Es wurden 41 Produkte zu dem Suchbegriff conversion rate optimization cro in 6 Shops gefunden:

RME DMC 842 M, 8-kanaliges AES42-Interface mit Controller für digitale Mikrofone und integrierter IE64 MADI Karte. 24bit/192kHz. Vollständig AES42 kompatibel, 8 XLR AES42 Eingänge (AES3 kompatibel), 8 symmetrische XLR Line Ausgänge, 24 Bit Sample Rate Conversion pro Eingang zuschaltbar, Digital Phantom Power schaltbar pro Eingang. Unterstützt Mode 1 und Mode 2, 63 dB Gain Range (pro Kanal einstellbar, selbst im asynchronen Mode 1), LED Pegelanzeige mit 13 LEDs pro Kanal, M/S De-/Encoding, 4 x AES/EBU Out per D-Sub, 8 Kanäle @ 192 kHz, 2 x ADAT Out (8 Kanäle, 96 kHz), Optionaler MADI I/O (I64 MADI Card) mit serieller Kaskadierung und digitalem/analogem Rückweg, MIDI I/O, Komplett fernbedienbar. Zukunftssicher Update per Flash möglich. 19", 2HE

Optimierungs-Kit für Rack-Luftstrom, für HP 642 1200mm, 647 1075mm, 842 1075mm, 842 1200mm, ProLiant DL360p Gen8

Dieses Buch ist interessant und steckt voller neuer Ideen. Es weckt die Neugier des Lesers. Das Buch richtet sich sowohl an Forscher als auch an Praktiker. Studenten und Forscher werden Kenntnisse über die Ameisenkolonie-Optimierung und ihre möglichen Anwendungen erwerben, und Praktiker werden neue Ideen und Lösungen für ihre kombinatorischen Optimierungs- und Entscheidungsprobleme finden. Die Ameisenkolonie-Optimierung gehört zu den besten Methoden zur Lösung schwieriger Optimierungsprobleme, die im realen Leben und in der Industrie auftreten. Sie hat bei einigen Anwendungen mit sehr restriktiven Randbedingungen hervorragende Ergebnisse erzielt.Der Leser wird sowohl theoretische Aspekte der Ameisenmethode als auch Anwendungen auf eine Vielzahl von Problemen finden. Folgende Anwendungen sind zu nennen: das Multiple-Knapsack-Problem, ein wichtiges wirtschaftliches Problem, das Problem der Netzplanung, das Problem der GPS-Vermessung, das Problem der E. coli-Kultivierung, das Modellieren der Positionierung drahtloser Sensornetzwerke, die Erkennung von Bildkanten, die Personalplanung. Bucheinband-Typ:HardcoverUnterstützte Sprachen:EnglischGeschrieben von:Stefka FidanovaTyp:PapierbuchAnzahl der Seiten:125 SeitenIllustrator:19 b/w illustrations, 9 illustrations in colourVeröffentlicher:Springer ChamFreigabedatum (TT/MM/JJ):28/02/2021Editionstyp:Erste AusgabeInternational Standard Book Number (ISBN):9783030673796

Der einzige Optimierer mit selektivem Einsatz Umfasst die Vorteile von Safety & Monitoring Schatten- und Alterstoleranz zur Maximierung des Lebenszeitertrags 25 Jahre Garantie

