Comparing aggregation methods in large-scale group AHP: Time for the shift to distance-based aggregation

This paper proposes a distance-based aggregation and consensus method for preference-approvals with potential applications in group decision-making, recommendation systems, and social choice theory. Preference-approvals are a type of preference data where individuals provide a list of approved alternatives in addition to a strict ranking. The proposed method aims to synthesise individual preference-approvals into a unified consensus representing the group’s collective view. The consensus is the preference-approval, which minimises the average distance within the whole set of voters. The performance of the proposed method is investigated through a sensitivity analysis on the weighting parameter, two simulation studies, and two real-data applications.

In the case of conflicting individuals or evaluator groups, finding the common preferences of the participants is a challenging task. This statement also refers to Intuitionistic Fuzzy Analytic Hierarchy Process models, in which uncertainty of the scoring of individuals is well-handled, however, the aggregation of the modified scores is generally conducted by the conventional way of multi-criteria decision-making. This paper offers two options for this aggregation: the relatively well-known entropy-based, and the lately emerged distance-based aggregations. The manuscript can be considered as a pioneer work by analyzing the nature of distance-based aggregation under a fuzzy environment. In the proposed model, three clearly separable conflicting groups are examined, and the objective is to find their common priority vector, which can be satisfactory to all participant clusters. We have tested the model results on a real-world case study, on a public transport development decision-making problem by conducting a large-scale survey involving three different stakeholder groups of transportation. The comparison of the different approaches has shown that both entropy-based and distance-based techniques can provide a feasible solution based on their high similarity in the final ordinal and cardinal outcomes.

Constrained two-stage Kalman filter for real-time state estimation of systems with time-varying measurement noise covariance

In this study, we present the functionalities of a new tool for FCMs using credibility weights and OWA-based operators for aggregation tasks. The average aggregation method for weighted interconnections among concepts is the most used method in FCM modeling. The aim of this research work is to (i) propose an alternative aggregation method based on learning OWA operators in aggregating FCM weights, assigned by many experts and/or stakeholders and (ii) to estimate and rank the experts' credibility using a distance-based method. The applicability and usefulness of the proposed methodology in modeling and decision-making is demonstrated using poverty eradication strategies under DAY-NRLM (Deendayal Antyodaya Yojana-National Rural Livelihoods Mission) of India. The results produced by the proposed learning OWA operators are compared with the known average aggregation method of FCMs. These results imply that the proposed alternative FCM aggregation approach is really challenging when a large number of experts and stakeholders are engaged to design the overall FCM model. © 2019 IEEE.

Machine Learning Indices, Political Institutions, and Economic Development

The single explicit analysis using time-dependent damping (SEATD) technique for laser shock peening (LSP) simulation employs variable damping to relax the excited model between laser shots, thus distinguishing it from conventional optimum constant damping methods. Dynamic relaxation (DR) is the well-established conventional technique that mathematically identifies the optimum constant damping coefficient and incremental time-step that guarantees stability and convergence while damping all mode shapes uniformly when bringing a model to quasi-static equilibrium. Examined in this research is a new systematic procedure to strive for a more effective, time-dependent variable damping profile for general LSP configurations and boundary conditions, based on excited modal parameters of a given laser-shocked system. The effects of increasing the number of mode shapes and selecting modes by contributed effective masses are studied, and a procedure to identify the most efficient variable damping profile is designed. Two different simulation cases are studied. It is found that the computational time is reduced by up to 25% (62.5 min) for just five laser shots using the presented variable damping method versus conventional optimum constant damping. Since LSP typically involved hundreds of shots, the accumulated savings in computation time during prediction of desired process parameters is significant.

Acoustic arrays have become an important tool in noise identification for aerospace measurement applications, and the conventional beamforming algorithm has been adopted as a processing technique of choice. In most practical cases the beamforming computations have to be conducted off-line due to extensive computational time requirements. An alternative algorithm with real-time capability has been proposed. The algorithm has a form similar to a classical observer whilst working in the frequency domain for the array processing. The performance of this observer-based algorithm is studied here in a simulation case and in an experimental case by comparing it to a conventional beamforming method. In this paper it is shown that the observer-based algorithm could release the coherence restriction between the background noise and the signal of interest. The proposed observer-based algorithm also has the capability of operating over sampling blocks recursively. The convergence rate of this recursive algorithm is also satisfactory for the simulation case. As a result, a great deal of experimental time could be saved as any testing defects could be revealed instantaneously and corrected on-site. In addition, this innovative approach provides an alternative perspective from which many techniques already in use could be extended to this new application area of array processing.

Expansion of the DC distribution networks in the future power system has raised concerns over their stability assessment. To assess stability of the expanded DC distribution networks, the conventional zone aggregation method requires access to not only the nodes connected to the converters but also some other nodes within the DC passive network. However, access to the nodes within the DC passive network is not always feasible as some parts of the DC network may be buried or encapsulated. To address this issue, this paper proposes a new zone aggregation method for impedance-based stability assessment of the expanded DC-distribution networks. Compared with the conventional method, the main advantages of the proposed method are as follow: i) it only utilizes the impedance/admittance information of the nodes connected to the converters. Moreover, all the impedances/admittances can be measured directly without knowing the internal structure and/or parameters; ii) it can assess stability of expanded distribution networks with minimum number of nodes. Performance and effectiveness of the proposed method are evaluated and validated by simulation studies on a 10-bus DC distribution network and a modified IEEE 37-bus system in the Matlab/Simulink software environment.

