03436nam 2200601Ia 450 991078200670332120230721032431.094-012-0569-81-4356-3907-310.1163/9789401205696(CKB)1000000000533856(EBL)556796(OCoLC)248060990(SSID)ssj0000119491(PQKBManifestationID)11140946(PQKBTitleCode)TC0000119491(PQKBWorkID)10057357(PQKB)10888187(OCoLC)248060990(OCoLC)712988510(OCoLC)764535814(OCoLC)781321588(OCoLC)796037170(OCoLC)989054123(nllekb)BRILL9789401205696(Au-PeEL)EBL556796(CaPaEBR)ebr10380138(MiAaPQ)EBC556796(EXLCZ)99100000000053385620080331d2008 uy 0engur|n|---|||||txtccr"Ces forces obscures de l'âme"[electronic resource] women, race and origins in the writings of Albert Camus /Christie MargerrisonAmsterdam [etc.] Rodopi20081 online resource (357 p.)Faux titre,0167-9392 ;311Description based upon print version of record.90-420-2379-1 Includes bibliographical references and index.Preliminary Material -- Abbreviations -- Introduction -- Early Confrontations with Others: the Écrits de jeunesse -- The Death of Woman and the Birth of Culture -- The Man-god and Death as an Act of the Will -- The Dark Continent of L’Étranger -- Mythical women in La Peste -- Woman, Race and the Fall of Man -- Sexual topographies -- The First Man -- Selected Bibliography -- Index.This is the first major investigation of Camus’s prose fiction to explore the developing presentation of women, from the author’s earliest writings to his last, unfinished novel. Avoiding the traditional relegation of this subject to an emotional or private sphere, it traces Camus’s intellectual development in order to demonstrate the centrality of this subject to Camus’s work as a whole. If the Absurd, constructed over the body of the “real” woman, liberates the writer to follow a “true path” of literary creation, the impending loss of his Algerian homeland impells a return to “all that he had not been free to choose”, the ties of blood. These conflictual and unresolved ties are here investigated, in conjunction with the presentation of mythical female figures expressing Camus’s darkest fears, partly voiced in other writings, concerning that “other” Algeria for which he would never fight. Exploring complex interconnections between sexuality, “race” and colonialism, this volume is pertinent to all who are interested in the writings of Camus, particularly those seeking relevant new ways of approaching his work.Faux titre ;no. 311.Women in literatureRace in literatureWomen in literature.Race in literature.848.91409Margerrison Christine1472199MiAaPQMiAaPQMiAaPQBOOK9910782006703321"Ces forces obscures de l'âme"3684907UNINA05381nam 2200649Ia 450 991100659660332120200520144314.01-61583-657-81-281-03520-397866110352040-08-050371-3(CKB)1000000000344080(EBL)313613(OCoLC)476102756(SSID)ssj0000135055(PQKBManifestationID)11143446(PQKBTitleCode)TC0000135055(PQKBWorkID)10057695(PQKB)10852507(MiAaPQ)EBC313613(EXLCZ)99100000000034408019990806d2000 uy 0engur|n|---|||||txtccrData reconciliation & gross error detection an intelligent use of process data /Shankar Narasimhan and Cornelius JordacheHouston, TX Gulf Pub. Co.c20001 online resource (425 p.)Description based upon print version of record.0-88415-255-3 Includes bibliographical references and indexes.Front Cover; Data Reconciliation & Gross Error Detection; Copyright Page; Contents; Acknowledgments; Preface; Chapter 1. The Importance of Data Reconciliation and Gross Error Detection; Process Data Conditioning Methods; Industrial Examples of Steady-State Data Reconciliation; Data Reconciliation Problem Formulation; Examples of Simple Reconciliation Problems; Benefits from Data Reconciliation and Gross Error Detection; A Brief History of Data Reconciliation and Gross Error Detection; Scope and Organization of the Book; Summary; ReferencesChapter 2. Measurement Errors and Error Reduction TechniquesClassification of Measurements Errors; Error Reduction Methods; Summary; References; Chapter 3. Linear Steady-State Data Reconciliation; Linear Systems With All Variables Measured; Linear Systems With Both Measured and Unmeasured Variables; Estimating Measurement Error Covariance Matrix; Simulation Technique for Evaluating Data Reconciliation; Summary; References; Chapter 4. Steady-State Data Reconciliation for Bilinear Systems; Bilinear Systems; Data Reconciliation of Bilinear SystemsBilinear Data Reconciliation Solution TechniquesSummary; References; Chapter 5. Nonlinear Steady-State Data Reconciliation,; Formulation of Nonlinear Data Reconciliation Problems; Solution Techniques for Equality Constrained Problems; Nonlinear Programming (NLP) Methods for Inequality Constrained; Variable Classification for Nonlinear Data Reconciliation; Comparison of Nonlinear Optimization Strategies for Data Reconciliation; Summary; References; Chapter 6. Data Reconciliation in Dynamic Systems; The Need for Dynamic Data Reconciliation; Linear Discrete Dynamic System ModelOptimal State Estimation Using Kalman FilterDynamic Data Reconciliation of Nonlinear Systems; Summary; References; Chapter 7. Introduction to Gross Error Detection; Problem Statements; Basic Statistical Tests for Gross Error Detection; Gross Error Detection Using Principal Component (PC) Tests; Statistical Tests for General Steady-State Models; Techniques for Single Gross Error Identification; Detectability and Identifiability of Gross Errors; Proposed Problems; Summary; References; Chapter 8. Multiple Gross Error Identification Strategies for Steady-State ProcessesStrategies for Multiple Gross Error Identification in Linear ProcessesPerformance Measures for Evaluating Gross Error Identification Strategies; Comparison of Multiple Gross Error Identification Strategies; Gross Error Detection in Nonlinear Processes; Bayesian Approach to Multiple Gross Error Identification; Proposed Problems; Summary; References; Chapter 9. Gross Error Detection in Linear Dynamic Systems; Problem Formulation for Detection of Measurement Biases; Statistical Properties of Innovations and the Global Test; Generalized Likelihood Ratio Method; Fault Diagnosis TechniquesThe State of the ArtThis book provides a systematic and comprehensive treatment of the variety of methods available for applying data reconciliation techniques. Data filtering, data compression and the impact of measurement selection on data reconciliation are also exhaustively explained.Data errors can cause big problems in any process plant or refinery. Process measurements can be correupted by power supply flucutations, network transmission and signla conversion noise, analog input filtering, changes in ambient conditions, instrument malfunctioning, miscalibration, and the wear and corrosion of senData reconciliation and gross error detectionChemical process controlAutomationAutomatic data collection systemsError analysis (Mathematics)Chemical process controlAutomation.Automatic data collection systems.Error analysis (Mathematics)660/.2815Narasimhan Shankar1824849Jordache Cornelius1824850MiAaPQMiAaPQMiAaPQBOOK9911006596603321Data reconciliation & gross error detection4392230UNINA