• Physically, the outlet temperature of water depends of its inlet temperature, water feed and steam feed. Thus, the value of the outlet temperature has a direct correlation with all used measurements. Vijaysai et al. [58] and Luo et al. [60] define that the main limitation of PCA is that it assumes normality and independence of the samples. Ku et al. [43] propose the **dynamic** PCA algorithm for overcoming the shortcomings of conventional PCA.

121 Lee mas

The authors in [1] and [2] present approaches for real time **dynamic** vulnerability assessment for power systems and detection of islanding conditions. In [3] and [4], PCA is used to analyze the steady state operational power grid data and expose some correlations. In [5], the researchers describe an algorithm for transformer differential protection based on pattern recognition of the differential current. In summary the potential of PCA for data reduction and feature extraction for power systems is high.

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This paper presents a methodology for the reduction of the training space based on the **analysis** of the variation of the linear components of the acoustic features. The methodology is applied to the automatic detection of voice disorders by means of stochastic **dynamic** models. The acoustic features used to model the speech are: MFCC, HNR, GNE, NNE and the energy envelopes. The feature extraction is carried out by means of PCA, and classification is done using discrete and continuous HMMs. The results showed a direct relationship between the **principal** directions (feature weights) and the classification performance. The **dynamic** feature **analysis** by means of PCA reduces the dimension of the original feature space while the topological complexity of the **dynamic** classifier remains unchanged. The experiments were tested with Kay Elemetrics (DB1) and UPM (DB2) databases. Results showed 91% of accuracy with 30% of computational cost reduction for DB1.

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Usually, scheduling/mapping algorithms are used to obtain the best assignment of the processes that make up an application to the processors of the architecture in which it will be run. In this paper, the DCS_AMTHA (**Dynamic** Concurrent Scheduling) algorithm to carry out the scheduling of multiple parallel applications on heterogeneously distributed architectures (cluster) is defined. This algorithm is based on AMTHA and the goal is to optimize the efficiency achieved by the whole system.

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In order to determine milling parameters, a procedure similar to that presented in [33] was followed. Kt and kr were considered to be potential functions of chip thickness along the cu[r]

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This paper shows a feature extraction method for electrocardiographic signals (ECG) based on **dynamic** programming algorithms. Specifically, we applied local alignment technique for recognition of template in continuous ECG signals. First, we encoded the signal to characters based on the sign and magnitude of first derivative, then we applied local alignment algorithm to search for a complex PQRST template in target continuous ECG signal. Finally, we arrange the data for direct measurement of morphological features in all PQRST segment detected. To validate these algorithms, we contrasted them with conventional **analysis** by measuring QT segments in the Massachusetts Institute of Technology (MIT) data base. We obtained processing time at least 100 times lower than those obtained via conventional manual **analysis** and error rates in QT measurement below 5%. The automated massive **analysis** of ECG presented in this work is suitable for post- processing methods like data mining, classification, and assisted diagnosis of cardiac pathologies.

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5 Lee mas

A large variety of complex systems can be analyzed by constructing a model that relies on some network structure [1–4]. The model may be dynamical, meaning that the values of some (state) variables do change with time and, depending on the nature of such variables, we can have different types of network models. The first type corresponds to **dynamic** graphs that follow evolution laws defined explicitly on the network [5–8]; the second type gathers dynamical systems where the state variables are defined on a network [9,10]; finally, the third type refers to co-evolution models that combine evolving networks and dynamical systems. In the first and third type, the underlying network structure changes with time, defining a time-varying or evolving network [11,12]. In the present work, we first characterize the basic features of some simple models of evolving networks whose evolution does not depend on network structure; the time evolution of these features serves as a reference baseline signature of the behavior of simple models. Then, a model that makes use of network structure is proposed to reflect some real network characteristics. The **analysis** of this model shows several regimes that indicate a sophisticated behavior; for some regime, the network reaches a high clustering coefficient/link density ratio [13] (when compared to the ratio values of baseline signatures), a common feature in many real networks.

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When spike energy increases, it usually means that bearing, gear, or other **component** problems are developing. It also means that acceleration and velocity trends should be more closely observed for changes; if acceleration readings exceed their allowable vibration limits but velocity readings are still acceptable, vibration spectrum **analysis** should be performed to confirm the problem. Repairs should be scheduled for a convenient future time.

