(2019.6.2.撮影)

�蛹ｴ 宏和（やなぎはら ひろかず） 広島大学 大学院先進理工系科学研究科 数学プログラム 教授 情報科学部 担当

公表論文

	---査読付き論文---
[1]	Fujikoshi, Y., Ohmae, M. & Yanagihara, H. (1999).
	Asymptotic approximations of the null distribution of the one-way ANOVA test statistic under nonnormality.
	J. Japan Statist. Soc., Vol. 29 (2), 147-161.
	DOI:10.14490/jjss1995.29.147
[2]	Yanagihara, H. (2000).
	Asymptotic expansion of the null distribution of one-way ANOVA test statistic for heteroscedastic case
	under nonnormality.
	Comm. Statist. Theory Methods, Vol. 29 (2), 463-476.
	DOI:10.1080/03610920008832495
[3]	Yanagihara, H. (2001).
	Asymptotic expansions of the null distributions of three test statistics in a nonnormal GMANOVA model.
	Hiroshima Math. J., Vol. 31 (2), 213-262.
	DOI:10.32917/hmj/1151105700
[4]	Wakaki, H., Yanagihara, H. & Fujikoshi, Y. (2002).
	Asymptotic expansions of the null distributions of test statistics for multivariate linear hypothesis
	under nonnormality.
	Hiroshima Math. J., Vol. 32 (1), 17-50.
	DOI:10.32917/hmj/1151007641
[5]	Yanagihara, H. (2003).
	Asymptotic expansion of the null distribution of test statistic for linear hypothesis in nonnormal linear model.
	J. Multivariate Anal., Vol. 84 (2), 222-246.
	DOI:10.1016/S0047-259X(02)00049-0
[6]	Yanagihara, H. & Tonda, T. (2003).
	Adjustment on an asymptotic expansion of the distribution function with χ2-approximation.
	Hiroshima Math. J., Vol. 33 (1), 15-25.
	DOI:10.32917/hmj/1150997864
[7]	Satoh, K., Yanagihara, H. & Ohtaki, M. (2003).
	Bridging the gap between B-spline & polynomial regression model.
	Comm. Statist. Simulation Comput., Vol. 32 (1), 179-190.
	DOI:10.1081/SAC-120013120
[8]	Yanagihara, H., Sekiguchi, R. & Fujikoshi, Y. (2003).
	Bias correction of AIC in logistic regression models.
	J. Statist. Plann. Inference, Vol. 115 (2), 349-360.
	(This paper was in the top 5 of the most downloaded articles January - December 2003)
	(This paper was in the top 17 of the most downloaded articles January - August 2004)
	(This paper was in the top 1 of the most requested articles April 2002 - April 2004)
	DOI:10.1016/S0378-3758(02)00167-2
[9]	�蛹ｴ宏和・吉本 敦 (2003).
	一般化多変量分散分析モデルの林木直径成長分析への適用可能性.
	統計数理 (特集: 森林資源統計学), Vol. 51 (1), 19-35.
	DOI:10.4005/jjfs.87.504
[10]	Yanagihara, H. & Ohtaki, M. (2003).
	Knot-placement to avoid over fitting in B-spline scedastic smoothing.
	Comm. Statist. Simulation Comput., Vol. 32 (3), 771-785.
	DOI:10.1081/SAC-120017861
[11]	Fujikoshi, Y., Noguchi, T., Ohtaki, M. & Yanagihara, H. (2003).
	Corrected versions of cross-validation criteria for selecting multivariate regression and growth curve models.
	Ann. Inst. Statist. Math., Vol. 55 (3), 537-553.
	DOI:10.1007/BF02517806
[12]	西山 智・�蛹ｴ宏和・吉村 功 (2004).
	最大対比法を活用するためのSAS/IMLプログラム.
	計量生物学, Vol. 24 (2), 57-70.
	DOI:10.5691/jjb.24.57
[13]	Yanagihara, H., Matsumoto, C. & Tonda, T. (2004).
	Asymptotic expansion of the null distribution of the modified normal likelihood ratio criterion for testing
	Σ=Σ0 under nonnormality.
	Hiroshima Math. J., Vol. 34 (1), 81-100.
	DOI:10.32917/hmj/1150998072
[14]	�蛹ｴ宏和・大瀧 慈 (2004).
