models due to population and relief energy in east Asia, Proc. of 8th China-Japan symposium on statistics, 2004.01.
38. T. Sakata, R. Nishii, T-S. Chin and R. Sawae, A new series of rotation invariant moments derived by Lie transformation group theory, Proc. of 9th International Workshop on Frontiers in Handwriting Recognition, 381-IWFHR-9, pp377--381., 10.1109/IWFHR.2004.5, 2004.01.
39. R. Nishii and S. Eguchi, Supervised image classification based on AdaBoost with contextual weak classifiers, Proc. of 2004 IEEE International Geoscience and Remote Sensing Symposium, 1470-II, pp1467 -- 1470, 2004.01.
40. R. Nishii, A Markov random field-based approach to decision level fusion for remote sensing image classification, IEEE Transactions on Geoscience and Remote Sensing, 2003.10.
41. 田中章司郎, 西井龍映, 人口増加に伴う森林減少の空間モデル, 応用統計学, 2003.01.
42. Y. Morisaki, R. Nishii, Contextual image fusion based on Markov random fields and its applications to geo-spatial image enhancement, Advances in Statistics, Combinatorics and Related Areas, Gulati et al. (Eds.), 2002.01.
43. 西井龍映, 統計手法によるリモートセンシング画像の判別分析, 応用統計学, 2002.01.
44. R. Nishii, S. Kusanobu and N. Nakaoka, Selection of variables and neighborhoods for spatial enhancement of thermal infrared images, Communication in Statistics -Theory and Methods, 1999.01.
45. R. Nishii and S. Tanaka, Accuracy and inaccuracy assessments in land-cover classification, IEEE Transactions on Geoscience and Remote Sensing, 1999.01.
46. 吉川 雅修, 高村 亘史, 藤村 貞夫, 西井 龍映, 宮本 泉, 田中 章司郎, パターン識別に対する斜交軸と変数選択手法の導入, 計測自動制御学会論文集, 1999.01.
47. R. Nishii, T. Yanagimoto and S. Kusanobu, The use of univariate Bayes regression models for spatial smoothing, Computational Statistics and Data Analysis, 1997.01.
48. S. Tanaka and R. Nishii, A model of deforestation by human population interactions, Environmental and Ecological Statistics, 1997.01.
49. R. Nishii, S. Kusanobu and S. Tanaka, Enhancement of low spatial resolution image based on high resolution bands, IEEE Transactions on Geoscience and Remote Sensing, 1996.01.
50. R. Nishii, Orthogonal functions of inverse Gaussian distributions, Lifetime Data: Models in Reliability and Survival Analysis, Jewell et al. (eds.), 1996.01.
51. R. Nishii, Convergence of the Gram-Charlier expansion of the signed log likelihood ratio, Communication in Statistics -Theory and Methods, 1994.01.
52. R. Nishii and T. Yanagimoto, Normal approximation to the distribution of the sample mean in the exponential family, Statistical Sciences and Data Analysis, K. Matusita et al. (Eds), 1993.01.
53. R. Nishii, Optimality of experimental designs, Discrete Mathematics, 1993.01.
54. R. Nishii, Saddlepoint approximation for the distribution function of the mean of random variables, Hiroshima Mathematical Journal, 1992.01.

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