No description available.

No description available.

No description available.

In the building automation domain, there are gaps among various tasks related to design engineering. As a result created system designs must be adapted to the given requirements on system functionality, which is related to increased costs and engineering effort than planned. For this reason standards are prepared to enable a coordination among these tasks by providing guidelines and unified artifacts for the design. Moreover, a huge variety of prefabricated devices offered from different manufacturers on the market for building automation that realize building automation functions by preprogrammed software components. Current methods for design creation do not consider this variety and design solution is limited to product lines of a few manufacturers and expertise of system integrators. Correspondingly, this results in design solutions of a limited quality. Thus, a great optimization potential of the quality of design solutions and coordination of tasks related to design engineering arises. For given design requirements, the existence of a high number of devices that realize required functions leads to a combinatorial explosion of design alternatives at different price and quality levels. Finding optimal design alternatives is a hard problem to which a new solution method is proposed based on heuristical approaches. By integrating problem specific knowledge into algorithms based on heuristics, a promisingly high optimization performance is achieved. Further, optimization algorithms are conceived to consider a set of flexibly defined quality criteria specified by users and achieve system design solutions of high quality. In order to realize this idea, optimization algorithms are proposed in this thesis based on goal-oriented operations that achieve a balanced convergence and exploration behavior for a search in the design space applied in different strategies. Further, a component model is proposed that enables a seamless integration of design engineering tasks according to the related standards and application of optimization algorithms.

Etwa die Hälfte aller Solarzellen weltweit wird aus blockerstarrtem Silizium hergestellt. Derartige Blöcke weisen in ihren Außenbereichen eine verringerte Diffusionslänge der Minoritätsladungsträger auf. Um die Ursache dafür im Fall des bodennahen Bereichs zu bestimmen wurden zwei Spezialblöcke (ein Block mit reduzierter Bor-Dotierung und ein Block mit Phosphor-Dotierung) - u. a. mittels DLTS und FTIR - auf Kristalldefekte untersucht. Zusätzlich zu Dotierelementen (B, P, Al, As) wurden im Bodenbereich folgende Defekte nachgewiesen: <u>Metalle</u>: Fe, Cr <u>Sauerstoffhaltige Defekte</u>: Interstitieller Sauerstoff, Thermische Donatoren (TD), O1, O2 <u>Stickstoffhaltige Defekte</u>: NN-Paar, NNO-Komplex, Shallow Thermal Donors (STD) <u>Ausgedehnte Defekte</u>: Versetzungen, Ausscheidungen, Korngrenzen. Die Verteilung der flachen Donatoren (P, TD, STD, As) und Akzeptoren (B, Al) bestimmt den Widerstandsverlauf im bodennahen Bereich des Phosphor dotierten Spezialblocks. Das dortige Diffusionslängenprofil kann im Rahmen der Shockley-Read-Hall-Statistik erst durch eine Erhöhung des Minoritätseinfangquerschnitts für das Cr-Niveau (Faktor 5) bzw. für das STD-Niveau (Faktor 10) nachgezeichnet werden. Eisen, Versetzungen und Korngrenzen haben hier keinen wesentlichen Einfluss. In den untersten Millimetern des Spezialblocks müssen weitere Defekte hinzukommen, die die Diffusionslänge zusätzlich reduzieren; Thermische Donatoren und O1 und eventuell Ausscheidungen kommen dazu in Frage. Die sinngemäße Übertragung der Konzentrationsverläufe aus den beiden Spezialblöcken auf einen Block mit einer produktionsüblichen Dotierung ([B]≈10^{<small>16</small>}/cm^{<small>3</small>}) ergibt, dass in diesem Fall verschiedene Defekte (TD, STD, CrB und FeB) einen Beitrag zur Diffusionslängenreduktion im bodennahen Blockbereich liefern.

The master’s thesis deals with the noise diagnostic in the solar cells. Describes the main kinds of noises. The samples were quality and reliability screened using noise reliability indicators. The samples were surveyed by measuring the I-V characteristics, the noise spectral density as a function of forward voltage and frequency. It was calculated the noise spectral density as a function of forward current.

