Laboratory Testing (LT) .... Informação a reter do capítulo ... METODOLOGIA DE CONSTRUÇÃO DAS BANCADAS DE SIMULAÇÃO ... Abordagem desenvolvida ... Dificuldades encontradas ... Info[r]

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of **the** array can be described by **the** inclusion of a distribu- tion of **the** coercive fields. This method has three important strenghtens; first **the** two assumptions are based on experi- mental evidence; second, it takes into account **the** intrinsic non-irreversible character of **the** hysteresis, and finally it gives an analytical description of **the** **loop**. **The** paper is organized as follows. **In** Sec. II, **the** **model** is presented and **the** hysteresis **loop** of a Ni nanowire array is obtained. Finally, conclusions are presented **in** Sec. III.

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This paper presents **the** integrated design and control of a buck boost converter (BBC). **In** **the** proposed methodology **the** design tool provides simultaneously **the** controller tuning and BBC design parameters **in** such a way that some closed- **loop** pre-specified static and dynamic behavior is obtained. This approach contrasts with **the** traditional methodology, where **the** design of BBC is performed without taking into ac- count its dynamical behavior. An optimization procedure is used to obtain **the** electronic components of **the** BBC and **the** tuning parameters of **the** controller, minimizing an objective function that considers **the** set of performance specifications. Although **the** methodology can be applied to any converter and any control strategy, **in** this particular case an ideal BBC and a Sliding **Model** Control (SMC) strategy are used. Some simulation results show **the** advantages and principally **the** flexibility that can be obtained with this approach.

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accelerated **the** rate of ‘‘0’’ ! ‘‘1’’ substitutions at **the** second site, or conversely **the** odds that a ‘‘0’’ at one site became replaced by a ‘‘1’’ with respect to **the** presence or absence of a ‘‘1’’ at **the** second site of a pair. **In** other words, if e ¼ 1, then **the** paired **model** was effectively equivalent to a **model** **in** which every site evolved independently. We simulated **the** evolution of binary sequences comprising 17 coevolving pairs of sites (i.e., f1, 2g, f3, 4g,..., f33, 34g) along **the** original neighbor-joining tree. Two hundred replicate alignments were generated **in** this fashion for a range of e parameter values. Each alignment was analyzed by **the** evolutionary- network method outlined above, i.e., by ﬁtting a **model** of independently evolving binary characters by maximum like- lihood, assigning substitution events to branches **in** **the** tree, and analyzing **the** distribution of substitutions as a Bayesian network using an MCMC analysis. We recorded **the** frequency that edges **in** **the** network with marginal posterior proba- bilities exceeding 0.95 recovered our predeﬁned paired interactions (true positives) or spurious ones (false positives). These results were contrasted with **the** rates of true and false positives obtained from using **the** Fisher exact test on **the** simulated binary sequence alignments (i.e., without correct- ing for **the** phylogeny). We chose **the** Fisher exact test as a representative example of pairwise association test statistics. Second, we simulated **the** evolution of nucleotide sequen- ces along **the** tree according to a more realistic codon substitution **model** whose parameters were estimated from **the** original alignment of V3 sequences. We randomly generated 100 replicate alignments with **the** same dimensions and characteristics (e.g., expected codon frequencies) as our observed V3 alignment by this method. Because **the** codon substitution **model** assumes that an alignment is a set of independently evolving codon sites [47], any signiﬁcant interactions between sites were false positives caused by founder effects **in** **the** phylogeny. We evaluated **the** false- positive rate for our method against **the** rate for a more conventional pairwise association test, **in** which we applied **the** Fisher exact test to every pairwise combination of codon positions **in** **the** simulated V3 **loop** sequences, enumerating consensus and nonconsensus residues to generate a 2 3 2 contingency table.

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As seen **in** Figure 5, **the** V-**model** divides each phase of **the** control system lifecycle into elementary activities and balances each specification and design activity (left-hand activities, top-down) with an adequate and targeted test and verification activity (right- hand activities, bottom-up). **The** main verification activities on **the** left-hand side are design reviews and different desktop analyses. At **the** bottom level, **the** actual product is built **in** different modules and further integrated bottom-up on **the** right-hand side. At each level of integration some form of testing, such as internal module and integration testing and **in** **the** end HIL testing and full-scale trials, is necessary to assure system integrity.

