When considering the addition of a mobile presentation channel to an existing web-based application, project managers should know how the mobile channel|s characteristics will impact the user experience and the cost of using the application, even before development begins. The PETTICOAT (Performance Tuning and cost discovery of mobile web-based Applications) approach presented here provides decision-makers with indicators on the economical feasibility of mobile channel development. In a nutshell, it involves analysing interaction patterns on the existing stationary channel, identifying key business processes among them, measuring the time and data volume incurred in their execution, and then simulating how the same interaction patterns would run when subjected to the frame conditions of a mobile channel. As a result of the simulation, we then gain time and volume projections for those interaction patterns that allow us to estimate the costs incurred by executing certain business processes on different mobile channels.

Multi-objective decision-making is critical for everyday tasks and engineering problems. Finding the perfect trade-off to maximize all the solution's criteria requires a considerable amount of experience or the availability of a significant number of resources. This makes these decisions difficult to achieve for expensive problems such as engineering. Most of the time, to solve such expensive problems, we are limited by time, resources, and available expertise. Therefore, it is desirable to simplify or approximate the problem when possible before solving it. The state-of-the-art approach for simplification is model-based or surrogate-based optimization. These approaches use approximation models of the real problem, which are cheaper to evaluate. These models, in essence, are simplified hypotheses of cause-effect relationships, and they replace high estimates with cheap approximations. In this thesis, we investigate surrogate models as wrappers for the real problem and apply \gls{moea} to find Pareto optimal decisions. The core idea of surrogate models is the combination and stacking of several models that each describe an independent objective. When combined, these independent models describe the multi-objective space and optimize this space as a single surrogate hypothesis - the surrogate compositional model. The combination of multiple models gives the potential to approximate more complicated problems and stacking of valid surrogate hypotheses speeds-up convergence. Consequently, a better result is obtained at lower costs. We combine several possible surrogate variants and use those that pass validation. After recombination of valid single objective surrogates to a multi-objective surrogate hypothesis, several instances of \gls{moea}s provide several Pareto front approximations. The modular structure of implementation allows us to avoid a static sampling plan and use self-adaptable models in a customizable portfolio. In numerous case studies, our methodology finds comparable solutions to standard NSGA2 using considerably fewer evaluations. We recommend the present approach for parameter tuning of expensive black-box functions.:1 Introduction 1.1 Motivation 1.2 Objectives 1.3 Research questions 1.4 Results overview 2 Background 2.1 Parameter tuning 2.2 Multi-objective optimization 2.2.1 Metrics for multi-objective solution 2.2.2 Solving methods 2.3 Surrogate optimization 2.3.1 Domain-specific problem 2.3.2 Initial sampling set 2.4 Discussion 3 Related Work 3.1 Comparison criteria 3.2 Platforms and frameworks 3.3 Model-based multi-objective algorithms 3.4 Scope of work 4 Compositional Surrogate 4.1 Combinations of surrogate models 4.1.1 Compositional Surrogate Model [RQ1] 4.1.2 Surrogate model portfolio [RQ2] 4.2 Sampling plan [RQ3] 4.2.1 Surrogate Validation 4.3 Discussion 5 Implementation 5.1 Compositional surrogate 5.2 Optimization orchestrator 6 Evaluation 6.1 Experimental setup 6.1.1 Optimization problems 6.1.2 Optimization search 6.1.3 Surrogate portfolio 6.1.4 Benchmark baseline 6.2 Benchmark 1: Portfolio with compositional surrogates. Dynamic sampling plan 6.3 Benchmark 2: Inner parameters 6.3.1 TutorM parameters 6.3.2 Sampling plan size 6.4 Benchmark 3: Scalability of surrogate models 6.5 Discussion of results 7 Conclusion 8 Future Work A Appendix A.1 Benchmark results on ZDT DTLZ, WFG problems