Many-to-many comprehensive relative importance analysis and its applications to analysis of semiconductor electrical testing parameters

The evaluation of Nonlinear Output Frequency Response Functions based on tailored data-driven modelling for rotor condition monitoring

In recent years, there has been a tendency to introduce new navigational aids which utilize rotating aerial arrays of large electrical dimensions in order to obtain highly accurate navigational information; one such example is the well-known system called Tacan. In general, the rotation of large arrays introduces severe mechanical problems which fix a lower limit of about 300 Mc/s to the operating radio frequencies of these aids. For many purposes it is desirable to operate in the 100 Mc/s region, and the paper describes a method by which this object may be achieved. Briefly, it consists of rotating an aerial array of small mechanical dimensions and allowing it to be coupled electrically to a static array of very large dimensions which acts as the final radiator of electromagnetic energy. The efficiency of coupling and the form of the static array are such as to produce a final radiation which makes the rotatable array appear to have the large dimensions of the static array. In effect, the method is similar to the familiar ‘goniometer’ technique. The method is described with reference to its application in a new and more accurate form of Vor system (v.h.f. omni-range) known as Vorac. The paper also describes how the new method itself helps to improve the vertical elevational performance of a conventional Vor system.

Uncertainty assessment in reservoir performance prediction using a two-stage clustering approach: Proof of concept and field application

We introduce ProFound, a source finding and image analysis package. ProFound provides methods to detect sources in noisy images, generate segmentation maps identifying the pixels belonging to each source, and measure statistics like flux, size and ellipticity. These inputs are key requirements of ProFit, our recently released galaxy profiling package, where the design aim is that these two software packages will be used in unison to semi-automatically profile large samples of galaxies. The key novel feature introduced in ProFound is that all photometry is executed on dilated segmentation maps that fully contain the identifiable flux, rather than using more traditional circular or ellipse based photometry. Also, to be less sensitive to pathological segmentation issues, the de-blending is made across saddle points in flux. We apply ProFound in a number of simulated and real world cases, and demonstrate that it behaves reasonably given its stated design goals. In particular, it offers good initial parameter estimation for ProFit, and also segmentation maps that follow the sometimes complex geometry of resolved sources, whilst capturing nearly all of the flux. A number of bulge-disc decomposition projects are already making use of the ProFound and ProFit pipeline, and adoption is being encouraged by publicly releasing the software for the open source R data analysis platform under an LGPL-3 license on GitHub (github.com/asgr/ProFound).

The current exponential increase of spatiotemporally explicit data streams from satellitebased Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heuristics provides a pathway for fast inference of essential vegetation variables by means of hybrid retrieval approaches, i.e., machine learning regression algorithms trained by radiative transfer model (RTM) simulations. In this study we summarize AL theory and perform a brief systematic literature survey about AL heuristics used in the context of Earth observation regression problems over terrestrial targets. Across all relevant studies it appeared that: (i) retrieval accuracy of AL-optimized training data sets outperformed models trained over large randomly sampled data sets, and (ii) Euclidean distance-based (EBD) diversity method tends to be the most efficient AL technique in terms of accuracy and computational demand. Additionally, a case study is presented based on experimental data employing both uncertainty and diversity AL criteria. Hereby, a a simulated training data base by the PROSAIL-PRO canopy RTM is used to demonstrate the benefit of AL techniques for the estimation of total leaf carotenoid content (Cxc) and leaf water content (Cw). Gaussian process regression (GPR) was incorporated to minimize and optimize the training data set with AL. Training the GPR algorithm on optimally AL-based sampled data sets led to improved variable retrievals compared to training on full data pools, which is further demonstrated on a mapping example. From these findings we can recommend the use of AL-based sub-sampling procedures to select the most informative samples out of large training data pools. This will not only optimize regression accuracy due to exclusion of redundant information, but also speed up processing time and reduce final model size of kernel-based machine learning regression algorithms, such as GPR. With this study we want to encourage further testing and implementation of AL sampling methods for hybrid retrieval workflows. AL can contribute to the solution of regression problems within the framework of operational vegetation monitoring using satellite imaging spectroscopy data, and may strongly facilitate data processing for cloud-computing platforms.

Potassium dihydrogen phosphate (KDP) single crystals are the only nonlinear crystals currently used for electro-optic switches and frequency converters in inertial confinement fusion research, due to their large dimension and exclusive physical properties. Based on the traditional solution-growth process, large bulk KDP crystals, usually with sizes up to 600 $\times $ 600 mm$^{{2}}$ so as to make a frequency doubler for the facility requirement loading highly flux of power laser, can be grown in standard Holden-type crystallizers, without spontaneous nucleation and visible defects, one to two orders of magnitude faster than by conventional methods. Pure water and KDP raw material with a few ion impurities such as Fe, Cr, and Al (less than 0.1 ppm) were used. The rapid-growth method includes extreme conditions such as temperature range from 60 to 35$^{\circ }$C, overcooling up to 5$^{\circ }$C, growth rates exceeding 10 mm/day, and crystal size up to 600 mm. The optical parameters of KDP crystals were determined. The optical properties of crystals determined indicate that they are of favorable quality for application in the facility.