Search Results - (((((((ant OR antiek) OR want) OR kkantor) OR cantor) OR anne) OR halten) OR canoeing) algorithms.

Microwave and millimeter wave circuits and systems : emerging design, technologies, and applications

Table of Contents: “…1.1.7 MBF Model -- the Memoryless PA Behavioural Model of ChoiceAcknowledgements; References; 2 Artificial Neural Network in Microwave Cavity Filter Tuning; 2.1 Introduction; 2.2 Artificial Neural Networks Filter Tuning; 2.2.1 The Inverse Model of the Filter; 2.2.2 Sequential Method; 2.2.3 Parallel Method; 2.2.4 Discussion on the ANN's Input Data; 2.3 Practical Implementation -- Tuning Experiments; 2.3.1 Sequential Method; 2.3.2 Parallel Method; 2.4 Influence of the Filter Characteristic Domain on Algorithm Efficiency; 2.5 Robots in the Microwave Filter Tuning; 2.6 Conclusions; Acknowledgement…”
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Biomedical image analysis recipes in MATLAB : for life scientists and engineers by Reyes-Aldasoro, Constantino Carlos

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Pediatric incontinence : evaluation and clinical management

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Knowledge mining using intelligent agents

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Discovering knowledge in data : an introduction to data mining by Larose, Daniel T.

Table of Contents: “…DISCOVERING KNOWLEDGE IN DATA -- Contents -- Preface -- 1 An Introduction to Data Mining -- 1.1 What is Data Mining? -- 1.2 Wanted: Data Miners -- 1.3 The Need for Human Direction of Data Mining -- 1.4 The Cross-Industry Standard Practice for Data Mining -- 1.4.1 Crisp-DM: The Six Phases -- 1.5 Fallacies of Data Mining -- 1.6 What Tasks Can Data Mining Accomplish? …”
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Clinical simulation : operations, engineering and management

Table of Contents: “…; 3.5 The Systems Approach to Training; 3.6 Defining the Performance Requirement; 3.7 Cost Versus Value Added; 3.8 Operations Cost; 3.9 Standardization: What is it, and who Wants it?; 3.10 Patients as Training Conditions; 3.11 Equipment as Training Conditions; 3.12 Increase in Training System Cost; 3.13 You as the Leader-Manager; 3.14 Conclusion; Endnotes; Topic II What's In It For Me.…”
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Digitalization of Society and Socio-Political Issues. 1, Digital, Communication, and Culture

Table of Contents: “…The Digitalization of Cultural Policies in France 149; Anne BELLON 14.1.…”
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Networks-on-chip : from implementations to programming paradigms by Ma, Sheng, Huang, Libo, 1976-, Lai, Mingche, Shi, Wei

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Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

Table of Contents: “…Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs).…”
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Learning Geospatial Analysis with Python - Second Edition. by Lawhead, Joel

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Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. by Julian, David

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Millimeter-wave digitally intensive frequency generation in CMOS by Wu, Wanghua, Staszewski, Robert Bogdan, 1965-, Long, John R. (John Robert), 1958-

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Computational models of argument : Proceedings of COMMA 2012

Table of Contents: “…Simari -- Automated Deployment of Argumentation Protocols / Michael Rovatsos -- On Preferred Extension Enumeration in Abstract Argumentation / Katie Atkinson -- Towards Experimental Algorithms for Abstract Argumentation / Katie Atkinson.…”
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The next economic disaster : why it's coming and how to avoid it by Vague, Richard

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Oracle Primavera P6 version 8 : project and portfolio management : a comprehensive guide to managing projects, resources, and portfolios using Primavera P6, through version 8.2 by Williams, Daniel L.

