Search results

This paper aims to demonstrate how to make emergency decision when decision makers face a complex and turbulent environment that needs quite different decision-making processes from conventional ones. Traditional decision techniques cannot meet the demands of today’s social stability and security.

The main work is to develop an instance-driven classifier for the emergency categories based upon three fuzzy measures: features for an instance, solution for the instance and effect evaluation of the outcome. First, the information collected from the past emergency events is encodes into a prototype model. Second, a three-dimensional space that describes the locations and mutual distance relationships of the emergency events in different emergency prototypes is formulated. Third, for any new emergency event to be classified, the nearest emergency prototype is identified in the three-dimensional space and is classified into that category.

An instance-driven classifier based on prototype theory helps decision makers to describe emergency concept more clearly. The maximizing deviation model is constructed to determine the optimal relative weights of features according to the characteristics of the new instance, such that every customized feature space maximizes the influence of features shared by members of the category. Comparisons and discusses of the proposed method with other existing methods are given.

To reduce the affection to economic development, more and more countries have recognized the importance of emergency response solutions as an indispensable activity. In a new emergency instance, it is very challengeable for a decision maker to form a rational and feasible humanitarian aids scheme under the time pressure. After selecting a most suitable prototype, decision makers can learn most relevant experience and lessons in the emergency profile database and generate plan for the new instance. The proposed approach is to effectively make full use of inhomogeneous information in different types of resources and optimize resource allocation.

The combination of instances can reflect different aspects of a prototype. This feature solves the problem of insufficient learning data, which is a significant characteristic of emergency decision-making. It can be seen as a customized classification mechanism, while the previous classifiers always assume key features of a category.

In clinical analysis, medical image segmentation is an important step to study the anatomical structure. This helps to diagnose and classify abnormality in the image. The wide variations in the image modality and limitations in the acquisition process of instruments make this segmentation challenging. This paper aims to propose a semi-automatic model to tackle these challenges and to segment medical images.

The authors propose Legendre polynomial-based active contour to segment region of interest (ROI) from the noisy, low-resolution and inhomogeneous medical images using the soft computing and multi-resolution framework. In the first phase, initial segmentation (i.e. prior clustering) is obtained from low-resolution medical images using fuzzy C-mean (FCM) clustering and noise is suppressed using wavelet energy-based multi-resolution approach. In the second phase, resultant segmentation is obtained using the Legendre polynomial-based level set approach.

The proposed model is tested on different medical images such as x-ray images for brain tumor identification, magnetic resonance imaging (MRI), spine images, blood cells and blood vessels. The rigorous analysis of the model is carried out by calculating the improvement against noise, required processing time and accuracy of the segmentation. The comparative analysis concludes that the proposed model withstands the noise and succeeds to segment any type of medical modality achieving an average accuracy of 99.57%.

The proposed design is an improvement to the Legendre level set (L2S) model. The integration of FCM and wavelet transform in L2S makes model insensitive to noise and intensity inhomogeneity and hence it succeeds to segment ROI from a wide variety of medical images even for the images where L2S failed to segment them.

Blown up theory is very important in modern forecasting science, and will result in revolution not only in forecasting theories but also in applied theories and applied methods. Moreover, the blown‐up theory will involve re‐thinking and re‐formulation of some concepts in traditional theories. This article is a record of dialogue between Professor OuYang and the author on some important issues. It is believed that this record will not only benefit us greatly, but also be inductive for young generations in developing their way of thinking and research directions.

In this article, a review of the past history of open problems and challenges facing the theory of blown‐ups is given. It expresses the hope that the open problems and challenges will be further studied by our readers in the years to come.

In this paper, some issues about model formulation and solving methods for the Lorenz’s model are discussed, which also deals with fundamental characteristics of non‐linear models. Mainly, there existed problems that Lorenz’s model was formulated based on the weak nonlinear, and spectral expansion which changed the fundamental properties of prototype of the N‐S equations. Furthermore, its solving methods, which are based on continuously smoothing schemes, not only distorted the fundamental characteristics of non‐linear models, but also led to misunderstanding of the concept of “schaos”, which has already caused confusion to people’s way of thinking. In essence, Lorenz’s chaos is an “error volute” which is trapped into “calculation of error values”. Whether it can be named “chaos” may need to be discussed with questions.

