Top PDF A general model of parameterized OWA aggregation with given orness level

A general model of parameterized OWA aggregation with given orness level

A general model of parameterized OWA aggregation with given orness level

The paper proposes a general model to obtain the OWA operator with orness as its control parameter. This general model includes the maximum entropy OWA operator and minimum variance OWA operator as spe- cial cases. Some properties of its solution are discussed. The solution equivalence to the minimax problem are proved, which is also a generalization of the solution equivalence for the minimum variance and minimax dis- parity problems. Then, these results are extended to the RIM quantifier case, which corresponds to the OWA operator in continuous form. A general model to obtain the parameterized RIM quantifiers of given orness level is proposed, with the property discussions and the solution equivalence proof to the corresponding mini- max problem. With the analytical optimal solution expression of these two kinds problems, the relationship between the OWA operator vector elements or the shape of the RIM quantifier membership function can be observed intuitively. We can not only use the OWA operator or RIM quantifier to get aggregation results con- sistent to the preference information (orness level), but also can make the obtained optimal OWA operator or
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The orness measures for two compound quasi-arithmetic mean aggregation operators

The orness measures for two compound quasi-arithmetic mean aggregation operators

Then, the paper proposes the orness measures for two compound forms of the quasi-arithmetic mean: the quasi-OWA operator and the Bajraktarevic´ mean. With the generating function technique, some properties of these orness measures are discussed. Two kinds of parameterized quasi-OWA operators and Bajraktarevic´ means with exponential functions and power functions are proposed, respectively. The exponential function quasi-OWA operator and Bajraktarevic´ mean are sym- metrical for their parameters. They are shift invariant. One can easily get its complementary part with given orness level. The power function quasi-OWA operator can be seen as the extension of the generalized OWA operator, while the power func- tion Bajraktarevic´ mean can be seen as the extension of the commonly used power root means and the weighted function average method. They are ratio invariant. The relationships and some extensions on the orness expressions of these aggre- gation operators are also discussed.
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On the properties of equidifferent OWA operator

On the properties of equidifferent OWA operator

Getting OWA weights under given orness level is an active topic in the OWA operator research. The paper proposes a series of weights generating methods in equidifferent forms. Similar to the geometric (maximum entropy) OWA operator, we propose a parameterized OWA operator called equidifferent OWA operator, which consist the adjacent weighes with a common difference. The maximum spread equidifferent OWA (MSEOWA) operator is equivalent to the minimum variance OWA operator, but is more computational efficient. Some properties associated with the orness level are discussed. One of them is that the aggregation value for any elements set is always increasing with the orness level, which can used as a parameterized aggregation method with orness as its control parameter. These properties similar to that of the geometric (maximum entropy) OWA operator, which can also be seen as the discrete case of equidifferent RIM (regular increasing monotone) quan- tifiers. The general forms of equidifferent OWA operator are proposed, and the weights generating methods are also extended in a similar way.
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Parameterized OWA operator weights: An extreme point approach

Parameterized OWA operator weights: An extreme point approach

Yager’s [1] ordered weighted averaging (OWA) operator for aggregating multiple input arguments has been a focus of re- search in a variety of fields such as decision science and computer science ever since its introduction. From a decision science perspective, the OWA method presents not only an unifying and generalizing formula for uncertain decision-making prob- lems, but also a multi-criteria aggregation technique capable of reflecting a decision-maker’s attitudinal character. In partic- ular, semantic meaning such as orness (or andness) provides an innovative method for the logical aggregation of multiple arguments in multi-criteria problems. The OWA operator is the inner product of an ordered input (or argument) vector and a weighting vector [1] . When the (ordered) argument vector is given, the OWA operator depends on the weighting vec- tor. In other words, the weighting vector plays a key role in the aggregation process. Thus, one of the main concerns when using OWA aggregation is how to generate OWA operator weights. Thus, it is possible to model different kinds of relation- ships among the criteria by appropriately selecting the weighting vector. Previous studies have advocated numerous weights generation and aggregation methods, including the programming-based approach [2–9] , the experience- or learning-based approach [10–12] , the analytic formula-based approach [13,14] , and the quantifier-guided approach [15–23] (please see pa- pers [24,25] for comprehensive reviews on these methods).
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Orness and parameterized RIM quantifier aggregation with OWA operators: A summary

