An important issue in fuzzy-rule-based modeling is how to select a set of important fuzzy rules from a given rule base. Even though it is conceivable that removal of redundant or less important fuzzy rules from the rule base can result in a compact fuzzy model with better generalizing ability, the decision as to which rules are redundant or less important is not an easy exercise. In this paper, we introduce several orthogonal transformation-based methods that provide new or alternative tools for rule selection. These methods include an orthogonal least squares (OLS) method, an eigenvalue decomposition (ED) method, a singular value decomposition and QR with column pivoting (SVD-QR) method, a total least squares (TLS) method, and a direct singular value decomposition (D-SVD) method. A common attribute of these methods is that they all work on a firing strength matrix and employ some measure index to detect the rules that should be retained and eliminated. We show the performance of these methods by applying them to solving a nonlinear plant modeling problem. Our conclusions based on analysis and simulation can be used as a guideline for choosing a proper rule selection method for a specific application.

Abstract-In this paper, we study the resilience of supply networks against disruptions and provide insights to supply chain managers on how to construct a resilient supply network from the perspective of complex network topologies. Our goal is to study how different network topologies, which are created from different growth models, affect the network's resilience against both random and targeted disruptions. Of particular interest are situations where the type of disruption is unknown. Using a military logistic network as a case study, we propose new network resilience metrics that reflect the heterogeneous roles (e.g., supply, relay, and demand) of nodes in supply networks. We also present a hybrid and tunable network growth model called Degree and Locality-based Attachment (DLA), in which new nodes make connections based on both degree and locality. Using computer simulations, we compare the resilience of several supply network topologies that are generated with different growth models. The results show that the new resilience metrics can capture important resilience requirements for supply networks very well. We also found that the supply network topology generated by the DLA model provides balanced resilience against both random and targeted disruptions.

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With the desire to apply the Dempster-Shafer theory to complex real world problems where the evidential strength is often imprecise and vague, several attempts have been made to generalize the theory. However, the important concept in the D-S theory that the belief and plausibility functions are lower and upper probabilities is no longer preserved in these generalizations. In this paper, we describe a generalized theory of evidence where the degree of belief in a fuzzy set is obtained by minimizing the probability of the fuzzy set under the constraints imposed by a basic probability assignment. To formulate the probabilistic constraint of a fuzzy focal element, we decompose it into a set of consonant non-fuzzy focal elements. By generalizing the compatibility relation to a possibility theory, we are able to justify our generalization to Dempster's rule based on possibility distribution. Our generalization not only extends the application of the D-S theory but also illustrates a way that probability theory and fuzzy set theory can be combined to deal with different kinds of uncertain information in AI systems.

The unique thermal and mechanical properties exhibited by shape memory alloys (SMAs) present exciting design possibilities in the field of aerospace engineering. When properly trained, SMA wires act as linear actuators by contracting when heated and returning to their original shape when cooled. These SMA wire actuators can be attached to points on the inside of an airfoil, and can be activated to alter the shape of the airfoil. This shape-change can effectively increase the efficiency of a wing in flight at several different flow regimes. To determine the necessary placement of the SMA wire actuators within the wing, a global optimization method that incorporates a coupled structural, thermal, and aerodynamic analysis has been utilized. A genetic algorithm has been chosen as the optimization tool to efficiently converge to a design solution. The genetic algorithm used in this case is a hybrid version with global search and optimization capabilities augmented by the simplex method with selective line search as a local search technique. A cost function based on the aerodynamic properties of the airfoil has been used to optimize this design problem to maximize the lift-to-drag ratio for a reconfigured airfoil shape at subsonic flow conditions. A wind tunnel model reconfigurable wing was fabricated based on the design optimization to verify the predicted structural and aerodynamic response. Wind tunnel tests indicated an increase in lift for a given flow velocity and angle of attack by activating the SMA wire actuators. The pressure data taken during the wind tunnel tests followed the trends expected from the numerical pressure results.

Online cancer communities help members support one another, provide new perspectives about living with cancer, normalize experiences, and reduce isolation. The American Cancer Society's 166000-member Cancer Survivors Network (CSN) is the largest online peer support community for cancer patients, survivors, and caregivers. Sentiment analysis and topic modeling were applied to CSN breast and colorectal cancer discussion posts from 2005 to 2010 to examine how sentiment change of thread initiators, a measure of social support, varies by discussion topic. The support provided in CSN is highest for medical, lifestyle, and treatment issues. Threads related to 1) treatments and side effects, surgery, mastectomy and reconstruction, and decision making for breast cancer, 2) lung scans, and 3) treatment drugs in colon cancer initiate with high negative sentiment and produce high average sentiment change. Using text mining tools to assess sentiment, sentiment change, and thread topics provides new insights that community managers can use to facilitate member interactions and enhance support outcomes.

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Community discovery has drawn significant re-inspired by the success of the application of LDA(Latent search interests among researchers from many disciplines for Dirichlet Allocation) models in the information retrieval and its increasing application in multiple, disparate areas, including image analysis domains. In this model, communities are computer science, biology, social science and so on. This paper .. 'describes an LDA(latent Dirichlet Allocation)-based hierarchical modeled as latent variahles and are considered as distrilutions Bayesian algorithm, namely SSN-LDA(Simple Social Network on the entire social actor space. This way each social actor LDA). In SSN-LDA, communities are modeled as latent variables contributes a part, big or small, to every community in the in the graphical model and defined as distributions over the social society. We also propose three different approaches to create actor space. The advantage of SSN-LDA is that it only requires social interaction profiles hased on the social interaction infortopological information as input. This model is evaluated on two research collaborative networks:CiteSeer and NanoSCI. The mation in the network. The latent probabilistic model and three experimental results demonstrate that this approach is promising pertaining representation approaches are evaluated on two cofor discovering community structures in large-scale networks.' authorship networks from two distinct academic communities, i.e Na;noSCI from the nanotechnology domain and CiteSeer