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1. |
Record Nr. |
UNINA9910554491103321 |
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Autore |
Asadi Farzin |
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Titolo |
Power electronics circuit analysis with PSIM® / / Farzin Asadi, Kei Eguchi |
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Pubbl/distr/stampa |
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Berlin : , : Walter de Gruyter GmbH, , [2021] |
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©2021 |
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ISBN |
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Descrizione fisica |
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1 online resource (608 pages) |
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Collana |
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Disciplina |
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Soggetti |
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Power electronics |
Power electronics - Design and construction |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Frontmatter -- Preface -- Contents -- Chapter 1 An overview of PSIM® -- Chapter 2 Basics of PSIM -- Chapter 3 Simview™ -- Chapter 4 PSIM’s elements -- Chapter 5 Simulation of power electronic converters -- Chapter 6 Electrical machines -- Chapter 7 SimCoupler™ -- Chapter 8 SmartCtrl -- References for further study -- Index |
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Sommario/riassunto |
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Power electronics systems are nonlinear variable structure systems. They involve passive components such as resistors, capacitors, and inductors, semiconductor switches such as thyristors and MOSFETs, and circuits for control. The analysis and design of such systems presents significant challenges. Fortunately, increased availability of powerful computer and simulation programs makes the analysis/design process much easier. PSIM® is an electronic circuit simulation software package, designed specifically for use in power electronics and motor drive simulations but can be used to simulate any electronic circuit. With fast simulation speed and user friendly interface, PSIM provides a powerful simulation environment to meed the user simulation and development needs. This book shows how to simulate the power electronics circuits in PSIM environment. The prerequisite for this book is a first course on power electronics. This book is composed of eight chapters: Chapter 1 is an introduction to PSIM. Chapter 2 shows the fundamentals of circuit simulation with PSIM. Chapter 3 introduces the Simview™. Simview is PSIM’s waveform display and post-processing |
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program. Chapter 4 introduces the most commonly used components of PSIM. Chapter 5 shows how PSIM can be used for analysis of power electronics circuits. 45 examples are studied in this chapter. Chapter 6 shows how you can simulate motors and mechanical loads in PSIM. Chapter 7 introduces the SimCoupler™. Simcoupler fuses PSIM with Simulink® by providing an interface for co-simulation. Chapter 8 introduces the SmartCtrl®. SmartCtrl is a controller design software specifically geared towards power electronics applications. https://powersimtech.com/2021/10/01/book-release-power-electronics-circuit-analysis-with-psim/ |
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2. |
Record Nr. |
UNINA9910828345603321 |
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Autore |
Dubuisson Séverine |
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Titolo |
Tracking with particle filter for high-dimensional observation and state spaces / / Séverine Dubuisson |
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Pubbl/distr/stampa |
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London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2015 |
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©2015 |
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ISBN |
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1-119-05405-2 |
1-119-00486-1 |
1-119-05391-9 |
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Descrizione fisica |
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1 online resource (223 p.) |
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Collana |
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Digital Signal and Image Processing Series |
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Disciplina |
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Soggetti |
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Computer vision - Mathematical models |
Pattern recognition systems |
Particle methods (Numerical analysis) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Cover; Title Page; Copyright; Contents; Notations; Introduction; 1: Visual Tracking by Particle Filtering; 1.1. Introduction; 1.2. Theoretical models; 1.2.1. Recursive Bayesian filtering; 1.2.2. Sequential Monte-Carlo methods; 1.2.2.1. Importance sampling; 1.2.2.2. Particle filter; 1.2.3. Application to visual tracking; 1.2.3.1. State model; 1.2.3.2. Observation model; 1.2.3.3. Importance function; 1.2.3.4. Likelihood |
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function; 1.2.3.5. Resampling methods; 1.3. Limits and challenges; 1.4. Scientific position; 1.5. Managing large sizes in particle filtering; 1.6. Conclusion |
2: Data Representation Models2.1. Introduction; 2.2. Computation of the likelihood function; 2.2.1. Exploitation of the spatial redundancy; 2.2.1.1. Optimal order for histogram computation; 2.2.1.2. Optimization of the integral histogram; 2.2.2. Exploitation of the temporal redundancy; 2.2.2.1. Temporal histogram; 2.2.2.2. Incremental distance between histograms; 2.3. Representation of complex information; 2.3.1. Representation of observations for movement detection, appearances and disappearances; 2.3.2. Representation of deformations; 2.3.3. Multifeature representation |
2.3.3.1. Multimodal tracking2.3.3.2. Multifragment tracking; 2.3.3.3. Multiappearance tracking; 2.4. Conclusion; 3: Tracking Models That Focus on the State Space; 3.1. Introduction; 3.2. Data association methods for multi-object tracking; 3.2.1. Particle filter with adaptive classification; 3.2.2. Energetic filter for data association; 3.3. Introducing fuzzy information into the particle filter; 3.3.1. Fuzzy representation; 3.3.2. Fuzzy spatial relations; 3.3.3. Integration of fuzzy spatial relations into the particle filter; 3.3.3.1. Application to tracking an object with erratic movements |
3.3.3.2. Application to multi-object tracking3.3.3.3. Application to tracking shapes; 3.4. Conjoint estimation of dynamic and static parameters; 3.5. Conclusion; 4: Models of Tracking by Decomposition of the State Space; 4.1. Introduction; 4.2. Ranked partitioned sampling; 4.3. Weighted partitioning with permutation of sub-particles; 4.3.1. Permutation of sub-samples; 4.3.2. Decrease the number of resamplings; 4.3.3. General algorithm and results; 4.4. Combinatorial resampling; 4.5. Conclusion; 5: Research Perspectives in Tracking and Managing Large Spaces |
5.1. Tracking for behavioral analysis: toward finer tracking of the "future" and the "now"5.2. Tracking for event detection: toward a top-down model; 5.3. Tracking to measure social interactions; Bibliography; Index |
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Sommario/riassunto |
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This title concerns the use of a particle filter framework to track objects defined in high-dimensional state-spaces using high-dimensional observation spaces. Current tracking applications require us to consider complex models for objects (articulated objects, multiple objects, multiple fragments, etc.) as well as multiple kinds of information (multiple cameras, multiple modalities, etc.). This book presents some recent research that considers the main bottleneck of particle filtering frameworks (high dimensional state spaces) for tracking in such difficult conditions. |
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