	B-スプラインノンパラメトリック回帰モデルにおける過剰適合の回避について.
	応用統計学 (特集号: ノンパラメトリック統計モデルとその応用), Vol. 33 (1), 51-69.
	DOI:10.5023/jappstat.33.51
[15]	Satoh, K., Yanagihara, H. & Ohtaki, M. (2004).
	Clustering method by connected neighborhoods & its application.
	Adv. Appl. Stat., Vol. 4 (2), 223-231.
[16]	�蛹ｴ宏和 (2005).
	非正規データにおける情報量規準を用いた共分散構造モデルの選択問題
	—「日本人の国民性調査」データへの適用 —.
	統計数理 (特集号: 日本人の国民性調査 50 年), Vol. 53 (1), 133-157.
[17]	Yanagihara, H. & Ohmoto, C. (2005).
	On distribution of AIC in linear regression models.
	J. Statist. Plann. Inference, Vol 133 (2), 417-433.
	(This paper was in the top 23 of the most downloaded articles April - June 2005)
	DOI:10.1016/j.jspi.2004.03.016
[18]	能本美穂・吉本 敦・�蛹ｴ宏和 (2005).
	木材生産を通した炭素の収支分析 — 福岡県八女地域を事例として —.
	日本森林学会誌, Vol. 87 (4), 313-322.
	DOI:10.4005/jjfs.87.313
[19]	Yanagihara, H., Tonda, T. & Matsumoto, C. (2005).
	The effects of nonnormality on asymptotic distributions of some likelihood ratio criteria for testing covariance
	structures under normal assumption.
	J. Multivariate Anal., Vol. 96 (2), 237-264.
	Erratum, (2008), Vol. 99 (2), 309-310.
	(This paper was in the top 20 of the most downloaded articles October - December 2005)
	DOI:10.1016/j.jmva.2004.10.014 (Original) DOI:10.1016/j.jmva.2005.10.008 (Erratum)
[20]	Yanagihara, H. & Yuan, K.-H. (2005).
	Four improved statistics for contrasting means by correcting skewness and kurtosis.
	British J. Math. Statist. Psych., Vol. 58 (2), 209-237.
	DOI:10.1348/000711005X64060
[21]	Yanagihara, H. & Yuan, K.-H. (2005).
	Three approximate solutions to the multivariate Behrens-Fisher problem.
	Comm. Statist. Simulation Comput., Vol. 34 (4), 975-988.
	DOI:10.1080/03610910500308396
[22]	Yanagihara, H. & Ohtaki, M. (2005).
	A family of regression models having partially additive and multiplicative covariate structure.
	Bull. Inform. Cybernet. “Special Issue in Honor of Professor Takashi Yanagawa Part II”, Vol. 37, 49-64.
	DOI:10.5109/12591
[23]	吉本 敦・�蛹ｴ宏和・二宮嘉行 (2005).
	多変量線形モデルによる林分成長要因探索のための変数選択.
	日本森林学会誌 (特集号: 森林計画最前線 — 緑豊かな森林の育成を目指した最新研究 —), Vol. 87 (6), 504-512.
	DOI:10.4005/jjfs.87.504
[24]	Fujikoshi, Y., Yanagihara, H., & Wakaki, H. (2005).
	Bias corrections of some criteria for selecting multivariate linear models in a general nonnormal case.
	Amer. J. Math. Management Sci. “25th Anniversary of the Thomas L. Saaty and Jacob Wolfowitz Prizes:
	New Advances and Applications by Prize Winners II”, Vol. 25 (3-4), 221-258.
	(This paper was selected as 2006 Jacob Wolfowitz Prize)
	DOI:10.1080/01966324.2005.10737651
[25]	Yanagihara, H. (2006).
	Corrected version of AIC for selecting multivariate normal linear regression models in a general nonnormal case.
	J. Multivariate Anal., Vol. 97 (5), 1070-1089.
	(This paper was in the top 3 of the most downloaded articles April - June 2006)
	DOI:10.1016/j.jmva.2005.06.005
[27]	Yanagihara, H., Tonda, T. & Matsumoto, C. (2006).
	Bias correction of cross-validation criterion based on Kullback-Leibler information under a general condition.
	J. Multivariate Anal. “Special issue dedicated to Professor Yasunori Fujikoshi”, Vol. 97 (9), 1965-1975.
	(This paper was in the top 15 of the most downloaded articles July - September 2006)
	DOI:10.1016/j.jmva.2005.10.009
[26]	Yanagihara, H. (2007).
