{"id":15873,"date":"2026-07-08T23:26:34","date_gmt":"2026-07-08T14:26:34","guid":{"rendered":"https:\/\/web.tohoku.ac.jp\/yusa\/?p=15873"},"modified":"2026-07-09T17:14:08","modified_gmt":"2026-07-09T08:14:08","slug":"yusa-ned","status":"publish","type":"post","link":"https:\/\/web.tohoku.ac.jp\/yusa\/index.php\/2026\/yusa-ned\/","title":{"rendered":"\u8ad6\u6587\u63b2\u8f09\u6c7a\u5b9a\uff08Nuclear Engineering and Design\u8a8c\uff09!!"},"content":{"rendered":"\n<p>\u56fd\u5206\u753a\u306e\u67d0\u6240\u3067\u67d0\u5148\u751f\u3068\u98f2\u3093\u3067\u3044\u305f\u6642\u306b\u3001\u3046\u308c\u3057\u3044\u30e1\u30fc\u30eb\u304c\u53c2\u308a\u307e\u3057\u305f\u3002Enhancing \u00e2\u2011a probability-of-detection models for the analysis of ultrasonic non-destructive testing signals: The impact of sample size, flaw size distribution, and nonlinear regression\uff08\u8d85\u97f3\u6ce2\u63a2\u50b7\u4fe1\u53f7\u306e\u5206\u6790\u306e\u305f\u3081\u306e\u9ad8\u5ea6\u5316\u00e2\u2011a\u30e2\u30c7\u30eb\uff1a\u30b5\u30f3\u30d7\u30eb\u30b5\u30a4\u30ba\u3001\u304d\u305a\u5206\u5e03\u3001\u975e\u7dda\u5f62\u56de\u5e30\u306e\u5f71\u97ff\uff09\u3068\u306e\u30bf\u30a4\u30c8\u30eb\u3067Nuclear Engineering and Design\u8a8c<span id='easy-footnote-1-15873' class='easy-footnote-margin-adjust'><\/span><span class='easy-footnote'><a href='https:\/\/web.tohoku.ac.jp\/yusa\/index.php\/2026\/yusa-ned\/#easy-footnote-bottom-1-15873' title='\u4ee5\u524d\u306f\u7d50\u69cbNuclear Engineering and Design\u8a8c\u306b\u51fa\u3057\u3066\u3044\u305f\u306e\u3067\u3059\u304c\u3001\u898b\u8fd4\u3057\u305f\u3089\u6700\u5f8c\u306e\u8ad6\u6587\u306f2012\u5e74\u3067\u3057\u305f\u3002\u539f\u5b50\u529b\u304b\u3089\u9060\u3056\u304b\u3063\u3066\u3044\u305f\u3001\u308f\u3051\u3067\u306f\u306a\u3044\u3068\u601d\u3044\u305f\u3044\u3068\u3053\u308d\u3067\u3059\u3002'><sup>1<\/sup><\/a><\/span>\u306b\u6295\u7a3f\u3057\u3066\u3044\u305f\u8ad6\u6587\u306e\u53d7\u7406\u901a\u77e5\u3067\u3059\u3002<\/p>\n\n\n\n<p>\u3053\u306e\u8ad6\u6587\u3001\u30bf\u30a4\u30c8\u30eb\u306e\u901a\u308a\u975e\u7834\u58ca\u691c\u67fb\u4fe1\u53f7\u306e\u5206\u6790\u306b\u95a2\u3059\u308b\u3082\u306e\u306a\u306e\u3067\u3001\u7d20\u76f4\u306b\u8003\u3048\u308b\u3068\u975e\u7834\u58ca\u691c\u67fb\u306b\u95a2\u3059\u308b\u8ad6\u6587\u8a8c\u306b\u6295\u7a3f\u3059\u308b\u3001\u3079\u304d\u3082\u306e\u3067\u306f\u3042\u308a\u307e\u3059\u3002\u304c\u3001\u539f\u5b50\u529b\u306b\u304a\u3044\u3066\u3082\u4eca\u5f8c\u3053\u306e\u3088\u3046\u306a\u975e\u7834\u58ca\u691c\u67fb\u306b\u95a2\u3059\u308b\u78ba\u7387\u8ad6\u7684\u306a\u8a55\u4fa1\u30fb\u5206\u6790\u3092\u3001\u3068\u3044\u3046\u3053\u3068\u3067\u3001\u3042\u3048\u3066\u539f\u5b50\u529b\u5de5\u5b66\u306e\u8ad6\u6587\u8a8c\u306b\u51fa\u3057\u3066\u307f\u307e\u3057\u305f\u3002\u5b9f\u306f\u5f53\u521d\u5225\u306e\u539f\u5b50\u529b\u5de5\u5b66\u8ad6\u6587\u8a8c\u306b\u6295\u7a3f\u3057\u305f\u306e\u3067\u3059\u304c\u3001\u305d\u3061\u3089\u306fout of scope\u3068\u3044\u3046\u3053\u3068\u3067\u62d2\u7d76\u3055\u308c\u3066\u3057\u307e\u3044\u307e\u3057\u305f\u3002\u6c17\u3092\u53d6\u308a\u76f4\u3057\u3066\u6295\u7a3f\u3057\u305fNuclear Engineering and Design\u8a8c\u3067\u306f\u63b2\u8f09\u306b\u81f3\u308a\u307e\u3057\u305f\u304c\u3001\u3084\u306f\u308a\u539f\u5b50\u529b\u5206\u91ce\u3067\u306f\u306a\u304b\u306a\u304b\u3053\u3046\u3044\u3063\u305f\u8003\u3048\u65b9\u306f\u53d7\u3051\u5165\u308c\u3089\u308c\u3065\u3089\u3044\u3001\u3068\u3044\u3046\u3053\u3068\u306a\u306e\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>\u4f55\u306f\u3068\u3082\u3042\u308c\u8ad6\u6587\u306e\u6982\u8981\u306f\u4ee5\u4e0b\u3002<\/p>\n\n\n\n<p><span style=\"text-decoration: underline;\">Title:<\/span> Enhancing \u00e2\u2011a probability-of-detection models for the analysis of ultrasonic non-destructive testing signals: The impact of sample size, flaw size distribution, and nonlinear regression<\/p>\n\n\n\n<p><span style=\"text-decoration: underline;\">Abstract:<\/span> Quantitative assessment of non-destructive testing (NDT) capability