Lee Suk Jin (32 results)

No Binding. Condition: Very Good. ORIGINAL 2005 Article, disbound from journal; no covers; in very good condition. Journal.

Hardcover. Condition: Good. unknown (not visible). Bilingual edition. The book is in good condition with no visible signs of wear or damage. The binding is intact, and there are no inscriptions or library markings. The cover and pages are clean, with illustrations remaining vibrant and clear. The dust jacket condition is unknown as it is not visible in the provided images. Kwak Chin-su, Kwon Mun-song, Kim Bong-chol, Kim Song-hoi, Kim Young-mi, Kim Woo-son, No Suk-ja (illustrator).…

Soft cover. Condition: Near Fine. Top of the first page has a small red heart stamp; otherwise volume is bright and fresh. 52 p., well illustrated. Oversize [br 32 ; drsr aro br 21].

Hardcover. Condition: Bon. Ancien livre de bibliothèque avec équipements. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Ammareal gives back up to 15% of this item's net price to charity organizations.…

24 cm. IX, 167 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Studies in Computational Intelligence. Volume 525. Sprache: Englisch.…

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New.

Condition: New. In English.

Condition: New. In English.

Condition: Used: Acceptable. Occasion - Etat Correct - Livre de bibliothèque, tampons présents - Le tigre aux sourcils blancs (2009) - Grand Format.

Condition: As New. Unread book in perfect condition.

Condition: New. pp. 176 Softcover reprint of the original 1st ed. 2014 edition NO-PA16APR2015-KAP.

Condition: New. pp. ix + 167.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. This book presents a customized prediction of respiratory motion with clustering from multiple patient interactions. The proposed method contributes to the improvement of patient treatments by considering breathing pattern for the accurate dose calculation in radiotherapy systems. Real-time tumor-tracking, where the prediction of irregularities becomes relevant, has yet to be clinically established. The statistical quantitative modeling for irregular breathing classification, in which commercial respiration traces are retrospectively categorized into several classes based on breathing pattern are discussed as well. The proposed statistical classification may provide clinical advantages to adjust the dose rate before and during the external beam radiotherapy for minimizing the safety margin.In the first chapter following the Introduction to this book, we review three prediction approaches of respiratory motion: model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the following chapter, we present a phantom study-prediction of human motion with distributed body sensors-using a Polhemus Liberty AC magnetic tracker. Next we describe respiratory motion estimation with hybrid implementation of extended Kalman filter. The given method assigns the recurrent neural network the role of the predictor and the extended Kalman filter the role of the corrector. After that, we present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction, we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. We have evaluated the new algorithm by comparing the prediction overshoot and thetracking estimation value. The experimental results of 448 patients' breathing patterns validated the proposed irregular breathing classifier in the last chapter.…

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. This book presents a customized prediction of respiratory motion with clustering from multiple patient interactions. The proposed method contributes to the improvement of patient treatments by considering breathing pattern for the accurate dose calculation in radiotherapy systems. Real-time tumor-tracking, where the prediction of irregularities becomes relevant, has yet to be clinically established. The statistical quantitative modeling for irregular breathing classification, in which commercial respiration traces are retrospectively categorized into several classes based on breathing pattern are discussed as well. The proposed statistical classification may provide clinical advantages to adjust the dose rate before and during the external beam radiotherapy for minimizing the safety margin.In the first chapter following the Introduction to this book, we review three prediction approaches of respiratory motion: model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the following chapter, we present a phantom study-prediction of human motion with distributed body sensors-using a Polhemus Liberty AC magnetic tracker. Next we describe respiratory motion estimation with hybrid implementation of extended Kalman filter. The given method assigns the recurrent neural network the role of the predictor and the extended Kalman filter the role of the corrector. After that, we present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction, we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. We have evaluated the new algorithm by comparing the prediction overshoot and thetracking estimation value. The experimental results of 448 patients' breathing patterns validated the proposed irregular breathing classifier in the last chapter.…

Hardcover. Condition: Brand New. 1st edition. 167 pages. 9.25x6.25x0.50 inches. In Stock.

Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This book describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. This book presents a customized prediction of respiratory motion with clustering from multiple patient interactions. The proposed method contributes to the improvement of patient treatments by considering breathing pattern for the accurate dose calculation in radiotherapy systems. Real-time tumor-tracking, where the prediction of irregularities becomes relevant, has yet to be clinically established. The statistical quantitative modeling for irregular breathing classification, in which commercial respiration traces are retrospectively categorized into several classes based on breathing pattern are discussed as well. The proposed statistical classification may provide clinical advantages to adjust the dose rate before and during the external beam radiotherapy for minimizing the safety margin.In the first chapter following the Introduction  to this book, we review three prediction approaches of respiratory motion: model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the following chapter, we present a phantom study¿prediction of human motion with distributed body sensors¿using a Polhemus Liberty AC magnetic tracker. Next we describe respiratory motion estimation with hybrid implementation of extended Kalman filter. The given method assigns the recurrent neural network the role of the predictor and the extended Kalman filter the role of the corrector. After that, we present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction, we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. We have evaluated the new algorithm by comparing the prediction overshoot and thetracking estimation value. The experimental results of 448 patients¿ breathing patterns validated the proposed irregular breathing classifier in the last chapter.…

Paperback. Condition: Brand New. reprint edition. 180 pages. 9.25x6.10x0.43 inches. In Stock.

Condition: new. Questo è un articolo print on demand.

Condition: new. Questo è un articolo print on demand.

PF. Condition: New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research in Prediction and Classification of Respiratory MotionIntroduction to recent algorithms describing respiratory motionWritten by experts in the fieldThis book describes recent radiotherapy technologies including to.…

Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research in Prediction and Classification of Respiratory MotionIntroduction to recent algorithms describing respiratory motionWritten by experts in the fieldThis book describes recent radiotherapy technologies including to.…

Condition: New. Print on Demand pp. 176.

Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems.This book presents a customized prediction of respiratory motion with clustering from multiple patient interactions. The proposed method contributes to the improvement of patient treatments by considering breathing pattern for the accurate dose calculation in radiotherapy systems. Real-time tumor-tracking, where the prediction of irregularities becomes relevant, has yet to be clinically established. The statistical quantitative modeling for irregular breathing classification, in which commercial respiration traces are retrospectively categorized into several classes based on breathing pattern are discussed as well. The proposed statistical classification may provide clinical advantages to adjust the dose rate before and during the external beam radiotherapy for minimizing the safety margin.In the first chapter following the Introduction to this book, we review three prediction approaches of respiratory motion: model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the following chapter, we present a phantom study¿prediction of human motion with distributed body sensors¿using a Polhemus Liberty AC magnetic tracker. Next we describe respiratory motion estimation with hybrid implementation of extended Kalman filter. The given method assigns the recurrent neural network the role of the predictor and the extended Kalman filter the role of the corrector. After that, we present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction, we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. We have evaluated the new algorithm by comparing the prediction overshoot and thetracking estimation value. The experimental results of 448 patients¿ breathing patterns validated the proposed irregular breathing classifier in the last chapter.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 180 pp. Englisch.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems.This book presents a customized prediction of respiratory motion with clustering from multiple patient interactions. The proposed method contributes to the improvement of patient treatments by considering breathing pattern for the accurate dose calculation in radiotherapy systems. Real-time tumor-tracking, where the prediction of irregularities becomes relevant, has yet to be clinically established. The statistical quantitative modeling for irregular breathing classification, in which commercial respiration traces are retrospectively categorized into several classes based on breathing pattern are discussed as well. The proposed statistical classification may provide clinical advantages to adjust the dose rate before and during the external beam radiotherapy for minimizing the safety margin.In the first chapter following the Introduction to this book, we review three prediction approaches of respiratory motion: model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the following chapter, we present a phantom study¿prediction of human motion with distributed body sensors¿using a Polhemus Liberty AC magnetic tracker. Next we describe respiratory motion estimation with hybrid implementation of extended Kalman filter. The given method assigns the recurrent neural network the role of the predictor and the extended Kalman filter the role of the corrector. After that, we present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction, we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. We have evaluated the new algorithm by comparing the prediction overshoot and thetracking estimation value. The experimental results of 448 patients¿ breathing patterns validated the proposed irregular breathing classifier in the last chapter.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 180 pp. Englisch.…

Condition: New. Print on Demand pp. ix + 167 67 Illus. (65 Col.).