9944435
On the centroids of fuzzy numbers
In a paper by Cheng [A new approach for ranking fuzzy numbers by distance method, Fuzzy Sets and Systems 95 (1998) 307-317], a centroid-based distance method was suggested for ranking fuzzy numbers, both normal and non-normal, where the fuzzy numbers are compared and ranked in terms of their Euclidean distances from their centroid points to the origin. It is found that the centroid formulae provided by the above paper are incorrect and have led to some misapplications. In this paper we present the correct centroid formulae for fuzzy numbers and justify them from the viewpoint of analytical geometry. A numerical example demonstrates that Cheng's formulae can significantly alter the result of the ranking procedure.
8185115
On the combination and normalization of interval-valued belief structures
This paper investigates the issues of combination and normalization of interval-valued belief structures within the framework of Dempster-Shafer theory (DST) of evidence. Existing approaches are reviewed, examined and critically analysed. They either ignore the normalization or separate it from the combination process, leading to irrational or suboptimal interval-valued belief structures. A new logically correct optimality approach is developed, where the combination and the normalization are optimised together rather than separately. Numerical examples are provided throughout the paper.
13993519
Interval efficiency assessment using data envelopment analysis
This paper studies how to conduct efficiency assessment using data envelopment analysis (DEA) in interval and/or fuzzy input-output environments. A new pair of interval DEA models is constructed on the basis of interval arithmetic, which differs from the existing DEA models handling interval data in that the former is a linear CCR model without the need of extra variable alternations and uses a fixed and unified production frontier (i.e. the same constraint set) to measure the efficiencies of decision-making units (DMUs) with interval input and output data, while the latter is usually a nonlinear optimization problem with the need of extra variable alternations or scale transformations and utilizes variable production frontiers (i.e. different constraint sets) to measure interval efficiencies. Ordinal preference information and fuzzy data are converted into interval data through the estimation of permissible intervals and @a-level sets, respectively, and are incorporated into the interval DEA models. The proposed interval DEA models are developed for measuring the lower and upper bounds of the best relative efficiency of each DMU with interval input and output data, which are different from the interval formed by the worst and the best relative efficiencies of each DMU. A minimax regret-based approach (MRA) is introduced to compare and rank the efficiency intervals of DMUs. Two numerical examples are provided to show the applications of the proposed interval DEA models and the preference ranking approach.
1590392
Inference and learning methodology of belief-rule-based expert system for pipeline leak detection
Belief rule based expert systems are an extension of traditional rule based systems and are capable of representing more complicated causal relationships using different types of information with uncertainties. This paper describes how the belief rule based expert systems can be trained and used for pipeline leak detection. Pipeline operations under different conditions are modelled by a belief rule base using expert knowledge, which is then trained and fine tuned using pipeline operating data, and validated by testing data. All training and testing data are collected and scaled from a real pipeline. The study demonstrates that the belief rule based system is flexible, can be adapted to represent complicated expert systems, and is a valid novel approach for pipeline leak detection.
19364030
The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees
Abstract Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modeled using the evidential reasoning (ER) approach. Several types of uncertainties such as ignorance and fuzziness can be modeled in the ER framework. In this paper, the ER approach will be extended to model new types of uncertainties including interval belief degrees and interval data that could be incurred in decision situations such as group decision making. The Dempster–Shafer (D–S) theory of evidence is first extended, which is one of the bases of the ER approach. The analytical ER algorithm is used to combine all evidence simultaneously. Two pairs of nonlinear optimization models are constructed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities of each alternative, respectively. Interval data are equivalently transformed to interval belief degrees and are incorporated into the nonlinear optimization models. A cargo ship selection problem is examined to show the implementation process of the proposed approach.
207143686
Estimating The Attribute Weights Through Evidential Reasoning And Mathematical Programming
An approach for estimating attribute weight is proposed, which is addressed to the problem inverse to the aggregation assessment based on a hierarchy of attributes. Evidential reasoning framework is employed since the assessment on some basic attributes may be uncertain and incomplete. As a by-product, a simple formula for evidence combination for our special case is obtained. The weights are estimated based on the preference given by the decision-maker, and represented as intervals. The more abundant preference the decision-maker shows, the clearer the relation among the basic attributes.
26719731
Recursive discrete Fourier transform with unified IIR filter structures
In this paper, we propose two unified IIR filters to recursively compute the discrete Fourier transform (DFT). With the advantage of local connection, regularity and modularity, the proposed methods can compute all DFT coefficients with the same filter structure. Based on stability behavior, the proposed IIR filter structures with optimal coefficients can achieve more accurate results than the traditional ones whose filter coefficients should be changed for computing the different DFT coefficients. Simulation results verify the aforementioned theoretical observations.
8764549
Efficient rate-distortion estimation for H.264/AVC coders
In video coders, the optimal coding mode decision for each coding block can be achieved by exhaustively calculating the rate-distortion cost, which simultaneously considers the distortion performance and the coding bit consumption of all possible modes. The best mode is chosen from the one with the minimum Lagrange cost. To avoid the expensive computation of Lagrange costs, in this paper, we propose transform-domain bit-rate estimation and distortion measures, based on quantized and inverse quantized integer transform coefficients, for the inter-mode decision in H.264/AVC coders. With the proposed scheme, entropy coding, inverse transform, and pixel-reconstructions are not required in the process. With ignorable degradation in coding performance, simulations demonstrate that the proposed estimation method achieves about 40% reduced computation time of rate-distortion cost for the inter-mode decision and saves about 17% total encoding time while combining with fast motion estimation and fast mode decision algorithms.
949732
Optimizing Queries Across Diverse Data Sources
Businessestoday need to interrelate data stored in diverse systems with differing capabilities, ideally via a single high-level query interface. We present the design of a query optimizer for Garlic [C 95], a middleware system designedto integrate data from a broad range of data sources with very different query capabilities. Garlic’s optimizer extends the rule-based approach of [Loh88] to work in a heterogeneous environment, by defining generic rules for the middleware and using wrapper-provided rules to encapsulate the capabilities of each data source. This approach offers great advantages in terms of plan quality, extensibility to new sources, incremental implementationof rules for new sources, and the ability to express the capabilities of a diverse set of sources. We describe the design and implementationof this optimizer, and illustrate its actions through an example.
32074986
Statistical Neighbor Distance Influence in Active Contours
In this paper, we propose a new model for segmentation of images containing multiple objects. In order to take advantage of the constraining information provided by neighboring objects, we incorporate information about the relative position and shape of neighbors into the segmentation process by defining a new "distance" term into the energy functional. We introduce a representation for relative neighbor distances, and define a probability distribution over the variances of the relative neighbor distances of a set of training images. By minimizing the energy functional, we formulate the model in terms of level set functions, and compute the associated Euler-Lagrange equations. The contours evolve both according to the relative distance information and the image grey level information. Several objects in an image can be automatically detected simultaneously.
5715843
A Cautionary Tale
Recently we have been investigating various methods for improving the retrieval of documents. One frequently used context for such studies is to work with a known database and set of queries, where sufficient work has previously been done to establish an "official" set of relevant documents for each query. Working within such a context, we have discovered at least one instance wherein the combination of the document descriptions in the database, the query, and the set of documents deemed relevant foredooms our attempts at retrieval improvement to failure. This note is to caution other researchers against such possibilities, and to recommend that the database in question, that of the National Physical Laboratory (NPL), as represented on Virginia Disk 1, be retired from use.