often relies on probability of detection (POD) curves obtained by the <em>\u00e2<\/em>\u2011<em>a<\/em> method that is commonly used in the recent POD analyses. However, practical guidance regarding the required number of flaws and their optimal size distribution remains limited. This study addresses this issue by exploring the influence of sample size and flaw size distribution on <em>\u00e2<\/em>\u2011<em>a<\/em> POD estimation through Monte Carlo simulations. A known, nonlinear, saturating relationship between flaw size and signal amplitude obtained by numerical simulations of angle-beam ultrasonic inspection is adopted as a reference, and synthetic datasets are generated under various sampling conditions. The analysis shows that in the conventional <em>\u00e2<\/em>\u2011<em>a<\/em> method, which performs a linear regression after variable transformation, POD estimates can become strongly biased and highly scattered when sampled flaws are not concentrated near the true (and unknown) flaw size of interest. In contrast, regression models applied without variable transformation, particularly a sigmoid model designed to reflect the saturation behavior of NDT signals, produce more stable estimates of flaw sizes of interest. These include <em>a<\/em><sub>50<\/sub> and <em>a<\/em><sub>90<\/sub>, which are flaw sizes corresponding to 50% and 90% probabilities of detection, respectively. Furthermore, they exhibit substantially reduced dependence on the chosen flaw size range. These findings indicate that using regression functions consistent with the underlying signal physics can relax stringent requirements on flaw size distribution and enable reliable <em>\u00e2<\/em>\u2011<em>a<\/em> POD studies with a comparatively small number of flawed specimens. They also underscore the importance of physics\u2011based modeling and suggest that concepts developed in model\u2011assisted POD (MAPOD) can be effectively leveraged even when simulation data are not directly used to construct POD curves.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n","protected":false},"excerpt":{"rendered":"<p>\u56fd\u5206\u753a\u306e\u67d0\u6240\u3067\u67d0\u5148\u751f\u3068\u98f2\u3093\u3067\u3044\u305f\u6642\u306b\u3001\u3046\u308c\u3057\u3044\u30e1\u30fc\u30eb\u304c\u53c2\u308a\u307e\u3057\u305f\u3002Enhancing \u00e2\u2011a probability-of-detection models f &#8230; <\/p>\n","protected":false},"author":1,"featured_media":15876,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2,8],"tags":[284],"class_list":["post-15873","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-achievement","category-journal","tag-284"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/web.tohoku.ac.jp\/yusa\/wp-content\/uploads\/2026\/07\/journal_img.png?fit=200%2C200&ssl=1","_links":{"self":[{"href":"https:\/\/web.tohoku.ac.jp\/yusa\/index.php\/wp-json\/wp\/v2\/posts\/15873","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/web.tohoku.ac.jp\/yusa\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/web.tohoku.ac.jp\/yusa\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/web.tohoku.ac.jp\/yusa\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/web.tohoku.ac.jp\/yusa\/index.php\/wp-json\/wp\/v2\/comments?post=15873"}],"version-history":[{"count":9,"href":"https:\/\/web.tohoku.ac.jp\/yusa\/index.php\/wp-json\/wp\/v2\/posts\/15873\/revisions"}],"predecessor-version":[{"id":15908,"href":"https:\/\/web.tohoku.ac.jp\/yusa\/index.php\/wp-json\/wp\/v2\/posts\/15873\/revisions\/15908"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/web.tohoku.ac.jp\/yusa\/index.php\/wp-json\/wp\/v2\/media\/15876"}],"wp:attachment":[{"href":"https:\/\/web.tohoku.ac.jp\/yusa\/index.php\/wp-json\/wp\/v2\/media?parent=15873"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/web.tohoku.ac.jp\/yusa\/index.php\/wp-json\/wp\/v2\/categories?post=15873"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/web.tohoku.ac.jp\/yusa\/index.php\/wp-json\/wp\/v2\/tags?post=15873"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}