Dieses Buch ist interessant und steckt voller neuer Ideen. Es weckt die Neugier des Lesers. Das Buch richtet sich sowohl an Forscher als auch an Praktiker. Studenten und Forscher werden Kenntnisse über die Ameisenkolonie-Optimierung und ihre möglichen Anwendungen erwerben, und Praktiker werden neue Ideen und Lösungen für ihre kombinatorischen Optimierungs- und Entscheidungsprobleme finden. Die Ameisenkolonie-Optimierung gehört zu den besten Methoden zur Lösung schwieriger Optimierungsprobleme, die im realen Leben und in der Industrie auftreten. Sie hat bei einigen Anwendungen mit sehr restriktiven Randbedingungen hervorragende Ergebnisse erzielt.Der Leser wird sowohl theoretische Aspekte der Ameisenmethode als auch Anwendungen auf eine Vielzahl von Problemen finden. Folgende Anwendungen sind zu nennen: das Multiple-Knapsack-Problem, ein wichtiges wirtschaftliches Problem, das Problem der Netzplanung, das Problem der GPS-Vermessung, das Problem der E. coli-Kultivierung, das Modellieren der Positionierung drahtloser Sensornetzwerke, die Erkennung von Bildkanten, die Personalplanung. Bucheinband-Typ:HardcoverUnterstützte Sprachen:EnglischGeschrieben von:Stefka FidanovaTyp:PapierbuchAnzahl der Seiten:125 SeitenIllustrator:19 b/w illustrations, 9 illustrations in colourVeröffentlicher:Springer ChamFreigabedatum (TT/MM/JJ):28/02/2021Editionstyp:Erste AusgabeInternational Standard Book Number (ISBN):9783030673796

Optimierungs-Kit für Rack-Luftstrom, für HP 642 1200mm, 647 1075mm, 842 1075mm, 842 1200mm, ProLiant DL360p Gen8

Dieses Buch stellt die jüngsten Fortschritte in der rechnergestützten Optimierung vor. Optimierung ist aus unserem Alltag nicht mehr wegzudenken. Wir versuchen, unseren Aufwand zu minimieren und den erzielten Gewinn zu maximieren. Viele reale und industrielle Probleme aus Technik, Wirtschaft, Medizin und anderen Bereichen lassen sich als Optimierungsaufgaben formulieren.Das Buch ist eine umfassende Sammlung von erweiterten Beiträgen aus den Workshops on Computational Optimization 2020. Das Buch enthält wichtige reale Probleme wie die Modellierung physikalischer Prozesse, Personaleinsatzplanung, Parametereinstellungen für die Steuerung verschiedener Prozesse, Transportprobleme, drahtlose Sensornetzwerke, Maschinenplanung, Modellierung der Luftverschmutzung, Lösung multipler Integrale und Systeme von Differentialgleichungen, die reale Prozesse beschreiben, Lösung technischer Probleme. Es wird gezeigt, wie Algorithmen für diese Probleme entwickelt werden können, die auf neuen intelligenten Methoden wie evolutionären Berechnungen, Ameisenkolonie-Optimierung, Constrain Programming und anderen beruhen. Diese Forschung zeigt, wie einige reale Probleme, die in der Technik, Wirtschaft und anderen Bereichen auftreten, als Optimierungsprobleme formuliert werden können. Bucheinband-Typ:HardcoverUnterstützte Sprachen:EnglischGeschrieben von:Stefka FidanovaTyp:PapierbuchAnzahl der Seiten:490 SeitenIllustrator:44 b/w illustrations, 51 illustrations in colourVeröffentlicher:Springer ChamFreigabedatum (TT/MM/JJ):15/12/2021Editionstyp:Erste AusgabeInternational Standard Book Number (ISBN):9783030823962