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In an attempt to solve these problems, some SBS have reached an agreement on the use of a limited vocabulary. A way to use this limited vocabulary is applied in the SBS Delicious: when a user starts introducing a tag, the system shows her those tags that start in the same way and that have been introduced previously, thereby allowing a direct selection. However, the use of a limited vocabulary as well as the suggestion of tags previously annotated in order to keep uniformity have also their drawbacks, because it happens that occasionally the same tag is used with different meanings and the use of synonyms and acronyms makes it more unclear. 3.2 **Principal** **Component** **Analysis** (PCA)

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In Figure 3 we show the results for the covariance matrix of monthly means of the accumulated precipitation and temperature of 173 stations. We consider only the first two **principal** components because they explain 85 per cent of the variance. The **analysis** indicates an interesting distribution for the first **principal** **component** (PC1), showing an extraordinary precipitation period from June to September (Fig. 3c). Further, an inspection of the variability of the monthly accumulated precipitation, observed in PC1 axis, reveals that two **principal** seasons can be distinguished: a relatively dry period from October to May and a rainy season from June to September. The precipitation variability included in PC2 shows a dry period in early summer and a light to moderate rainfall in the rest of the year (Fig. 3d). This mid-year drought in early summer is associated with the known phenomenon called canícula/(dog days) (Magaña et al., 1999; Vázquez, 2000; Cavazos et al., 2002). In late fall and early winter, PC2 reflects the influence of hurricanes and events of El Niño Southern Oscillation (ENSO) (Cavazos and Hastenrath, 1990). The PC1 for temperature shows high variability from August to November with a maximum in September (Fig. 3a). It can also be concluded that, climatically, the most stable months are March and April. The variability observed in PC2 for temperature can be explained by cold fronts moving into the area between late summer and early winter (Fig. 3b).

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by inspection patterns, trends, clusters, etc. in the objects. **Principal** components **analysis** (PCA) is a technique, extremely useful to summarize all the information contained in the X-matrix and put it in a form understandable by humans. The PCA works by decomposing the X-matrix as the product of two smaller matrices P and T. The loading matrix P with information about the variables contains a few vectors, the so-called **principal** components (PCs), which are obtained as linear combinations of original X-variables. The score matrix T, with information about the objects, is such that every object is described in terms of the projections onto PCs, instead of the original variables: X = TP’ + E where ’ denotes transpose matrix. The information not contained in the matrices remains as unexplained X-variance in a residual matrix E. Every PC i is a new co-ordinate expressed as a linear combination of the old features x j : PC i = S j b ij x j . The new co-ordinates PC i are called scores or factors while coefficients b ij are called loadings. The scores are ordered according to the information content with regard to total variance among all objects. The score-score plots show the positions of compounds in the new co-ordinate system, while loading-loading plots show the position of features that represent compounds in the new co-ordination. The PCs present two interesting properties. (1) They are extracted in decaying order of importance. The first PC F 1 always contains more information than the second F 2 , F 2 more than the third F 3 , etc. (2) Every PC is orthogonal to one another. There is no correlation between the information contained in different PCs. A PCA was performed for yams. The importance of PCA factors F 1–14 for properties is collected in Table 2. In particular the use of only the first factor F 1 explains 36% of the variance (64% of the error), the combined application of the first two factors F 1/2 accounts for 64% of variance (36% of error), the utilization of the first three factors F 1–3 rationalizes 80% of variance (20% of error), etc.

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Proximate composition of six food spices commonly used in South-East Nigeria are classified by **principal** **component** **analysis** (PCAs) of constituents and spices cluster **analysis** (CAs). Samples are grouped into two classes. Compositional PCA and spice CA permit classificating them and group the similar ones. The first PCA axis explains 61% of the variance; first two, 93%; first three, 99; etc. Different behaviour of species depends on ash, fibre, fat, moisture, etc. Macronutrients (protein, carbohydrate, fat) contents are adequate. Carbohydrate amounts are high. Fat quantities are moderate. Fat is closer to protein than to carbohydrate.