	A family of estimators for multivariate kurtosis in a nonnormal linear regression model.
	J. Multivariate Anal. , Vol. 98 (1), 1-29.
	(This paper was in the top 8 of the most downloaded articles October - December 2006)
	DOI:10.1016/j.jmva.2005.05.015
[28]	Kamo, K., Kaneko, S., Satoh, K., Yanagihara, H., Mizuno, S. & Sobue, T. (2007).
	A mathematical estimation of true cancer incidence using data from population-based cancer registries.
	Jpn. J. Clin. Oncol., Vol. 37 (2), 150-155.
	DOI:10.1093/jjco/hyl143
[29]	Yanagihara, H. (2007).
	Conditions for robustness to nonnormality of test statistics in a GMANOVA model.
	J. Japan Statist. Soc., Vol. 37 (1), 135-155.
	DOI:10.14490/jjss.37.135
[30]	Yuan, K.-H., Hayashi, K. & Yanagihara, H. (2007).
	A class of population covariance matrices in the bootstrap approach to covariance structure analysis.
	Multivariate Behav. Res., Vol. 42 (2), 261-281.
	DOI:10.1080/00273170701360662
[31]	山村麻理子・�蛹ｴ宏和 (2007).
	「国民生活基礎調査」データにもとづく居宅介護サービス利用に関する多変量プロビット分析.
	統計数理, Vol. 55 (1), 125-142.
[32]	Kamo, K., Yanagihara, H., Kato, A. & Yoshimoto, A. (2008).
	Probability estimation of snow damage on sugi (Cryptomeria japonica) forest stands by logistic regression
	model in Toyama prefecture, Japan.
	J. Forest Sci., Vol. 24 (3), 137-142.
[33]	Yoshimoto, A., Kato, A. & Yanagihara, H. (2008).
	Economic analysis of snow damage on sugi (Cryptomeria japonica) forest stands in Japan within the forest
	stand optimization framework.
	J. Forest Sci., Vol. 24 (3), 143-149.
[34]	佐藤健一・�蛹ｴ宏和・加茂憲一 (2009).
	離散分布の経時測定データにおける線形な変化係数の推測について.
	応用統計学, Vol. 38 (1), 19-28.
	(本論文により2010年度応用統計学会優秀論文章を受章)
	DOI:10.5023/jappstat.38.19
[35]	�蛹ｴ宏和・永井 勇・佐藤健一 (2009).
	多変量一般化リッジ回帰におけるリッジパラメータ最適化のためのバイアス補正 Cp 規準.
	応用統計学, Vol. 38 (3), 151-172.
	DOI:10.5023/jappstat.38.151
[36]	Yamamura, M., & Yanagihara, H. (2009).
	Ordering municipalities by medical cost efficiency under the Japanese national health insurance system
	using the stochastic cost frontier model.
	Amer. J. Math. Management Sci., Vol. 29 (3-4), 371-392.
	DOI:10.1080/01966324.2009.10737764
[37]	Yanagihara, H. & Satoh, K. (2010).
	An unbiased Cp criterion for multivariate ridge regression.
	J. Multivariate Anal., Vol. 101 (5), 1226-1238.
	DOI:10.1016/j.jmva.2009.09.017
[38]	Srivastava, M. S. & Yanagihara, H. (2010).
	Testing the equality of several covariance matrices with fewer observations than the dimension.
	J. Multivariate Anal., Vol. 101 (6), 1319-1219.
	DOI:10.1016/j.jmva.2009.12.010
[39]	Yamamura, M., Yanagihara, H. & Srivastava, M. S. (2010).
	Variable selection in multivariate linear regression models with fewer observations than the dimension.
	Japanese J. Appl. Statist., Vol. 39 (1), 1-19.
	DOI:10.5023/jappstat.39.1
[40]	Satoh, K., Yanagihara, H. & Kamo, K. (2010).
	A robust estimation method for a growth curve model with balanced design.
	J. Stat., Adv. Theory Appl., Vol. 3 (2), 113-124.
[41]	吉本 敦・木島真志・�蛹ｴ宏和 (2010).
	隣接空間制約による最適林分団地化パターンの探求.
	統計数理, Vol. 58 (1), 113-126.