Einführungskurse in die kombinatorische Optimierung sind in den höheren Semestern der Informatik, des Wirtschaftsingenieurwesens und der Betriebswirtschaftslehre beliebt, da sie in diesen Bereichen breite Anwendung finden. Es gibt mehrere veröffentlichte Lehrbücher, die diesen Kurs behandeln, und die Autoren haben viele von ihnen in ihrer eigenen Lehrtätigkeit verwendet. Der vorliegende Text füllt eine Lücke und ist so aufgebaut, dass der Schwerpunkt auf Methodik und relevantem Inhalt liegt. Er bietet eine schrittweise Herangehensweise, mit der der Student die Lösung kombinatorischer Optimierungsprobleme erlernt. Anwendungen und Probleme werden anhand aktueller technologischer Entwicklungen wie drahtlose Kommunikation, Cloud Computing, soziale Netzwerke und maschinelles Lernen betrachtet, um nur einige zu nennen, und der Leser wird an die Grenzen der kombinatorischen Optimierung geführt. In jedem Kapitel werden gängige Probleme wie Minimum Spanning Tree, kürzester Pfad, Maximum Matching, Network Flow, Set-Cover sowie Schlüsselalgorithmen wie Greedy Algorithm, Dynamic Programming, Augmenting Path und Divide-and-Conquer vorgestellt. Historische Anmerkungen, zahlreiche Übungen in jedem Kapitel, strategisch platzierte Grafiken und eine umfangreiche Bibliographie gehören zu den Glanzstücken dieses Lehrbuchs. Bucheinband-Typ:HardcoverUnterstützte Sprachen:EnglischGeschrieben von:Ding-Zhu Du, Panos M. Pardalos, Xiaodong Hu, Weili WuTyp:PapierbuchAnzahl der Seiten:402 SeitenIllustrator:12 b/w illustrations, 132 illustrations in colourVeröffentlicher:Springer ChamFreigabedatum (TT/MM/JJ):27/09/2022Editionstyp:Erste AusgabeInternational Standard Book Number (ISBN):9783031105944

Nonconvex Optimization is a multi-disciplinary research field that deals with the characterization and computation of local/global minima/maxima of nonlinear, nonconvex, nonsmooth, discrete and continuous functions. Nonconvex optimization problems are frequently encountered in modeling real world systems for a very broad range of applications including engineering, mathematical economics, management science, financial engineering, and social science. This contributed volume consists of selected contributions from the Advanced Training Programme on Nonconvex Optimization and Its Applications held at Banaras Hindu University in March 2009. It aims to bring together new concepts, theoretical developments, and applications from these researchers. Both theoretical and applied articles are contained in this volume which adds to the state of the art research in this field. Topics in Nonconvex Optimization is suitable for advanced graduate students and researchers in thisarea.

Nonconvex Optimization is a multi-disciplinary research field that deals with the characterization and computation of local/global minima/maxima of nonlinear, nonconvex, nonsmooth, discrete and continuous functions. Nonconvex optimization problems are frequently encountered in modeling real world systems for a very broad range of applications including engineering, mathematical economics, management science, financial engineering, and social science. This contributed volume consists of selected contributions from the Advanced Training Programme on Nonconvex Optimization and Its Applications held at Banaras Hindu University in March 2009. It aims to bring together new concepts, theoretical developments, and applications from these researchers. Both theoretical and applied articles are contained in this volume which adds to the state of the art research in this field. Topics in Nonconvex Optimization is suitable for advanced graduate students and researchers in thisarea.

Nonconvex Optimization is a multi-disciplinary research field that deals with the characterization and computation of local/global minima/maxima of nonlinear, nonconvex, nonsmooth, discrete and continuous functions. Nonconvex optimization problems are frequently encountered in modeling real world systems for a very broad range of applications including engineering, mathematical economics, management science, financial engineering, and social science. This contributed volume consists of selected contributions from the Advanced Training Programme on Nonconvex Optimization and Its Applications held at Banaras Hindu University in March 2009. It aims to bring together new concepts, theoretical developments, and applications from these researchers. Both theoretical and applied articles are contained in this volume which adds to the state of the art research in this field. Topics in Nonconvex Optimization is suitable for advanced graduate students and researchers in thisarea.

Due to the general complementary convex structure underlying most nonconvex optimization problems encountered in applications, convex analysis plays an essential role in the development of global optimization methods. This book develops a coherent and rigorous theory of deterministic global optimization from this point of view. Part I constitutes an introduction to convex analysis, with an emphasis on concepts, properties and results particularly needed for global optimization, including those pertaining to the complementary convex structure. Part II presents the foundation and application of global search principles such as partitioning and cutting, outer and inner approximation, and decomposition to general global optimization problems and to problems with a low-rank nonconvex structure as well as quadratic problems. Much new material is offered, aside from a rigorous mathematical development. Audience: The book is written as a text for graduate students in engineering, mathematics, operations research, computer science and other disciplines dealing with optimization theory. It is also addressed to all scientists in various fields who are interested in mathematical optimization.