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12 Lee mas

In summary, most of assumptions adopted to solve the consolidation problem by different authors are not representative of what is observed in the field. Although some assumptions such as a variable hydraulic conductivity and compressibility, and the consideration of large strains and self‐ weight forces, etc. have been included throughout the evolution of the consolidation formulation, other assumptions have not accurately been taken into account. The complexity is still manifolds; for instance, constitutive equations relating effective stress and void ratio with a constant compressibility coefficient are not representative of the real behaviour of hydraulic fills. This constitutive behaviour is sensitive to several phenomenon such as the multidimensional consolidation (lateral deformation and dissipation of pore pressure), pore pressure increment due to dilatancy, the **dynamic** affection, or time dependent effects (i.e., secondary consolidation due to rheology phenomena). Accordingly, the newest and most advanced constitutive models require introducing accurately these points. This will be one of the main lines of investigation developed in this study.

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We can see in Table 1 that technology differences in the Spanish regions are skilled-labor augmenting, both in relatively and absolutely senses. It is relatively, because richer regions use skilled worker more efficiently than poor countries (regression coefficients are positives for skilled workers) and it is absolutely, because they use unskilled worker less efficiently than poor zones (regression coefficients are negative for unskilled workers). Our reference case (an skilled worker is one who has completed high school studies and the elasticity of substitution between skilled and unskilled labor is 1.7) is statistically significant. From now on, we use our reference case in subsequent **analysis**.

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Abstract: In this paper it is presented the conceptual approach of a model for representing short-term **dynamic** **analysis** of the agents’ behavior in the electricity market. The main purpose is to explore the interaction among generation companies through the offers systematically sent to a day-ahead single-node uniform-price market. The conceptual framework is aimed to shed light on two main questions: i) How the results of medium-term models (i.e., market share ob- jectives, hydro scheduling, system marginal price) are internalized into daily offers or, alterna- tively, how to reach medium-term objectives by means of short-term offers and ii) How to ana- lyze in detail the market dynamics in case of severe perturbations of agents’ behavior (i.e., price wars).

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system are partitioned into those that exhibit long wavelength behaviour and those that exhibit short wavelength local behaviour (and possess signicant **dynamic** uncertainty). Recently, Shorter and Langley [2005a] developed a general method for coupling FE and SEA based on wave concepts, and implemented in the commercial software VAOne [ESI, 2011]. Zhao and Vlahopoulos [2000] propose another hybrid approach by using a deterministic FE model for the global long wavelength components and an energy nite element model for the local short wavelength components. The global FEM and local EFEM equations, together with the coupling interface equations, are solved simultaneously through an iterative but computationally ecient process. So far, this approach has only been validated for co-linear beam networks. Other authors [Grice and Pinnington, 2002; Hong et al., 2006; Ji et al., 2006] also developed hybrid methods in a series of papers in which the short wavelength components are described statistically by eective impedances applies to the wavelength components. The approaches dier in the way the eective impedance is computed and in the way the response of the short wavelength components is recovered.

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The following work uses the **dynamic** temporal sequence alignment to adjust or contract the different registered segments of the PPG signal in order to determine the maximum value of each of the waves that make up the registered signal and thereby obtain the HRV this way. The method makes a temporary (local) signal alignment of the PPG, in order to temporarily file the signal data into an array belonging to the waveform of each registered signal [34] [35]. In this way it could recover the timing with the maximum amplitude values of the pulse waves without using other reference signals.

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combination of PCs, we have chosen to normalize each light curve independently to have a mean of 0 and scatter of 1. These aligned, normalized input light curves can be seen in Figure 3. We carry out PCA by utilizing singular value decomposition, adopted from the PCA module of scikit-learn ( Pedregosa et al. 2011 ) . Figure 4 shows the ﬁ rst six PCs, according to our decomposition. As we have chosen not to normalize in each phase point, the ﬁ rst PC contains the average light-curve shape of the normalized light curves. Including further components to describe the light curves modi ﬁ es this average shape, and this can be easily understood in the context of the individual light curves, for PCs of low order: the second **component** can make the bump at the end of the rising branch ( around phase 0.15 ) more or less pronounced, while the third **component** is important to reproduce the double-peaked light curves displayed by some of the variables.

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Fruits from Nigeria are classified by **principal** **component** analyses (PCAs) of proximates and minerals, and plants cluster analyses (CAs), which agree. Samples group into three classes. Compositional PCA and fruit CA allow classification and concur. The first axis explains 39%, the first two, 59%, the first three, 73% variance. Moisture and K contents are high; ash and carbohydrate, low. Fruit behaviour depends on ash, fibre and K. Most nutritional constituents are grouped into the same class.

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