Improvement of Fuzzy Image Contrast Enhancement Using Simulated Ergodic Fuzzy Markov Chains

This paper presents a novel fuzzy enhancement technique using simulated ergodic fuzzy Markov chains for low contrast brain magnetic resonance imaging (MRI). The fuzzy image contrast enhancement is proposed by weighted fuzzy expected value. The membership values are then modified to enhance the image using ergodic fuzzy Markov chains. The qualitative performance of the proposed method is compared to another method in which ergodic fuzzy Markov chains are not considered. The proposed method produces better quality image

airline revenue management

With the increasing interest in decision support systems and the continuous advance of computer science, revenue management is a discipline which has received a great deal of interest in recent years. Although revenue management has seen many new applications throughout the years, the main focus of research continues to be the airline industry. Ever since Littlewood (1972) first proposed a solution method for the airline revenue management problem, a variety of solution methods have been introduced. In this paper we will give an overview of the solution methods presented throughout the literature.revenue management;seat inventory control;OR techniques;mathematical programming

Condition Assessment Models for Sewer Pipelines

Underground pipeline system is a complex infrastructure system that has significant impact on social, environmental and economic aspects. Sewer pipeline networks are considered to be an extremely expensive asset. This study aims to develop condition assessment models for sewer pipeline networks. Seventeen factors affecting the condition of sewer network were considered for gravity pipelines in addition to the operating pressure for pressurized pipelines. Two different methodologies were adopted for models’ development. The first method by using an integrated Fuzzy Analytic Network Process (FANP) and Monte-Carlo simulation and the second method by using FANP, fuzzy set theory (FST) and Evidential Reasoning (ER). The models’ output is the assessed pipeline condition. In order to collect the necessary data for developing the models, questionnaires were distributed among experts in sewer pipelines in the state of Qatar. In addition, actual data for an existing sewage network in the state of Qatar was used to validate the models’ outputs. The “Ground Disturbance” factor was found to be the most influential factor followed by the “Location” factor with a weight of 10.6% and 9.3% for pipelines under gravity and 8.8% and 8.6% for pipelines under pressure, respectively. On the other hand, the least affecting factor was the “Length” followed by “Diameter” with weights of 2.2% and 2.5% for pipelines under gravity and 2.5% and 2.6% for pipelines under pressure. The developed models were able to satisfactorily assess the conditions of deteriorating sewer pipelines with an average validity of approximately 85% for the first approach and 86% for the second approach. The developed models are expected to be a useful tool for decision makers to properly plan for their inspections and provide effective rehabilitation of sewer networks.1)- NPRP grant # (NPRP6-357-2-150) from the QatarNational Research Fund (Member of Qatar Foundation) 2)-Tarek Zayed, Professor of Civil Engineeringat Concordia University for his support in the analysis part, the Public Works 3)-Authority of Qatar (ASHGAL) for their support in the data collection