[42]	Yanagihara, H., Himeno, T. & Yuan, K.-H. (2010).
	GLS discrepancy based information criteria for selecting covariance structure models.
	Behaviormetrika, Vol. 37 (2), 71-86.
	DOI:10.2333/bhmk.37.71
[43]	冨田哲治・佐藤健一・�蛹ｴ宏和 (2010).
	空間データに対する交互作用モデルを用いた変化係数曲面の推測について.
	応用統計学, Vol. 39 (2-3), 59-70.
	DOI:10.5023/jappstat.39.59
[44]	Satoh, K. & Yanagihara, H. (2010).
	Estimation of varying coefficients for a growth curve model.
	Amer. J. Math. Management Sci., Vol. 30 (3-4), 243-256.
	DOI:10.1080/01966324.2010.10737787
[45]	Matsumoto, C., Yanagihara, H. & Wakaki, H. (2011).
	Improvement of the quality of the chi-square approximation for the ADF test on a covariance matrix
	with a linear structure.
	J. Statist. Plann. Inference, Vol. 141 (4), 1535-1542 .
	DOI:10.1016/j.jspi.2010.11.012
[46]	Yanagihara, H., Kamo, K. & Tonda, T. (2011).
	Second-order bias-corrected AIC in multivariate normal linear models under nonnormality.
	Canad. J. Statist., Vol. 39 (1), 126-146.
	DOI:10.1002/cjs.10090
[47]	Yanagihara, H. (2012).
	A non-iterative optimization method for smoothness in penalized spline regression.
	Stat. Comput., Vol. 22 (2), 527-544.
	DOI:10.1007/s11222-011-9245-0
[48]	Yanagihara, H. & Fujisawa, H. (2012).
	Iterative bias correction of the cross-validation criterion.
	Scand. J. Stat., Vol. 39 (1), 116-130.
	DOI:10.1111/j.1467-9469.2011.00754.x
[49]	Yanagihara, H., Kamo, K., Imori, S. & Satoh, K. (2012).
	Bias-corrected AIC for selecting variables in multinomial logistic regression models.
	Linear Algebra Appl., Vol. 436 (11), 4329-4341.
	DOI:10.1016/j.laa.2012.01.018
[50]	Nagai, I., Yanagihara, H. & Satoh, K. (2012).
	Optimization of ridge parameters in multivariate generalized ridge regression by plug-in methods.
	Hiroshima Math. J., Vol. 42 (3), 301-324.
	DOI:10.32917/hmj/1355238371
[51]	Kamo, K., Yanagihara, H. & Satoh, K. (2013).
	Bias-corrected AIC for selecting variables in Poisson regression models.
	Comm. Statist. Theory Methods, Vol. 42 (11), 1911-1921.
	DOI:10.1080/03610926.2011.600504
[52]	Yanagihara, H., Yuan, K.-H., Fujisawa, H. & Hayashi, K. (2013).
	A class of model selection criteria based on cross-validation method.
	Hiroshima Math. J., Vol. 43 (2), 149-177.
	DOI:10.32917/hmj/1372180510
[53]	Nagai, I., Fukui, K. & Yanagihara, H. (2013).
	Choosing the number of repetitions in the multiple plug-in optimization method for the ridge parameters
	in multivariate generalized ridge regression.
	Bull. Inform. Cybernet., Vol. 45, 25-35.
	DOI:10.5109/1563529
[54]	Fujikoshi, Y., Sakurai, T. & Yanagihara, H. (2014).
	Consistency of high-dimensional AIC-type and Cp-type criteria in multivariate linear regression.
	J. Multivariate Anal., Vol. 123, 184-200.
	DOI:10.1016/j.jmva.2013.09.006
[55]	Imori, S., Yanagihara, H. & Wakaki, H. (2014).
	Simple formula for calculating bias-corrected AIC in generalized linear models.
	Scand. J. Stat., Vol. 41 (2), 535-555.
	DOI:10.1111/sjos.12049
[56]	Hashiyama, Y., Yanagihara, H. & Fujikoshi, Y. (2014).
	Jackknife bias correction of the AIC for selecting variables in canonical correlation analysis under
	model misspecification.
	Linear Algebra Appl., Vol. 455, 82-106.
	DOI:10.1016/j.laa.2014.04.028
[57]	Srivastava, M. S., Yanagihara, H. & Kubokawa, T. (2014).