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Stochastic filtering with applications in finance by Bhar, Ramaprasad

Table of Contents: “…Economic convergence in a filtering framework. 3.3. Ex-ante equity risk premium. 3.4. Concluding remarks -- 4. …”
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Design optimization of fluid machinery : applying computational fluid dynamics and numerical optimization by Kim, Kwang-Yong, 1956-, Samad, Abdus, Benini, Ernesto

Table of Contents: “…2.2.5.3 Periodic/Cyclic Boundary Conditions2.2.5.4 Symmetry Boundary Conditions; 2.2.6 Moving Reference Frame (MRF); 2.2.7 Verification and Validation; 2.2.8 Commercial CFD Software; 2.2.9 Open Source Codes; 2.2.9.1 OpenFOAM; References; Chapter 3 Optimization Methodology; 3.1 Introduction; 3.1.1 Engineering Optimization Definition; 3.1.2 Design Space; 3.1.3 Design Variables and Objectives; 3.1.4 Optimization Procedure; 3.1.5 Search Algorithm; 3.2 Multi-Objective Optimization (MOO); 3.2.1 Weighted Sum Approach; 3.2.2 Pareto-Optimal Front…”
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Listed Volatility and Variance Derivatives : a Python-based Guide. by Hilpisch, Yves

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Statistical learning from a regression perspective by Berk, Richard A.

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Credit securitizations and derivatives : challenges for the global markets