Orness and parameterized RIM quantifier aggregation with OWA operators: A summary

used method is to obtain the desired OWA operator under a given orness level [12–15,29,35,58] , which is usu- ally formulated as a constrained optimization problem. The objective to be optimized can be the (Shannon) entropy [12,14,29,35] , the variance [15,24] , the maximum dispersion [2,43] , the (generalized) Re´nyi entropy [31] , the total square deviation [41] , or even the preemptive goal programming [42] . O’Hagan [35] suggested the problem of constraint nonlinear programming with a maximum entropy procedure, the solution is called a MEOWA (Maximum Entropy OWA) operator. Filev and Yager [12] further proposed a method to generate MEOWA weighting vector by an immediate parameter. Fulle´r and Majlender [14] transformed the maximum entropy model into a polynomial equation, which can be solved analytically. Liu and Chen [29] proposed gen- eral forms of the MEOWA operator with a parametric geometric approach, and discussed its aggregation properties. Apart from maximum entropy OWA operator, Fulle´r and Majlender [15] suggested the minimal variability OWA operator problem in quadratic programming, and proposed an analytical method of it. Liu [24] gave this OWA operator generating method with the equidifferent OWA operator, and discussed its properties. A closely related work is that of Wang and Parkan [43] . They proposed a linear programming model with minimax disparity approach to get the OWA operator under desired orness level. The solution equivalence of the minimum variance problem and the minimax disparity problem was proved theoretically by Liu recently [28] . Majlender [31] proposed a maximum Re´nyi entropy OWA operator problem with expo- nential objective function, which can include the maximum entropy and minimum variance problem as special cases, and an analytical solution was proposed.
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Banned Items Recognition by OWA Operator

Banned Items Recognition by OWA Operator

The fuzzy rule-based classification system generates too many rules for high dimension problems. It is often said that the numeral of fuzzy if-then rules exponentially increases as the number of features increases. [2]For this purpose, only a small number of features are selected for constructing a fuzzy classifier, which decreases its accuracy. To solve this problem, we present a multi-level fuzzy classifier consists of several small fuzzy classifiers with a small number of features, which not only improve the performance of fuzzy classifier but also solve the problem of high dimension.
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Disaggregation of results from a detailed general equilibrium model of the US to the State level

Disaggregation of results from a detailed general equilibrium model of the US to the State level

The LMPST approach for estimating SHIN was adapted for CGE modelling by Dixon, Parmenter and Sutton (1978) who disaggregated results from a CGE model to the six Australian States. In Australia, LMPST’s distinction between local and national goods is tenable for disaggregation to the state level. This is because most of Australia’s economic activity takes place in the capital cities of the states and these cities are far from state borders. Thus, there are many goods that are barely traded across state borders and can therefore be classified as local without too much loss of realism. At the same time, there are many goods for which the state distribution of production seems to be independent of the state distribution of absorption. It is reasonable to classify these goods as national.
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Emotion Model Implementation for Parameterized Facial Animation in Human-Robot-Interaction

Emotion Model Implementation for Parameterized Facial Animation in Human-Robot-Interaction

Abstract: In recent years robotic systems have matured enough to perform simple home or office tasks, guide visitors in environments such as museums or stores and aid people in their daily life. To make the interaction with service and even industrial robots as fast and intuitive as possible, researchers strive to create transparent interfaces close to human-human interaction. As facial expressions play a central role in human-human communication, robot faces were implemented with varying degrees of human-likeness and expressiveness. We propose an emotion model to parameterize a screen based facial animation via inter-process communication. A software will animate transitions and add additional animations to make a digital face appear “alive” and equip a robotic system with a virtual face. The result will be an inviting appearance to motivate potential users to seek interaction with the robot.
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Unbalanced interval-valued OWA operators

Unbalanced interval-valued OWA operators

Abstract In this work, we introduce a new class of functions defined on the interval-valued setting. These functions extend classical OWA operators but allow for different weighting vectors to handle the lower bounds and the upper bounds of the considered intervals. As a consequence, the resulting functions need not be an interval-valued aggregation function, so we study, in the case of the lexicographical order, when these operators give an interval as output and are monotone. We also discuss an illustrative example on a decision making problem in order to show the usefulness of our developments.
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A Method for Decision Making with the OWA Operator

A Method for Decision Making with the OWA Operator

Abstract. A new method for decision making that uses the ordered weighted averaging (OWA) operator in the aggregation of the information is presented. It is used a concept that it is known in the literature as the index of maximum and minimum level (IMAM). This index is based on distance measures and other techniques that are useful for decision making. By using the OWA operator in the IMAM, we form a new aggregation operator that we call the ordered weighted averaging index of maximum and minimum level (OWAIMAM) operator. The main advantage is that it provides a parameterized family of aggregation operators between the minimum and the maximum and a wide range of special cases. Then, the decision maker may take decisions according to his degree of optimism and considering ideals in the decision process. A further extension of this approach is presented by using hybrid averages and Choquet integrals. We also develop an application of the new approach in a multi-person decision-making problem regarding the selection of strategies.
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River dune predictions : comparison between a parameterized dune model and a cellular automaton dune model