	Tests for covariance matrices in high dimension with less sample size.
	J. Multivariate Anal., Vol. 130, 289-309.
	DOI:10.1016/j.jmva.2014.06.003
[58]	Kamada, A., Yanagihara, H., Wakaki, H. & Fukui, K. (2014).
	Selecting a shrinkage parameter in structural equation modeling with a near singular covariance matrix
	by the GIC minimization method.
	Hiroshima Math. J., Vol. 44 (3), 315-326.
	DOI:10.32917/hmj/1419619749
[59]	Fukui, K., Yamamura, M. & Yanagihara, H. (2015).
	Comparison with RSS-based model selection criteria for selecting growth functions.
	FORMATH , Vol. 14, 27-39.
	DOI:10.15684/formath.14.004
[60]	Yanagihara, H., Wakaki, H. & Fujikoshi, Y. (2015).
	A consistency property of the AIC for multivariate linear models when the dimension and the sample
	size are large.
	Electron. J. Stat., Vol. 9, 869-897.
	DOI:10.1214/15-EJS1022
[61]	Yuan, K.-H., Tian, Y. & Yanagihara, H. (2015).
	Empirical correction to the likelihood ratio statistic for structural equation modeling with many variables.
	Psychometrika, Vol. 80 (2), 379-405..
	DOI:10.1007/s11336-013-9386-5
[62]	Yanagihara, H. (2015).
	Conditions for consistency of a log-likelihood-based information criterion in normal multivariate linear
	regression models under the violation of normality assumption.
	J. Japan Statist. Soc., Vol. 45 (1), 21-56.
	DOI:10.14490/jjss.45.21
[63]	Yamamura, M., Fukui, K. & Yanagihara, H. (2016).
	Illustration of the varying coefficient model for a tree growth analysis from the age and space
	perspectives.
	FORMATH , Vol. 15, 1-9.
	DOI:10.15684/formath.15.001
[64]	Yanagihara, H. (2016).
	A high-dimensionality-adjusted consistent Cp-type statistic for selecting variables in a normality-assumed
	linear regression with multiple responses.
	Procedia Comput. Sci., Vol. 96, Knowledge-Based and Intelligent Information & Engineering Systems:
	Proceedings of the 20th International Conference KES-2016 (Eds. R. J. Howlett, L. C. Jain, B. Gabrys,
	C. Toro & C. P. Lim), 1096-1105.
	DOI:10.1016/j.procs.2016.08.151
[65]	Yamamura, M., Yanagihara, H., Solvang, H. K., Øien, N., & Haug, T. (2016).
	Canonical correlation analysis for geographical and chronological responses.
	Procedia Comput. Sci., Vol. 96, Knowledge-Based and Intelligent Information & Engineering Systems:
	Proceedings of the 20th International Conference KES-2016 (Eds. R. J. Howlett, L. C. Jain, B. Gabrys,
	C. Toro & C. P. Lim), 1096-1105.
	DOI:10.1016/j.procs.2016.08.180
[66]	Solvang, H. K., Yanagihara, H., Øien, N. & Haug, T. (2017)
	Temporal and geographical variation in body condition of common minke whales (Balaenoptera
	acutorostrata acutorostrata) in the Northeast Atlantic.
	Polar Biol., Vol. 40 (3), 667-683.
	DOI:10.1007/s00300-016-1992-0
[67]	Yanagihara, H., Oda, R., Hashiyama, Y. & Fujikoshi, Y. (2017).
	High-dimensional asymptotic behaviors of differences between the log-determinants of two Wishart
	matrices.
	J. Multivariate Anal., Vol. 157, 70-86.
	DOI:10.1016/j.jmva.2017.03.002
[68]	Yanagihara, H., Kamo, K., Imori, S. & Yamamura, M. (2017).
	A study on the bias-correction effect of the AIC for selecting variables in normal multivariate linear
	regression models under model misspecification.
	REVSTAT-Stat. J., Vol. 15 (3), 299-332.
[69]	Nagai, I., Takahashi, K. & Yanagihara, H. (2017).
	Information criterion-based nonhierarchical clustering.
	Int. J. Knowl. Eng. Soft Data Paradig., Vol. 6 (1), 1-43.
	DOI:10.1504/IJKESDP.2017.089504
[70]	Yanagihara, H. (2018).