Table of Contents: “…Market Credit Risk Pricing -- Regulation -- Developments in Structured Finance Markets -- Impairments of Asset-Backed Securities and Outstanding Ratings -- Issuance of Asset-backed Securities and Outstanding Volume -- Global CDO Issuance and Outstanding Volume -- PART II CREDIT PORTFOLIO RISK MEASUREMENT -- Mortgage Credit Risk -- Five C's of Credit and Mortgage Credit Risk -- Determinants of Mortgage Default, Loss Given Default and Exposure at Default -- Determinants of Mortgage Default -- Determinants of Mortgage LGD -- Determinants of Mortgage EAD -- Modeling Methods for Default, LGD and EAD -- Model Risk Management -- Credit Portfolio Correlations and Uncertainty -- Introduction -- Gaussian and Semi-Gaussian Single Risk Factor Model -- Individual and Simultaneous Confidence Bounds and Intervals -- Confidence Intervals for Asset Correlations -- Confidence Intervals for Default and Survival Time Correlations -- Confidence Intervals for Default Correlations -- Confidence Intervals for Survival Time Correlations -- Credit Portfolio Correlations with Dynamic Leverage Ratios -- The Hui et al. (2007) Model -- The Method of Images for Constant Coefficients -- The Method of Images for Time-Varying Coefficients -- Modelling Default Correlations in a Two-Firm Model -- Default Correlations -- A Two-Firm Model with Dynamic Leverage Ratios -- Method of Images for Constant Coefficients -- Method of Images for Time-Varying Coefficients -- Alternative Methodologies for General Values -- Numerical Results -- Accuracy -- The Impact of Correlation between Two Firms -- The Impact of Different Credit Quality Paired Firms -- The Impact of Volatilities -- The Impact of Drift Levels -- The Impact of Initial Value of Leverage Ratio Levels -- Impact of Correlation between Firms and Interest Rates -- The Price of Credit-Linked Notes -- A Hierarchical Model of Tail-Dependent Asset Returns -- The Variance Compound Gamma Model -- Multivariate Process for Logarithmic Asset Returns -- Dependence Structure -- Sampling -- Copula Properties -- An Application Example -- Portfolio Setup -- Test Portfolios -- Parameter Setup -- Simulation Results -- Importance Sampling Algorithm -- Conclusions -- Appendix A: The VCG Probability Distribution Function Appendix B: HAC Representation for the VCG Framework -- Monte Carlo Methods for Portfolio Credit Risk -- Modeling Credit Portfolio Losses -- Risk Measures -- Modeling Dependency -- Estimating Risk Measures via Monte Carlo -- Crude Monte Carlo Estimators -- Importance Sampling -- Specific Models -- The Bernoulli Mixture Model -- Factor Models -- Copula Models -- Intensity Models -- An Example Point Process Model -- Appendix A: A Primer on Rare-event Simulation -- Efficiency -- Importance Sampling -- The Choice of g -- Adaptive Importance Sampling -- Importance Sampling for Stochastic Processes -- Credit Portfolio Risk and Diversification -- Introduction -- Model Setup -- Independent Asset Values -- Correlated Asset Values -- Large Portfolio Limit -- Correlated Diffusion -- Correlated GARCH Process -- Applications of the Structural Recovery Rate -- Conclusions -- PART III CREDIT PORTFOLIO RISK SECURITIZATION AND TRANCHING -- Differences in Tranching Methods: Some Results and Implications -- Defining a Tranche -- The Mathematics of Tranching -- PD-based Tranching -- EL-based Tranching -- The EL of a Tranche Necessarily Increases When Either the Attachment Point or the Detachment Point is Decreased -- Upper Bound on Tranche Expected LGD (LGDt) Assumption Given EL-based Tranches -- Skipping of Some Tranches in the EL-based Approach -- Global Structured Finance Rating -- Asset-Backed Securities -- The ABS Structure for the Experiment -- Cash Flow Modeling -- Modeling and Simulating Defaults -- Expected Loss Rating -- Global Sensitivity Analysis -- Elementary Effects -- Variance-based Method -- Global Sensitivity Analysis Results -- Uncertainty Analysis -- Sensitivity Analysis -- Global Rating -- PART IV CREDIT DERIVATIVES -- Analytic Dynamic Factor Copula Model -- Pricing Equations -- One-factor Copula Model -- Multi-period Factor Copula Models -- Calibration -- Dynamic Modeling of Credit Derivatives -- General Model Choice -- Modeling Option Prices -- Modeling Credit Risk -- Portfolio Credit Derivatives -- Modeling Asset Dynamics -- The Market Model -- The Asset-value Model -- Empirical Analysis -- Elementary Data -- Implied Dividends -- Market Dynamics -- Asset Value Model -- Tranche Pricing -- Out-of-time Application -- Pricing and Calibration in Market Models -- Basic notions -- The model -- Modeling Assumptions -- Absence of Arbitrage -- An affine specification -- Pricing -- Calibration -- Calibration Procedure -- Calibration Results -- Appendix A: Computations -- Counterparty Credit Risk and Clearing of Derivatives -- From the Perspective of an Industrial Corporate with a Focus on Commodity Markets -- Credit exposures in commodity business -- Settlement Exposure -- Performance Exposure -- Example of Fixed Price Deal with Performance Exposure -- Example of a Floating Price Deal with Performance Exposure -- General Remarks on Credit Exposure Concepts -- Ex Ante exposure-reducing techniques -- Payment Terms -- Material Adverse Change Clauses -- Master Agreements -- Netting -- Margining -- Close Out Exposure and Threshold -- Ex Ante risk-reducing techniques -- Credit Enhancements in General -- Parent Company Guarantees -- Letters of Credit -- Credit Insurance -- Clearing via a Central Counterparty -- Ex Post risk-reducing techniques -- Factoring -- Novation -- Risk-reducing Trades -- Hedging with CDS -- Hedging with Contingent-CDS -- Hedging with Puts on Equity -- Ex Post work out considerations -- Practical credit risk management and pricing Peculiarities of commodity markets -- Peculiarities of commodity related credit portfolios -- Credit Risk Capital for a commodity related portfolio measured with an extension of CreditMetrics -- CreditRisk+ study: applied to a commodity related credit portfolio -- CDS Industrial Sector Indices, Credit and Liquidity Risk -- The Data -- Methodology and Results -- Preliminary Analysis -- Common Factor Analysis -- Stability of Relations -- Risk Transfer and Pricing of Illiquid Assets with Loan CDS -- Shipping Market -- Loan Credit Default Swaps -- LCDS Pricing -- Modeling LCDS Under the Intensity-based Model -- Valuation Framework for LCDS -- The Structural Approach -- Credit Risk in Shipping Loans -- Valuation of LCDS on Shipping Loans -- Simulation Model -- Numerical Results -- Appendix A: Monte Carlo Parameterization PART V REGULATION -- Regulatory Capital Requirements for Securitizations -- Regulatory Approaches for Securitizations -- Ratings Based Approach (RBA) -- Supervisory Formula Approach (SFA) -- Standardized Approach (SA) -- Post-crisis Revisions to the Basel Framework -- Regulating OTC Derivatives -- The Wall Street Transparency and Accountability Part of the Dodd-Frank Act of 2010 -- Which Derivatives Will Be Affected? …”
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