River dune predictions : comparison between a parameterized dune model and a cellular automaton dune model

The typical asymmetric form of river dunes is important for the determination of hydraulic roughness of the river bed. Asymmetric dunes generated in a steady, uniform and unidirectional flow induce implications to the flow. Flow resistance, bed shear stress and sediment transport are affected by the shape of these dunes. Turbulence over such dunes is dominated by the flow separation zone and very important for dune formation (Best, 2005). Flow close to the bed follows the bed profile. However, when river dunes have an asymmetric form with steep lee sides, the flow will separate from this profile at the dune crest because the longitudinal flow velocity is larger than the vertical velocity caused by gravitational force. The flow separation results in rotational flow behind the dune crest with variations in the pressure gradient, as presented in figure 1. The rotational flow causes energy loss, a turbulent flow regime and a reverse flow near the bed that result in a zero net discharge through a vertical cross section between the bed and the separation zone (Paarlberg et al., 2007). This leads to a sudden increase in the hydraulic roughness and therefore to an increase of the water level.
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Promoting Access to the General Curriculum for Students with Mental Retardation: A Multi-Level Model

Promoting Access to the General Curriculum for Students with Mental Retardation: A Multi-Level Model

curriculum) for students who are the same age and grade level as the student for whom the IEP is being designed, as well as with information about unique student learning needs (based on input from multiple stakeholders and assessment sources). This model is detailed in Wehmeyer, Lattin, et al. (2001), so will only be summarized in this article. When considering a student’s formal curriculum, it may be that some students can progress on portions of the general curriculum without accommodations or curriculum modifications and as such that portion of the general curriculum will be the “most appropriate” formal curriculum. It is likely, however, that most students with mental retardation or developmental disabilities will need some accommodations or modifications. To achieve that, the IEP team is first encouraged to consider how assistive technology can accommodate for student limitations and can enable the student to progress without curriculum modifications. Once assistive technology has been considered, teams consider three levels of curriculum modifications. The first is curriculum adaptation, which refers to efforts to adapt the curriculum’s presentation and representation or the student’s engagement with the curriculum (as discussed subsequently). A second level of modification is curriculum augmentation, where additional content is added to the curriculum to enable students to progress. Such efforts typically include teaching students additional ‘learning-to-learn’ or self- regulation strategies that, in turn, enable students to progress more effectively in the curriculum. Neither of these levels of curriculum modification changes the general curriculum content. The third level, curriculum alteration, does change the general curriculum to add content specific to students needs, which might include traditional functional skills or other needed skills not in the general curriculum. This also, presumably, necessitates the elimination of content in the general curriculum. For many students with mental retardation, the third level of curriculum modification (e.g., alternative curriculum) is where planning currently begins, but if students are to maximally benefit from and progress in the general curriculum, IEP teams need to consider accommodations and curriculum adaptations and augmentations before considering alternative curricula. It is also evident that if the general curriculum is broad enough to cover functional areas, that will limit the need to move to an alternative curriculum.
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A general framework for using aggregation in visual exploration of movement data

A general framework for using aggregation in visual exploration of movement data

In all aggregations discussed so far the results are numeric values such as counts, sums, statistical means, etc. Buliung & Kanaroglou (2004) derive a kind of geometric summary of several trajectories. The authors use functions of ArcGIS to build a convex hull containing the trajectories, compute the central tendency and dispersion of the paths, and represent the results on a map as the averaged path. Such geometric summarization can work well only when the trajectories are similar in shape and close in space. It can be applied, for example, to groups of similar trajectories resulting from clustering. Grouping of trajectories by similarity and/or closeness of the routes followed by geometric and/or numeric summarization may be called R-aggregation (i.e. route-based). The paper (Andrienko et al. 2007) contains examples of combining route-based grouping of trajectories by means of clustering with S×S- and S×S×T×T-aggregation. It should be noted that route-based grouping does not guarantee that each trajectory is put in some group as there may be trajectories whose routes significantly differ from all others. Hence, a result of R-aggregation may consist of aggregates and solitary trajectories. For the sake of uniformity, the latter can be represented as aggregates of magnitude one.
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Using Outlook Web Access (OWA)

Using Outlook Web Access (OWA)

o Shared or Unsecured: Select this option if you are working from a non- secure computer, or one that multiple people have access to (such as at a public library). This will cause your OWA session to automatically timeout after only a short period of inactivity to prevent non-authorized people from accessing your account.

