Search results for: muon

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<meta name="description" content="Search results for: muon"> <meta name="keywords" content="muon"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" 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class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="muon"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: muon</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Development of Nondestructive Imaging Analysis Method Using Muonic X-Ray with a Double-Sided Silicon Strip Detector</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=I-Huan%20Chiu">I-Huan Chiu</a>, <a href="https://publications.waset.org/search?q=Kazuhiko%20Ninomiya"> Kazuhiko Ninomiya</a>, <a href="https://publications.waset.org/search?q=Shin%E2%80%99ichiro%20Takeda"> Shin’ichiro Takeda</a>, <a href="https://publications.waset.org/search?q=Meito%20Kajino"> Meito Kajino</a>, <a href="https://publications.waset.org/search?q=Miho%20Katsuragawa"> Miho Katsuragawa</a>, <a href="https://publications.waset.org/search?q=Shunsaku%20Nagasawa"> Shunsaku Nagasawa</a>, <a href="https://publications.waset.org/search?q=Atsushi%20Shinohara"> Atsushi Shinohara</a>, <a href="https://publications.waset.org/search?q=Tadayuki%20Takahashi"> Tadayuki Takahashi</a>, <a href="https://publications.waset.org/search?q=Ryota%20Tomaru"> Ryota Tomaru</a>, <a href="https://publications.waset.org/search?q=Shin%20Watanabe"> Shin Watanabe</a>, <a href="https://publications.waset.org/search?q=Goro%20Yabu"> Goro Yabu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In recent years, a nondestructive elemental analysis method based on muonic X-ray measurements has been developed and applied for various samples. Muonic X-rays are emitted after the formation of a muonic atom, which occurs when a negatively charged muon is captured in a muon atomic orbit around the nucleus. Because muonic X-rays have a higher energy than electronic X-rays due to the muon mass, they can be measured without being absorbed by a material. Thus, estimating the two-dimensional (2D) elemental distribution of a sample became possible using an X-ray imaging detector. In this work, we report a non-destructive imaging experiment using muonic X-rays at Japan Proton Accelerator Research Complex. The irradiated target consisted of a polypropylene material, and a double-sided silicon strip detector, which was developed as an imaging detector for astronomical obervation, was employed. A peak corresponding to muonic X-rays from the carbon atoms in the target was clearly observed in the energy spectrum at an energy of 14 keV, and 2D visualizations were successfully reconstructed to reveal the projection image from the target. This result demonstrates the potential of the nondestructive elemental imaging method that is based on muonic X-ray measurement. To obtain a higher position resolution for imaging a smaller target, a new detector system will be developed to improve the statistical analysis in further research.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=DSSD" title="DSSD">DSSD</a>, <a href="https://publications.waset.org/search?q=muon" title=" muon"> muon</a>, <a href="https://publications.waset.org/search?q=muonic%20X-ray" title=" muonic X-ray"> muonic X-ray</a>, <a href="https://publications.waset.org/search?q=imaging" title=" imaging"> imaging</a>, <a href="https://publications.waset.org/search?q=non-destructive%20analysis" title=" non-destructive analysis"> non-destructive analysis</a> </p> <a href="https://publications.waset.org/10012392/development-of-nondestructive-imaging-analysis-method-using-muonic-x-ray-with-a-double-sided-silicon-strip-detector" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012392/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012392/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012392/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012392/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012392/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012392/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012392/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012392/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012392/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012392/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012392.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1259</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Ensembling Classifiers – An Application toImage Data Classification from Cherenkov Telescope Experiment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Praveen%20Boinee">Praveen Boinee</a>, <a href="https://publications.waset.org/search?q=Alessandro%20De%20Angelis"> Alessandro De Angelis</a>, <a href="https://publications.waset.org/search?q=Gian%20Luca%20Foresti"> Gian Luca Foresti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques with classifiers such as random forests, neural networks and support vector machines. The data sets are from MAGIC, a Cherenkov telescope experiment. The task is to classify gamma signals from overwhelmingly hadron and muon signals representing a rare class classification problem. We compare the individual classifiers with their ensemble counterparts and discuss the results. WEKA a wonderful tool for machine learning has been used for making the experiments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Ensembles" title="Ensembles">Ensembles</a>, <a href="https://publications.waset.org/search?q=WEKA" title=" WEKA"> WEKA</a>, <a href="https://publications.waset.org/search?q=Neural%20networks%20%5BNN%5D" title=" Neural networks [NN]"> Neural networks [NN]</a>, <a href="https://publications.waset.org/search?q=SupportVector%20Machines%20%5BSVM%5D" title=" SupportVector Machines [SVM]"> SupportVector Machines [SVM]</a>, <a href="https://publications.waset.org/search?q=Random%20Forests%20%5BRF%5D." title=" Random Forests [RF]."> Random Forests [RF].</a> </p> <a href="https://publications.waset.org/8462/ensembling-classifiers-an-application-toimage-data-classification-from-cherenkov-telescope-experiment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8462/apa" target="_blank" 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Search results for: muon