Investigation on soft computing techniques for airport environment evaluation systems

Spatial and temporal information exist widely in engineering fields, especially in airport environmental management systems. Airport environment is influenced by many different factors and uncertainty is a significant part of the system. Decision support considering this kind of spatial and temporal information and uncertainty is crucial for airport environment related engineering planning and operation. Geographical information systems and computer aided design are two powerful tools in supporting spatial and temporal information systems. However, the present geographical information systems and computer aided design software are still too general in considering the special features in airport environment, especially for uncertainty. In this thesis, a series of parameters and methods for neural network-based knowledge discovery and training improvement are put forward, such as the relative strength of effect, dynamic state space search strategy and compound architecture. [Continues.

The Reconstruction of Non-Minimal Derivative Coupling Inflationary Potentials

We derive the reconstruction formulae for the inflation model with the non-minimal derivative coupling term. If reconstructing the potential from the tensor-to-scalar ratio, we could obtain the potential without using the high friction limit. As an example, we reconstruct the potential from the parametrization $r=8\alpha/(N+\beta)^{\gamma}$, which is a general form of the $\alpha$-attractor. The reconstructed potential has the same asymptotic behavior as the T- and E-model if we choose $\gamma=2$ and $\alpha\ll1$. We also discuss the constraints from the reheating phase preceding the radiation domination by assuming the parameter $w_{re}$ of state equation during reheating is a constant. The scale of big-bang nucleosynthesis could put a up limit on $n_s$ if $w_{re}=2/3$ and a low limit on $n_s$ if $w_{re}=1/6$.Comment: 12 pages, 3 figure

Weight Analysis for Multiattribute Group Decision-Making with Interval Grey Numbers Based on Decision-Makers’ Psychological Criteria

open access articleTo address the problem of multiattribute group decision-making with interval grey numbers, decision matrices are adjusted using kernels of interval grey numbers to reduce the psychological effects of decision-makers. The comprehensive weights of attributes are obtained by aggregating the subjective weights with objective weights, which are calculated based on the accuracy and difference of attributes. Considering the consistent, best, and worst decision-making abilities of decision-makers, grey incidence models are established to obtain the consistency weights and individual bipolar weights of decision-makers; then, the comprehensive weights of decision-makers are determined. A clustering approach of interval grey numbers is presented, and overall evaluations are obtained. Finally, an example is provided and its validity is tested to verify the feasibility of the proposed method

Uncertainty representation of grey numbers and grey sets

In the literature there is a presumption that a grey set and an interval-valued fuzzy set are equivalent. This presumption ignores the existence of discrete components in a grey number. In this paper new measurements of uncertainties of grey numbers and grey sets, consisting of both absolute and relative uncertainties, are defined to give a comprehensive representation of uncertainties in a grey number and a grey set. Some simple examples are provided to illustrate that the proposed uncertainty measurement can give an effective representation of both absolute and relative uncertainties in a grey number and a grey set. The relationships between grey sets and interval-valued fuzzy sets are also analysed from the point of view of the proposed uncertainty representation. The analysis demonstrates that grey sets and intervalvalued fuzzy sets provide different but overlapping models for uncertainty representation in sets

Forecasting the multifactorial interval grey number sequences using grey relational model and GM (1, N) model based on effective information transformation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In the context of data eruption, the data often shows a short-term pattern and changes rapidly which makes it difficult to use a single real value to express. For this kind of small-sample and interval data, how to analyze and predict muti-factor sequences efficiently becomes a problem. By this means, grey system theory (GST) is developed in which the interval grey numbers, as a typical object of GST, characterize the range of data and the grey relational and prediction models analyze the relations of multiple grey numbers and forecast the future. However, traditional grey relative relational model has some limitations: the results obtained always show low resolution and there are no extractions for the interval feature information from the interval grey number sequence. In this paper, the grey relational analysis model (GRA) based on effective information transformation of interval grey numbers is established, which contains comprehensive information of area differences and slope variances and optimizes the resolution of traditional grey degree. Then, according to the relational results, the multivariable GM model (GM(1,N)) is proposed to forecast the interval grey number sequence. To verify the effectiveness of this novel model, it is established to analyze the relationship between the degree of traffic congestion and its relevant factors in the Yangtze River Delta of China and predict the development of urban traffic congestion degrees in this area over the next five years. In addition, some traditional statistical methods (principal component analysis, multiple linear regression models and curve regression models) are established for comparisons. The results show high performances of the novel GRA model and GM(1,N) model, which means the models proposed in this paper are suitable for interval grey numbers from regional data. The strengths which recommend the use of this novel method lie in its high recognition mechanism and muti-angle information transformation for interval grey numbers as well as its characteristic of timeliness in information processing

A Novel Synthetic Index of Two Counts and Mathematical Model for Researcher Evaluation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose – The purpose of this paper is to present a novel synthetic index of two counts and mathematical model for researcher evaluation. Design/methodology/approach –A synthetic index L for researcher evaluation considering both the total number of other citations (C) and non-academic impact (I), and a synthetic evaluation model is proposed in this paper. C and I are verified impact indexes. According to investigation by Delphi method, researchers are divided into five different classes of “below average”, “average”, “good”, “excellent” and “stellar”. The threshold values for counts C of grey class “stellar” are determined by deep investigation. The possibility functions of the two counts C and I on four grey classes of “below average”, “average”, “good”, and “excellent” are built. Findings –The novel synthetic index of two counts and mathematical model for researcher evaluation providing a better way to conduct researcher assessment. Practical implications –The synthetic index L presented in this paper can be used to evaluate a researcher. It’s more reasonable than the current research assessment indexes such as the number of publications and the numbers of so called high quality journal publications, and the amount of granted funds, etc. The synthetic index L reflect the actual value created by a researcher. No artificial manoeuvre can change them significantly. Originality/value –A synthetic index L for researcher evaluation considering both the total number of other citations (C) and non-academic impact (I), and a synthetic evaluation model is proposed in this paper

A New 95 GHz Methanol Maser Catalog: I. Data

The Purple Mountain Observatory 13.7 m radio telescope has been used to search for 95 GHz (8$_0$--7$_1$A$^+$) class I methanol masers towards 1020 Bolocam Galactic Plane Survey (BGPS) sources, leading to 213 detections. We have compared the line width of the methanol and HCO$^+$ thermal emission in all of the methanol detections and on that basis we find 205 of the 213 detections are very likely to be masers. This corresponds to an overall detection rate of 95 GHz methanol masers towards our BGPS sample of 20%. Of the 205 detected masers 144 (70%) are new discoveries. Combining our results with those of previous 95 GHz methanol masers searches, a total of four hundred and eighty-one 95 GHz methanol masers are now known, we have compiled a catalog listing the locations and properties of all known 95 GHz methanol masers.Comment: 18 pages, 7 figures, 8 tables, accepted for publication in ApJ