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Temporal Aggregation for ARMA Model in an Application Perspective

Temporal Aggregation for ARMA Model in an Application Perspective

Temporal aggregation accounts for this phenomenon, that is, the frequency of data collection is lower than the frequency at which data are generated, thus part of items in the original random process 𝑥 = {𝑥 𝑡 } 𝑡=0 ∞ are missing, only items after aggregation 𝑋 = {𝑋 𝜏 } 𝜏=0 ∞ can be observed, where 𝑡 is the original frequency and 𝜏 is the after-aggregation frequency, 𝑋 is a specific function of 𝑥 determined by the aggregation scheme (Marcellino, 1999). In the above case, trading data are generated at every minute while people only gather them at every day. Temporal aggregation model is a great method to fix this problem by ‘transferring’ time unit to integrate every single hour data or even every minute data (not realistic though) into the daily model for optimization. So once data is available, players could improve their model to better its forecasting ability.
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Incremental aggregation model for data stream classification

Incremental aggregation model for data stream classification

In online data stream processing, data stream classification task confronts several challenges such as, concept drift, concept evolution and partial labeling due to the dynamic nature of data streams. Amid these issues, concept drift is on the top concern that degrades the accuracy of data stream classification task, immediately upon its occurrence. However, concept evolution and partial labeling are also equally notable plights that are not focused by most of the existing approaches. Ensemble learning is a widely accepted prominent method that attempts to reconcile the issues encountering in the data stream classification. Our previous work addresses only the different types of concept drifts. This paper expounds a Novel Incremental Aggregation Model (IAM) which makes use of Adaptive Probabilistic Neural Network (APNN), Aggregate Weighted Ensemble Model (AWEM) and Ensemble Cloning that makes the system impeccable by combating against all the above said issues. The performance of the proposed algorithm has been experimentally tested with few synthetic data sets. Experimental results show that our model outperforms the existing ensemble approaches in terms of accuracy.
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Addressing Computational Complexity of Electromagnetic Systems Using Parameterized Model Order Reduction

Addressing Computational Complexity of Electromagnetic Systems Using Parameterized Model Order Reduction

In this chapter, a parameterized MOR technique is developed for distributed electromagnetic systems that have arbitrary functions of frequency due to material properties, boundary conditions and delay elements. The proposed algorithm directly differentiates the network equations and uses multi-order Arnoldi method to calculate the moments, without having to perform rational curve fitting or introduce separate variables to approximate the arbitrary functions of frequency. The developed algorithm is also extended to implicitly calculate the moments with respect to arbitrary function design parameters as well as the cross-moments. This procedure results in a parameterized reduced order model that is valid over a user defined range of design parameters while preserving the form of the original system. Numerical examples are provided to illustrate the validity of the proposed technique.
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Aggregation model for curtailable generation and sheddable loads

Aggregation model for curtailable generation and sheddable loads

In this model, curtailment can be defined as an instance, when a generation unit produces less than it could. Curtail- ment can be voluntary or involuntary, as for example en- forced by a TSO/DSO, and common reasons for curtail- ment include network constraints, operational security, ex- cess generation with respect to the grid load, and strategic bidding related to the potential price manipulations [2]. The availability of wind does not only influence when power can be generated, but also the ability to adjust the generated output [3]. The same can be applied to solar pho- tovoltaic (PV) generation. PV generation provides possi- bilities to full or partial down-regulation by reducing the volume of injected electricity. Down-regulation is also used for wind power. By controlling the pitch of the wind turbine blades, the power output can be curtailed partially. In addition, there are test projects [4] studying the possi- bility for using wind power for upward-regulation. The market design considered in the SmartNet project is for nearly real-time operation, and therefore it is reasonable to assume that wind power can be used for both up- and down-regulation. The flexibility levels submitted by wind and PV generation should correspond to the available gen- eration potential at a given time.
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Tour of Outlook Web App (OWA)

Tour of Outlook Web App (OWA)

The Block or Allow section in the options pane allows users to whitelist (allow) or blacklist (deny) all mail from specific email addresses as well as extra options to handle personal[r]

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A simple, regionally parameterized model for predicting nonpoint source areas in the northeastern US

A simple, regionally parameterized model for predicting nonpoint source areas in the northeastern US

Model results for the 10 USGS gauges used in this study based on a take-one-out approach. Daily NSE values in parentheses are during periods that include additional precipitation gauges inside the watershed (if applicable). RSR is the ratio of the root mean square error to the standard deviation of observed streamflow, and PBIAS is the percent bias ( Moriasi et al., 2007 ). The average gauge distance from watershed was calculated by assigning a distance of zero for all gauges inside the watershed, and using the dist2Line function in the R-package geosphere ( Hijmans et al., 2012 ) to calculate the minimum distance from a gauge to the closest point along the watershed’s boundary.
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