Etwa die Hälfte aller Solarzellen weltweit wird aus blockerstarrtem Silizium hergestellt. Derartige Blöcke weisen in ihren Außenbereichen eine verringerte Diffusionslänge der Minoritätsladungsträger auf. Um die Ursache dafür im Fall des bodennahen Bereichs zu bestimmen wurden zwei Spezialblöcke (ein Block mit reduzierter Bor-Dotierung und ein Block mit Phosphor-Dotierung) - u. a. mittels DLTS und FTIR - auf Kristalldefekte untersucht. Zusätzlich zu Dotierelementen (B, P, Al, As) wurden im Bodenbereich folgende Defekte nachgewiesen: <u>Metalle</u>: Fe, Cr <u>Sauerstoffhaltige Defekte</u>: Interstitieller Sauerstoff, Thermische Donatoren (TD), O1, O2 <u>Stickstoffhaltige Defekte</u>: NN-Paar, NNO-Komplex, Shallow Thermal Donors (STD) <u>Ausgedehnte Defekte</u>: Versetzungen, Ausscheidungen, Korngrenzen. Die Verteilung der flachen Donatoren (P, TD, STD, As) und Akzeptoren (B, Al) bestimmt den Widerstandsverlauf im bodennahen Bereich des Phosphor dotierten Spezialblocks. Das dortige Diffusionslängenprofil kann im Rahmen der Shockley-Read-Hall-Statistik erst durch eine Erhöhung des Minoritätseinfangquerschnitts für das Cr-Niveau (Faktor 5) bzw. für das STD-Niveau (Faktor 10) nachgezeichnet werden. Eisen, Versetzungen und Korngrenzen haben hier keinen wesentlichen Einfluss. In den untersten Millimetern des Spezialblocks müssen weitere Defekte hinzukommen, die die Diffusionslänge zusätzlich reduzieren; Thermische Donatoren und O1 und eventuell Ausscheidungen kommen dazu in Frage. Die sinngemäße Übertragung der Konzentrationsverläufe aus den beiden Spezialblöcken auf einen Block mit einer produktionsüblichen Dotierung ([B]≈10^{<small>16</small>}/cm^{<small>3</small>}) ergibt, dass in diesem Fall verschiedene Defekte (TD, STD, CrB und FeB) einen Beitrag zur Diffusionslängenreduktion im bodennahen Blockbereich liefern.

Interleukin-10 (IL-10) is an important regulator of immune responses secreted by different cell types. Previous results from our group suggested that the biological effects of this cytokine critically depend on its cellular source. Recent studies reported IL-10 dependent immunosuppressive functions of a specialized subset of regulatory B cells and mast cells. These results relied on adoptive cell transfers, a technique which can potentially introduce artifacts. Therefore, we aimed to readdress these questions in independent models using IL-10 transcriptional reporter mice and various conditional IL-10 mutant mice. Findings in IL-10 reporter system suggested prominent IL-10 transcription in regulatory B cells upon LPS administration. Exposure of mice to contact allergen revealed robust reporter expression in CD8 T cells, moderate to mild reporter expression in CD4 T cells and dendritic cells (DC) respectively, and lack of reporter expression in B cells, mast cells and NK cells in allergen challenged ears. We generated cell-type specific IL-10 mutants by Cre/LoxP-mediated conditional gene inactivation. Efficiency and specificity of Cre-mediated recombination was demonstrated by Southern blot and PCR methods. Various immunogenic challenges in conditional IL-10 mutants did not reveal a role for B cell-derived IL-10 in restraining innate TLR or T cell-dependent inflammatory responses. Likewise, mice with selective inactivation of the il10 gene in mast cells exhibited normal CHS responses and unaltered immune response to CpG oligodeoxynucleotides. On the other hand, DC-specific IL-10 mutants developed excessive inflammatory responses to contact allergens, while innate responses to TLR ligands were not altered. This indicates a non-redundant role for DC-derived IL-10 in contact allergy. Thus, the conditional IL-10 ‘‘knockout’’ mice combined with the novel transcriptional IL-10 reporter system can serve as ideal tools to understand the cell-type specific contributions to IL-10-mediated immune regulation.