Combine two typically separate sources of data—behavioral quantitative data and usability testing qualitative data—into a powerful single tool that helps improve your organization’s website by increasing conversion and ROI. The combination of the what is happening data of website activity, coupled with the why it's happening data of usability testing, provides a complete 360-degree view into what is causing poor performance, where your website can be optimized, and how it can be improved. There are plenty of books focusing on big data and using data analytics to improve websites, or on utilizing usability testing and UX research methods for improvement. This is the first book that combines both subjects into a methodology you can use over and over again to improve any website. UX Optimization is ideal for anyone who wants to combine the power of quantitative data with the insights provided by qualitative data to improve website results. The book uses step-by-step instructions with photos, drawings, and supporting screenshots to show you how to: define personas, conduct behavioral UX data analysis, perform UX and usability testing evaluations, and combine behavioral UX and usability data to create a powerful set of optimization recommendations that can dramatically improve any website. What You’ll Learn Understand personas: what they are and how to use them to analyze data Use quantitative research tools and techniques for analysis Know where to find UX behavioral data and when to use it Use qualitative research tools, techniques, and procedures Analyze qualitative data to find patterns of consistent task flow errors Combine qualitative and quantitative data for a 360-degree view Make recommendations for optimizations based on your findings Test optimization recommendations to ensure improvements are achieved Who This Book Is For Big data analytics (quantitative) professionals who want to learn more about the qualitative side of analysis; UX researchers, usability testers, and UX designers (qualitative professionals) who want to know more about big data and behavioral UX analysis; and students of UX, UX designers, product managers, developers, and those at startups who want to understand how to use behavioral UX and usability testing data to optimize their websites and apps.

Combine two typically separate sources of data—behavioral quantitative data and usability testing qualitative data—into a powerful single tool that helps improve your organization’s website by increasing conversion and ROI. The combination of the what is happening data of website activity, coupled with the why it's happening data of usability testing, provides a complete 360-degree view into what is causing poor performance, where your website can be optimized, and how it can be improved. There are plenty of books focusing on big data and using data analytics to improve websites, or on utilizing usability testing and UX research methods for improvement. This is the first book that combines both subjects into a methodology you can use over and over again to improve any website. UX Optimization is ideal for anyone who wants to combine the power of quantitative data with the insights provided by qualitative data to improve website results. The book uses step-by-step instructions with photos, drawings, and supporting screenshots to show you how to: define personas, conduct behavioral UX data analysis, perform UX and usability testing evaluations, and combine behavioral UX and usability data to create a powerful set of optimization recommendations that can dramatically improve any website. What You’ll Learn Understand personas: what they are and how to use them to analyze data Use quantitative research tools and techniques for analysis Know where to find UX behavioral data and when to use it Use qualitative research tools, techniques, and procedures Analyze qualitative data to find patterns of consistent task flow errors Combine qualitative and quantitative data for a 360-degree view Make recommendations for optimizations based on your findings Test optimization recommendations to ensure improvements are achieved Who This Book Is For Big data analytics (quantitative) professionals who want to learn more about the qualitative side of analysis; UX researchers, usability testers, and UX designers (qualitative professionals) who want to know more about big data and behavioral UX analysis; and students of UX, UX designers, product managers, developers, and those at startups who want to understand how to use behavioral UX and usability testing data to optimize their websites and apps.

Nonconvexity and nonsmoothness arise in a large class of engineering applica­ tions. In many cases of practical importance the possibilities offered by opti­ mization with its algorithms and heuristics can substantially improve the per­ formance and the range of applicability of classical computational mechanics algorithms. For a class of problems this approach is the only one that really works. The present book presents in a comprehensive way the application of opti­ mization algorithms and heuristics in smooth and nonsmooth mechanics. The necessity of this approach is presented to the reader through simple, represen­ tative examples. As things become more complex, the necessary material from convex and nonconvex optimization and from mechanics are introduced in a self-contained way. Unilateral contact and friction problems, adhesive contact and delamination problems, nonconvex elastoplasticity, fractal friction laws, frames with semi­ rigid connections, are among the applications which are treated in details here. Working algorithms are given for each application and are demonstrated by means of representative examples. The interested reader will find helpful references to up-to-date scientific and technical literature so that to be able to work on research or engineering topics which are not directly covered here.

