Jeonghoon (32 results)

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is an introduction to Markov chain modeling with applications to communication networks. It begins with a general introduction to performance modeling in Chapter 1 where we introduce different performance models. We then introduce basic ideas of Markov chain modeling: Markov property, discrete time Markov chain (DTMC) and continuous time Markov chain (CTMC). We also discuss how to find the steady state distributions from these Markov chains and how they can be used to compute the system performance metric. The solution methodologies include a balance equation technique, limiting probability technique, and the uniformization. We try to minimize the theoretical aspects of the Markov chain so that the book is easily accessible to readers without deep mathematical backgrounds. We then introduce how to develop a Markov chain model with simple applications: a forwarding system, a cellular system blocking, slotted ALOHA, Wi-Fi model, and multichannel based LAN model. The examples cover CTMC, DTMC, birth-death process and non birth-death process. We then introduce more difficult examples in Chapter 4, which are related to wireless LAN networks: the Bianchi model and Multi-Channel MAC model with fixed duration. These models are more advanced than those introduced in Chapter 3 because they require more advanced concepts such as renewal-reward theorem and the queueing network model. We introduce these concepts in the appendix as needed so that readers can follow them without difficulty. We hope that this textbook will be helpful to students, researchers, and network practitioners who want to understand and use mathematical modeling techniques.Table of Contents: Performance Modeling / Markov Chain Modeling / Developing Markov Chain Performance Models / Advanced Markov Chain Models.

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Taschenbuch. Condition: Neu. Performance Modeling of Communication Networks with Markov Chains | Jeonghoon Mo | Taschenbuch | Synthesis Lectures on Learning, Networks, and Algorithms | ix | Englisch | 2010 | Springer | EAN 9783031799884 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence.Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing How to use synthetic data to save labeling costs for training How do we increase robustness and decrease memory usage For inevitably poor conditions: How do we know that the network is uncertain about its decisions Can we understand a bit more about what actually happens inside neural networks This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

Taschenbuch. Condition: Neu. Deep Neural Networks and Data for Automated Driving | Robustness, Uncertainty Quantification, and Insights Towards Safety | Jeonghoon Mo | Taschenbuch | X | Englisch | 2022 | Springer Nature Switzerland | EAN 9783031012358 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the 13th Information Retrieval Societies Conference, AIRS 2017, held in Jeju, Korea, in November 2017. The 17 full papers presented were carefully reviewed and selected from numerous submissions. The final program of AIRS 2017 is divided in the following tracks: IR Infrastructure and Systems; IR Models and Theories; Personalization and Recommendation; Data Mining for IR; and IR Evaluation.

Taschenbuch. Condition: Neu. Information Retrieval Technology | 13th Asia Information Retrieval Societies Conference, AIRS 2017, Jeju Island, South Korea, November 22-24, 2017, Proceedings | Won-Kyung Sung (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xii | Englisch | 2017 | Springer | EAN 9783319701448 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Condition: Hervorragend. Zustand: Hervorragend | Seiten: 448 | Sprache: Englisch | Produktart: Bücher | This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence.Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety?This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is an introduction to Markov chain modeling with applications to communication networks. It begins with a general introduction to performance modeling in Chapter 1 where we introduce different performance models. We then introduce basic ideas of Markov chain modeling: Markov property, discrete time Markov chain (DTMC) and continuous time Markov chain (CTMC). We also discuss how to find the steady state distributions from these Markov chains and how they can be used to compute the system performance metric. The solution methodologies include a balance equation technique, limiting probability technique, and the uniformization. We try to minimize the theoretical aspects of the Markov chain so that the book is easily accessible to readers without deep mathematical backgrounds. We then introduce how to develop a Markov chain model with simple applications: a forwarding system, a cellular system blocking, slotted ALOHA, Wi-Fi model, and multichannel based LAN model. The examples cover CTMC, DTMC, birth-death process and non birth-death process. We then introduce more difficult examples in Chapter 4, which are related to wireless LAN networks: the Bianchi model and Multi-Channel MAC model with fixed duration. These models are more advanced than those introduced in Chapter 3 because they require more advanced concepts such as renewal-reward theorem and the queueing network model. We introduce these concepts in the appendix as needed so that readers can follow them without difficulty. We hope that this textbook will be helpful to students, researchers, and network practitioners who want to understand and use mathematical modeling techniques.Table of Contents: Performance Modeling / Markov Chain Modeling / Developing Markov Chain Performance Models / Advanced Markov Chain Models 92 pp. Englisch.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence.Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing How to use synthetic data to save labeling costs for training How do we increase robustness and decrease memory usage For inevitably poor conditions: How do we know that the network is uncertain about its decisions Can we understand a bit more about what actually happens inside neural networks This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above. 448 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book constitutes the refereed proceedings of the 13th Information Retrieval Societies Conference, AIRS 2017, held in Jeju, Korea, in November 2017. The 17 full papers presented were carefully reviewed and selected from numerous submissions. The final program of AIRS 2017 is divided in the following tracks: IR Infrastructure and Systems; IR Models and Theories; Personalization and Recommendation; Data Mining for IR; and IR Evaluation. 248 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book is an introduction to Markov chain modeling with applications to communication networks. It begins with a general introduction to performance modeling in Chapter 1 where we introduce different performance models. We then introduce basic ideas of M.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence.Environment perception for highly automated driving heavily employs deep neural networks, facing many challen.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is an introduction to Markov chain modeling with applications to communication networks. It begins with a general introduction to performance modeling in Chapter 1 where we introduce different performance models. We then introduce basic ideas of Markov chain modeling: Markov property, discrete time Markov chain (DTMC) and continuous time Markov chain (CTMC). We also discuss how to find the steady state distributions from these Markov chains and how they can be used to compute the system performance metric. The solution methodologies include a balance equation technique, limiting probability technique, and the uniformization. We try to minimize the theoretical aspects of the Markov chain so that the book is easily accessible to readers without deep mathematical backgrounds. We then introduce how to develop a Markov chain model with simple applications: a forwarding system, a cellular system blocking, slotted ALOHA, Wi-Fi model, and multichannel based LAN model. The examples cover CTMC, DTMC, birth-death process and non birth-death process. We then introduce more difficult examples in Chapter 4, which are related to wireless LAN networks: the Bianchi model and Multi-Channel MAC model with fixed duration. These models are more advanced than those introduced in Chapter 3 because they require more advanced concepts such as renewal-reward theorem and the queueing network model. We introduce these concepts in the appendix as needed so that readers can follow them without difficulty. We hope that this textbook will be helpful to students, researchers, and network practitioners who want to understand and use mathematical modeling techniques.Table of Contents: Performance Modeling / Markov Chain Modeling / Developing Markov Chain Performance Models / Advanced Markov Chain ModelsSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 92 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents the latest developments from industry and research on automated driving and artificial intelligenceProvides in introduction to current knowledge in neural networks and AIProvides a basis for future research and a guide for practiti.