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Our analysis has used a particular physical situation to emphasize **the** general feature that conclusions which are spurious and incompatible with observations can be obtained from theoretical analysis, if limited modeling is utilized for description of situations which are beyond **the** range of validity of a **model**, and that more accurate modeling can put **the** analysis back **in** consonance with observations. Other similar situations can be found **in** different physical contexts. For instance, some analogy can be found with a mechanical situation, related to **the** inertia of a body. **The** current does not diverge **in** a perfectly conducting **loop** of wire under **the** effect of an induced eletromotive force, due to **the** self-inductance, **in** **the** same way as **the** velocity of a body does not

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Abstract - Two multi-objective optimization based tuning methods for **model** predictive control are proposed. Both methods consider **the** minimization of **the** error between **the** closed-**loop** response and an output reference trajectory as tuning goals. **The** first approach is based on **the** ranking of **the** outputs according to their importance to **the** plant operation and it is solved by a lexicographic optimization algorithm. **The** second method solves a compromise optimization problem. **The** former is designed for systems **in** which **the** number of inputs is equal to **the** number of outputs, while **the** latter can also be applied to non-square systems. **The** main contribution is an automated tuning framework based on a straightforward goal definition. **The** proposed methods are tested on a finite horizon **model** predictive controller **in** closed-**loop** with a 3x3 subsystem of **the** Shell Heavy Oil Fractionator benchmark system. **The** simulation results show that **the** methods proposed here can be a useful tool to reduce **the** commissioning time of **the** controller. **The** methods are compared to an existing multi-objective optimization based tuning approach. **The** computational time required to run **the** proposed tuning algorithms is considerably reduced when compared to **the** existing approach and, moreover, it does not need an a posteriori decision to select a solution from a set of Pareto optimal solutions.

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efficient technique for designing a robust controller can be alternatively used to design **the** robust controller for **the** system. Uncertainties **in** this approach are modeled as normalized co prime factors; this uncertainty **model** does not represent actual physical uncertainty, which usually is unknown **in** real problems. This technique requires only two specified weights, pre compensator and post compensator, for shaping **the** nominal plant so that **the** desired open **loop** shape is achieved. Fortunately, **the** selection of such weights is based on **the** concept of classical **loop** shaping which is a well known technique **in** controller design. By **the** reasons mentioned above, this technique is simpler and more intuitive than other robust control techniques. However, **the** controller designed by H ∞ **loop** shaping is still complicated and has high

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PLB mIL-3 predominantly uses a slipknotting mechanism (Figure 5A) [35]. However, we observe that **the** N-terminal PLBs with covalent loops $68 residues, predominantly use a plugging mechanism where **the** C-terminal inserts itself through its covalent **loop**. Hence, as **the** **loop** becomes larger/bigger, **the** mechanism switches from a slipknot- to a plugging mechanism. This is **the** first evidence, to date, that directly demonstrates a change **in** threading mechanism based on covalent **loop** length **in** structurally homologous proteins. Smaller covalent loops form more local native contacts with hairpins or turns because these structures allow for building up interaction surfaces that provide **the** driving force to pierce **the** lasso via a slipknotting mechanism. Therefore, slipknotting is more likely to occur when threading smaller covalent loops. Increasing **the** size of **the** covalent **loop** increases **the** ‘‘open’’ space inside **the** lasso. This leads to a larger number of possible conformations capable of threading a terminal and there are more possible entry encounters with **the** larger **loop** that will be productive. As **the** size of **the** **loop** becomes even bigger **the** terminal is no longer restricted by topological constraints. However **in** contrast to a polymer [17,36], which will perform a random motion without forming any native contacts, **the** protein terminal will make native contacts on **the** other side of **loop** and be stabilized, leading to a knot-like native conformation. We use a native-centric **model** although threading also involves many non- native interactions. It has indeed been shown that including non- native interactions **in** **the** **model** can facilitate knotting [37] and