This book presents state-of-the-art technologies in wind farm layout optimization and control to improve the current industry/research practice. The contents take readers towards a different kind of uncertainty handling through the discussion on several techniques enabling maximum energy harnessing out of uncertain situations. The book aims to give a detailed overview of such concepts in the first part, where the recent advancements in the fields of (i) Wind farm layout optimization, (ii) Multi-objective Optimization and Uncertainty handling in optimization methods, (iii) Development of Machine Learning-based surrogate models in optimization, and (iv) Different types of wake models for wind farms will be discussed. The second part will cover the application of the aforementioned techniques on the wind farm layout optimization and control through several chapters such as (i) Wind farm performance assessment using Computational Fluid Dynamics (CFD) tools, (ii) Artificial Neural Network (ANN) based hybrid wake models, (iii) Long Short-term Memory (LSTM) & Support Vector Regression (SVR) based forecasting and micro-siting, (iv) windfarm micro-siting using data-driven Robust Optimization (RO) as well as Generative Adversarial Networks (GANs), (v) Reinforcement learning (RL) based wind farm control and (vi) Application of eXplainable AI (XAI) tools for interpreting wind time-series data. In this manner, the book provides state-of-the-art techniques in the fields of multi-objective optimization, Evolutionary Algorithms, Machine Learning surrogate models, Bayesian Optimization, Data Analysis, and Optimization under Uncertainty and their applications in the field of wind energy generation that can be extremely generic and can be applied to many other engineering fields. This volume will be of interest to those in academia and industry.

This book presents state-of-the-art technologies in wind farm layout optimization and control to improve the current industry/research practice. The contents take readers towards a different kind of uncertainty handling through the discussion on several techniques enabling maximum energy harnessing out of uncertain situations. The book aims to give a detailed overview of such concepts in the first part, where the recent advancements in the fields of (i) Wind farm layout optimization, (ii) Multi-objective Optimization and Uncertainty handling in optimization methods, (iii) Development of Machine Learning-based surrogate models in optimization, and (iv) Different types of wake models for wind farms will be discussed. The second part will cover the application of the aforementioned techniques on the wind farm layout optimization and control through several chapters such as (i) Wind farm performance assessment using Computational Fluid Dynamics (CFD) tools, (ii) Artificial Neural Network (ANN) based hybrid wake models, (iii) Long Short-term Memory (LSTM) & Support Vector Regression (SVR) based forecasting and micro-siting, (iv) windfarm micro-siting using data-driven Robust Optimization (RO) as well as Generative Adversarial Networks (GANs), (v) Reinforcement learning (RL) based wind farm control and (vi) Application of eXplainable AI (XAI) tools for interpreting wind time-series data. In this manner, the book provides state-of-the-art techniques in the fields of multi-objective optimization, Evolutionary Algorithms, Machine Learning surrogate models, Bayesian Optimization, Data Analysis, and Optimization under Uncertainty and their applications in the field of wind energy generation that can be extremely generic and can be applied to many other engineering fields. This volume will be of interest to those in academia and industry.

This book focuses on the treatment and disposal technology used for solid waste in China, puts forward an optimal management scheme that takes into account the complete lifecycle, and introduces a new technical model that reflects the synergies of resource utilization and secondary pollution control. In addition, it provides a set of methods for professional on-site investigation, risk assessment, classification management and control to minimize the risk of groundwater contamination by solid waste at landfill. Given the extent and depth of knowledge and experience gathered in the book, it offers an important reference guide for government managers, environmental researchers, and all those involved in and concerned about solid waste disposal.