In the building automation domain, there are gaps among various tasks related to design engineering. As a result created system designs must be adapted to the given requirements on system functionality, which is related to increased costs and engineering effort than planned. For this reason standards are prepared to enable a coordination among these tasks by providing guidelines and unified artifacts for the design. Moreover, a huge variety of prefabricated devices offered from different manufacturers on the market for building automation that realize building automation functions by preprogrammed software components. Current methods for design creation do not consider this variety and design solution is limited to product lines of a few manufacturers and expertise of system integrators. Correspondingly, this results in design solutions of a limited quality. Thus, a great optimization potential of the quality of design solutions and coordination of tasks related to design engineering arises. For given design requirements, the existence of a high number of devices that realize required functions leads to a combinatorial explosion of design alternatives at different price and quality levels. Finding optimal design alternatives is a hard problem to which a new solution method is proposed based on heuristical approaches. By integrating problem specific knowledge into algorithms based on heuristics, a promisingly high optimization performance is achieved. Further, optimization algorithms are conceived to consider a set of flexibly defined quality criteria specified by users and achieve system design solutions of high quality. In order to realize this idea, optimization algorithms are proposed in this thesis based on goal-oriented operations that achieve a balanced convergence and exploration behavior for a search in the design space applied in different strategies. Further, a component model is proposed that enables a seamless integration of design engineering tasks according to the related standards and application of optimization algorithms.:1 Introduction 17 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.3 Goals and Use of the Thesis . . . . . . . . . . . . . . . . . . . . . 21 1.4 Solution Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . 24 2 Design Creation for Building Automation Systems 25 2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.2 Engineering of Building Automation Systems . . . . . . . . . . . 29 2.3 Network Protocols of Building Automation Systems . . . . . . . 33 2.4 Existing Solutions for Design Creation . . . . . . . . . . . . . . . 34 2.5 The Device Interoperability Problem . . . . . . . . . . . . . . . . 37 2.6 Guidelines for Planning of Room Automation Systems . . . . . . 38 2.7 Quality Requirements on BAS . . . . . . . . . . . . . . . . . . . 41 2.8 Quality Requirements on Design . . . . . . . . . . . . . . . . . . 42 2.8.1 Quality Requirements Related to Project Planning . . . . 42 2.8.2 Quality Requirements Related to Project Implementation 43 2.9 Quality Requirements on Methods . . . . . . . . . . . . . . . . . 44 2.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3 The Design Creation Task 47 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2 System Design Composition Model . . . . . . . . . . . . . . . . . 49 3.2.1 Abstract and Detailed Design Model . . . . . . . . . . . . 49 3.2.2 Mapping Model . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3 Formulation of the Problem . . . . . . . . . . . . . . . . . . . . . 53 3.3.1 Problem properties . . . . . . . . . . . . . . . . . . . . . . 54 3.3.2 Requirements on Algorithms . . . . . . . . . . . . . . . . 56 3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4 Solution Methods for Design Generation and Optimization 59 4.1 Combinatorial Optimization . . . . . . . . . . . . . . . . . . . . . 59 4.2 Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.3 Examples for Metaheuristics . . . . . . . . . . . . . . . . . . . . . 62 4.3.1 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . 62 4.3.2 Tabu Search . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.3 Ant Colony Optimization . . . . . . . . . . . . . . . . . . 65 4.3.4 Evolutionary Computation . . . . . . . . . . . . . . . . . 66 4.4 Choice of the Solver Algorithm . . . . . . . . . . . . . . . . . . . 69 4.5 Specialized Methods for Diversity Preservation . . . . . . . . . . 70 4.6 Approaches for Real World Problems . . . . . . . . . . . . . . . . 71 4.6.1 Component-Based Mapping Problems . . . . . . . . . . . 71 4.6.2 Network Design Problems . . . . . . . . . . . . . . . . . . 73 4.6.3 Comparison of Solution Methods . . . . . . . . . . . . . . 74 4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5 Automated Creation of Optimized Designs 79 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.2 Design Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.3 Component Model . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.3.1 Presumptions . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.3.2 Integration of Component Model . . . . . . . . . . . . . . 87 5.4 Design Generation . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.4.1 Component Search . . . . . . . . . . . . . . . . . . . . . . 88 5.4.2 Generation Approaches . . . . . . . . . . . . . . . . . . . 100 5.5 Design Improvement . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.5.1 Problems and Requirements . . . . . . . . . . . . . . . . . 107 5.5.2 Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 5.5.3 Application Strategies . . . . . . . . . . . . . . . . . . . . 121 5.6 Realization of the Approach . . . . . . . . . . . . . . . . . . . . . 122 5.6.1 Objective Functions . . . . . . . . . . . . . . . . . . . . . 122 5.6.2 Individual Representation . . . . . . . . . . . . . . . . . . 123 5.7 Automated Design Creation For A Building . . . . . . . . . . . . 124 5.7.1 Room Spanning Control . . . . . . . . . . . . . . . . . . . 124 5.7.2 Flexible Rooms . . . . . . . . . . . . . . . . . . . . . . . . 125 5.7.3 Technology Spanning Designs . . . . . . . . . . . . . . . . 129 5.7.4 Preferences for Mapping of Function Blocks to Devices . . 132 5.8 Further Uses and Applicability of the Approach . . . . . . . . . . 133 5.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 6 Validation and Performance Analysis 137 6.1 Validation Method . . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.3 Example Abstract Designs and Performance Tests . . . . . . . . 139 6.3.1 Criteria for Choosing Example Abstract Designs . . . . . 139 6.3.2 Example Abstract Designs . . . . . . . . . . . . . . . . . . 140 6.3.3 Performance Tests . . . . . . . . . . . . . . . . . . . . . . 142 6.3.4 Population Size P - Analysis . . . . . . . . . . . . . . . . 151 6.3.5 Cross-Over Probability pC - Analysis . . . . . . . . . . . 157 6.3.6 Mutation Probability pM - Analysis . . . . . . . . . . . . 162 6.3.7 Discussion for Optimization Results and Example Designs 168 6.3.8 Resource Consumption . . . . . . . . . . . . . . . . . . . . 171 6.3.9 Parallelism . . . . . . . . . . . . . . . . . . . . . . . . . . 172 6.4 Optimization Framework . . . . . . . . . . . . . . . . . . . . . . . 172 6.5 Framework Design . . . . . . . . . . . . . . . . . . . . . . . . . . 174 6.5.1 Components and Interfaces . . . . . . . . . . . . . . . . . 174 6.5.2 Workflow Model . . . . . . . . . . . . . . . . . . . . . . . 177 6.5.3 Optimization Control By Graphical User Interface . . . . 180 6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 7 Conclusions 185 A Appendix of Designs 189 Bibliography 201 Index 211

No description available.