Spårledning används inom järnvägen för att lokalisera var tåg befinner sig. Detta examensarbete behandlar en modell av tonfrekvensspårledningar från Alstom. Teoridelen behandlar de båda modellerna DTC – 921 och DTC- 24 medan uträkningarna enbart behandlar den tidigare. Tonfrekvensspårledningar separeras med elektroniska skarvar genom förbindningar i spåret. Den förbindning som examensarbetet undersökt är av typ S-förbindning som idag består av en kopparkabel. Arbetet gick ut på att undersöka hur funktionen är uppbyggd och hur den skulle påverkas vid ett byte av material då koppar är stöldbegärligt. Undersökningar har gjorts på tre olika alternativ: aluminium, järn och safecable från safetrack.  Genom att studera S-förbindningens funktion och hur den agerar med tillhörande tuningbox har materialets eventuella påverkan utvärderats. Med teoretiska uträkningar har parametrarna räknats ut för de olika materialen. För att bekräfta funktionen har simuleringar i Pspice utförts. / Track circuits are used to detect where the trains are along the railway. This report contains a study of two models of audio frequency track circuit from Alstom called DTC – 921 and DTC - 24. The calculations made in the result section are made on only DTC-921. The different frequencies between two track circuits are divided by an S-bond. The S-bond is made of cupper which is a material that is theft-prone. The purpose of the project was to investigate the functionality of the S-bond and how  a cable with a different kind of material would affect the function. Three different kinds of materials have been investigated: aluminum, iron and safecable from safetrack.   By studying the function of the S-bond and how it interacts with the tuning box the affect off the different materials have been evaluated. The parameters of the materials have been calculated and the function has been simulated in Pspice.

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Tris(3,5-dimethylpyrazolyl)arsane (1) is introduced as an low-cost and convenient to handle arsenic precursor for the straight forward synthesis of InAs quantum dots (QDs). Transamination of 1 with the solvent oleylamine (OLAH) gives trioleylarsane (As(OLA)3) which in the presence of the reducing agents DIBAL-H or P(OLA)3 yields InAs QDs via a typical hot injection approach. The size of the obtained InAs core QDs are tuned by varying the reaction time, the amount of the applied reducing agent, or even more effectively by changing the indium and/or zinc halide precursors, InX3 and ZnX2 (Cl, Br, or I). Passivation of the resulting InAs particles with a protective ZnS or ZnSe shell results in improved photoluminescence (PL) of the core/shell QDs covering a spectral range between 600–1150 nm.

Today's trend in microprocessor design is increasing the number of execution cores within one single chip. Increasing the processor's clock speed reached its limit with growing power consumption. This trend brings new opportunities to software developers, as they can take advantage of real multithreading in their applications. But a lot of new problems to solve appear with threading compared to sequential programming. With proper design, threading can enhance performance by making better use of hardware resources. However, the improper use of threading can lead to performance degradation, unpredictible behavior, or error conditions that are difficult to solve. For this reason Intel developed a suite of tools, that can help software developers to analyze performance and detect coding errors in thread interactions. This thesis focuses on the examination of ways that this tools can be used in multithreaded application development.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18942号 / 工博第3984号 / 新制||工||1614(附属図書館) / 31893 / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 松野 文俊, 教授 椹木 哲夫, 教授 藤本 健治 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21108号 / 工博第4472号 / 新制||工||1695(附属図書館) / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 篠原 真毅, 教授 山川 宏, 教授 木本 恒暢 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Achieving optimal performance of a database can be crucial for many businesses, and tuning its configuration parameters is a necessary step in this process. Many existing tuning methods involve complex machine learning algorithms and require large amounts of historical data from the system being tuned. However, training machine learning models can be problematic if a considerable amount of computational resources and data storage is required. This paper investigates the possibility of using less complex search algorithms or evolutionary algorithms to tune database configuration parameters, and presents a framework that employs Hill Climbing and Particle Swarm Optimization. The performance of the algorithms are tested on a PostgreSQL database using read-only workloads. Particle Swarm Optimization displayed the largest improvement in query response time, improving it by 26.09% compared to using the configuration parameters' default values. Given the improvement shown by Particle Swarm Optimization, evolutionary algorithms may be promising in the field of database tuning.