	Explicit solution to the minimization problem of generalized cross-validation criterion for selecting ridge
	parameters in generalized ridge regression.
	Hiroshima Math. J., Vol. 48 (2), 203-222.
	DOI:10.32917/hmj/1533088835
[71]	Oda, R., Yanagihara, H. & Fujikoshi, Y. (2019).
	Asymptotic null and non-null distributions of test statistics for redundancy in high-dimensional canonical
	correlation analysis.
	Random Matrices-Theo., 1950001-1-26.
	DOI:10.1142/S2010326319500011
[72]	�蛹ｴ宏和 (2019).
	非正規性の下での多変量線形回帰モデルにおけるモデル選択規準の大標本・高次元漸近理論による一致性の評価.
	日本統計学会誌, Vol. 49 (1), 133-159 (日本統計学会研究業績賞受賞者特別寄稿論文).
	DOI:10.11329/jjssj.49.133
[73]	Oda, R., Suzuki,Y., Yanagihara, H. & Fujikoshi, Y. (2020).
	A consistent variable selection method in high-dimensional canonical discriminant analysis.
	J. Multivariate Anal., Vol. 175, 104561-1-13.
	DOI:10.1016/j.jmva.2019.104561
[74]	Ohishi, M., Yanagihara, H. & Fujikoshi, Y. (2020).
	A fast algorithm for optimizing ridge parameters in a generalized ridge regression by minimizing a model
	selection criterion.
	J. Statist. Plann. Inference, Vol. 204, 187-205.
	DOI:10.1016/j.jspi.2019.04.010
[75]	Oda, R. & Yanagihara, H. (2020).
	A fast and consistent variable selection method for high-dimensional multivariate linear regression with a large
	number of explanatory variables.
	Electron. J. Stat., Vol. 14, 1386-1412.
	DOI:10.1214/20-EJS1701
[76]	Ohishi, M., Yanagihara, H. & Wakaki, H. (2020).
	Optimization of generalized Cp criterion for selecting ridge parameters in generalized ridge regression.
	Smart Innov. Syst. Tec. Vol. 193, Intelligent Decision Technologies 2020: Proceedings of the 12th KES International
	Conference on Intelligent Decision Technologies (KES-IDT 2020) (eds. I. Czarnowski, R. J. Howlett & L. C. Jain),
	267-278.
	DOI:10.1007/978-981-15-5925-9_23
[77]	Fukui, K., Ohishi, M., Yamamura, M. & Yanagihara, H. (2020).
	A fast optimization method for additive model via partial generalized ridge regression.
	Smart Innov. Syst. Tec. Vol. 193, Intelligent Decision Technologies 2020: Proceedings of the 12th KES International
	Conference on Intelligent Decision Technologies (KES-IDT 2020) (eds. I. Czarnowski, R. J. Howlett & L. C. Jain),
	279-290.
	DOI:10.1007/978-981-15-5925-9_24
[78]	Ohishi, M., Yanagihara, H. & Kawano, S. (2020).
	Equivalence between adaptive-Lasso and generalized ridge estimators in linear regression with orthogonal explanatory
	variables after optimizing regularization parameters.
	Ann. Inst. Statist. Math., Vol. 72 (6), 1501-1516.
	DOI:10.1007/s10463-019-00734-2
[79]	Oda, R., Yanagihara, H. & Fujikoshi, Y. (2021).
	Strong consistency of log-likelihood-based information criterion in high-dimensional canonical correlation analysis.
	Sankhya A, Vol. 83 (1), 109-127.
	DOI:10.1007/s13171-019-00174-3
[80]	Oda, R., Mima,Y., Yanagihara, H. & Fujikoshi, Y. (2021).
	A high-dimensional bias-corrected AIC for selecting response variables in multivariate calibration.
	Comm. Statist. Theory Methods, Vol. 50 (14), 3453-3476.
	DOI:10.1080/03610926.2019.1705978
[81]	Oda, R. & Yanagihara, H. (2021).
	A consistent likelihood-based variable selection method in normal multivariate linear regression.
	Smart Innov. Syst. Tec., Vol. 238, Intelligent Decision Technologies: Proceedings of the 13th KES-IDT 2021 Conference
	(eds. I. Czarnowski, R. J. Howlett & L. C. Jain), 391-401.
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[91]	Horikawa, K., Nagai, I., Monden, R. & Yanagihara, H. (2023).
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