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Different from **the** studies carried out **in** de Oliveira et al. (2010b), **in** this work **the** methodology is also applied to tune AVRs. After generating parameter ranges just for PSSs, **the** approach was employed to generate parameter ranges just for AVRs (with nominal PSSs **in** operation, but with their param- eters unchanged) and also for AVRs and PSSs, simultane- ously. This evaluation took into account a minimum damp- ing ratio of 5% and **the** same operating conditions employed **in** **the** previous test. Since **the** AVR gains are usually higher than **the** PSS gains, **the** calculation of **the** parameter ranges for **the** AVR consider gain deviations of 20 p.u. for **the** al- gorithm iterations (i.e., ∆ρ=20 p.u.). **The** nominal parame- ters of **the** AVRs employed **in** **the** **model** of test system 1 are Ke=200 p.u. and Te=0.01 s. Fig. 12 presents **the** parame- ter box determined for **the** simultaneous tuning of **the** AVRs placed **in** generators G1 and G3. **The** parameter box gener- ated for **the** simultaneous tuning of **the** AVR and PSS placed **in** generator G3 is presented **in** Fig. 13. Fig. 14 shows **the** eigenvalues related to **the** closed-**loop** **model** of test system 1, **in** **the** base case operating condition, after **the** retuning of **the** AVR and PSS (different values of static gains belonging to **the** parameter box presented **in** Fig. 13 were considered).

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which is due to **the** fact that **the** a priori information on **the** soil texture, taken from **the** FAO map, is **in** considerable agreement with **the** field measurements of grain size. **In** case that **the** FAO information would stronger deviate, much higher uncertainties **in** **the** **model** simulations can be expected. **The** soil moisture rms error as well as **the** **model** efficiency for these open **loop** simulations are given **in** Table 2 for **the** upper (5 cm) and

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On Example 8, **the** algorithm converged to **the** same **model**, irrespective of initial **model**. This is good news, because an iterative algorithm should preferably con- verge to **the** optimal solution, irrespective of initial val- ues. This is investigated further **in** Section 8 of exam- ples, see e.g., Tables 4 and 5. However, it is well known as described **in** Ljung (1999) on p.338 that e.g. **the** it- erative prediction error method only is guaranteed to converge to a local solution and to find **the** global solu- tion one have to start **the** iterations at different feasible initial values. On our MIMO Example 8.2 we observed that local solutions of **the** bootstrap subspace identifi- cation exists, and starting with a zero **model** for this example resulted **in** a biased **model**. However, starting with an initial **model** from **the** higher order ARX strat- egy resulted **in** a unbiased **model** with approximately as optimal parameter estimates as as **the** corresponding PEM estimates. This is further documented **in** Exam- ple 8.2.

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Subbotko, Methods of distribution of tool at units based on TM software of Guhring, Production Engineering Wroc ł aw University of Technology,(2006) 273- 280 (in Polish).. Bocheński, C[r]

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I think we can answer this question **in** **the** positive: Yes, He can, because He is **the** most perfect being and His omnipotence is absolutely unlimited. A very important premise underlying **the** answer to **the** last question is that **the** risk is not so great, or even that it is very small. It is so because **the** nature and mechanism of **the** created world ensure with a very high proba- bility that all purposes intended by God will be attained without his causal action **in** **the** processes occurring **in** **the** world. **The** emergence of life **in** **the** universe is almost inevitable, because **the** universe is large and old enough, and biochemical mechanisms are very effective. **The** emergence of sentient beings was also almost inevitable because of longstanding and countless mutations and adaptations of living organisms to their environment. All this was very probable and hence **in** a sense necessary (inevitable). **The** great advantage of **the** non-deterministic world is its own creativity, which is possible because of **the** chance events happening **in** a way restricted only by **the** laws of nature. Thus, if one evolutionary path fails another one is opened. Perhaps a mutation suitable for **the** growth and development of a given species happened by chance and enabled it to survive **in** hard con- ditions and further develop. Elasticity and redundancy are very typical for **the** world of chance, but because of these properties, this world has a large number of possibilities and abilities to develop and regenerate after various natural catastrophes (Łukasiewicz 2006).

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