The purpose of this paper is to solve numerically the identification of the thermal conductivity of an inhomogeneous and possibly anisotropic medium from interior/internal temperature measurements.

The formulated coefficient identification problem is inverse and ill-posed, and therefore, to obtain a stable solution, a non-linear regularized least-squares approach is used. For the numerical discretization of the orthotropic heat equation, the finite-difference method is applied, while the non-linear minimization is performed using the MATLAB toolbox routine lsqnonlin.

Numerical results show the accuracy and stability of solution even in the presence of noise (modelling inexact measurements) in the input temperature data.

The mathematical formulation uses temporal temperature measurements taken at many points inside the sample, and this may be too much information that is provided to identify a space-wise dependent only conductivity tensor.

As noisy data are inverted, the paper models real situations in which practical temperature measurements recorded using thermocouples are inherently contaminated with random noise.

The identification of the conductivity of inhomogeneous and orthotropic media will be of great interest to the inverse problems community with applications in geophysics, groundwater flow and heat transfer.

The current investigation advances the field of coefficient identification problems by generalizing the conductivity to be anisotropic in addition of being heterogeneous. The originality lies in performing, for the first time, numerical simulations of inversion to find the orthotropic and inhomogeneous thermal conductivity from noisy temperature measurements. Further value and physical significance are brought in by determining the degree of cure in a resin transfer molding process, in addition to obtaining the inhomogeneous thermal conductivity of the tested material.

This paper attempts to develop an efficient algorithm to solve the inverse problem of identifying constitutive parameters in VFG (viscoelastic functionally graded) materials/structures.

An adaptive recursive algorithm with high fidelity is developed to acquire the derivatives of displacements with respect to constitutive parameters, which are required for the accurate and stable gradient based inverse analysis. A two-step strategy is presented in the process of identification, by which the unknown parameters can be separately identified and the scale and complexity of the inverse VFG problem are reduced. At each step, the process of identification is treated as an optimization problem that is solved by the Levenberg–Marquardt method.

The solution accuracy of forward problems and derivatives of displacements can be stably achieved with different step sizes, and constitutive parameters of homogenous/regional-inhomogeneous VFG materials/structures can be effectively and accurately identified. By examining the reliability, resolution, impacts of reference information and noisy data, the effectiveness of the proposed approach is numerically verified via three numerical examples.

An adaptive recursive algorithm is developed for derivatives computing with high fidelity, providing a solid platform for the sensitivity analysis and thereby a two-step strategy in conjunction with Levenberg–Marquardt method is presented in the process of identification. Consequently, an effective algorithm is developed to identify constitutive parameters of homogenous/regional-inhomogeneous VFG materials/structures.

Crack damage detection for aluminum alloy materials using fiber Bragg Grating (FBG) sensor is a kind of structure health monitoring. In this paper, the damage index of full width at half maximum (FWHM) was extracted from the distorted reflection spectra caused by the crack-tip inhomogeneous strain field, so as to explain the crack propagation behaviors.

The FWHM variations were also investigated through combining the theoretical calculations with simulation and experimental analyses. The transfer matrix algorithm was developed to explore the mechanism by which FWHM changed with the linear and quadratic strain. Moreover, the crack-tip inhomogeneous strain field on the specimen surface was computed according to the digital image correlation measurement during the experiments.

The experimental results demonstrated that the saltation points in FWHM curve accorded with the moments of crack propagation to FBG sensors.

The interpretation of reflected spectrum deformation mechanism with crack propagation was analyzed based on both simulations and experiments, and then the performance of potential damage features – FWHM were proposed and evaluated. According to the correlation between the damage characteristic and the crack-tip location, the crack-tip of the specimen could be measured rapidly and accurately with this technique.

Gives introductory remarks about chapter 1 of this group of 31 papers, from ISEF 1999 Proceedings, in the methodologies for field analysis, in the electromagnetic community. Observes that computer package implementation theory contributes to clarification. Discusses the areas covered by some of the papers ‐ such as artificial intelligence using fuzzy logic. Includes applications such as permanent magnets and looks at eddy current problems. States the finite element method is currently the most popular method used for field computation. Closes by pointing out the amalgam of topics.