A Spatial Fuzzy Compromise Approach for Flood Disaster Management

Natural disasters affect regions with different intensity and produce damages that vary in space. Topographical features of the region; location of properties that may be exposed to the peril; level of exposure; impact of different mitigation measures; are all variables with considerable spatial variability. A new method for evaluation of disaster impacts has been presented in this report that takes into consideration spatial variability of variables involved and associated uncertainty. Flood management has been used to illustrate the utility of proposed approach. Floodplain management is a spatial problem. Representation of flood damage mitigation alternatives and objectives in space provides a better insight into the management problem and its characteristics. Protection of a region from floods can be achieved through various structural and non-structural measures. Comparison of different measures and evaluation of their impacts is based on the multiple criteria. If they are described spatially, decision-making problem can be conceptualized as spatial multi criteria decision-making (MCDM). Tkach and Simonovic (1997) introduced spatial Compromise Programming (SPC) technique to account for spatial variability in flood management. Some of the criteria and preferences of the stakeholders involved with flood management are subject to uncertainty that may originate in the data, knowledge of the domain or our ability to adequately describe the decision problem. The main characteristic of flood management is the existence of objective and subjective uncertainty. Fuzzy set theory has been successfully used to address both, objective and subjective uncertainty. Bender and Simonovic (2000) incorporated vagueness and imprecision as sources of uncertainty into multi criteria decision-making in water resources. In this report a new technique named Spatial Fuzzy Compromise Programming (SFCP) has been developed to enhance our ability to address the issues related to uncertainties in spatial environment. A general fuzzy compromise programming technique, when made 2 spatially distributed, proved to be a powerful and flexible addition to the list of techniques available for decision making where multiple criteria are used to judge multiple alternatives. All uncertain variables (subjective and objective) are modeled by way of fuzzy sets. In the present study, fuzzy measures have been introduced to spatial multi criteria decision-making in the GIS environment in order to account for uncertainties. Through a case study of the Red River floodplain near the City of St. Adolphe in Manitoba, Canada, it has been illustrated that the new technique provides measurable improvement in flood management. Final results in the form of maps that shown spatial distribution of the impacts of mitigation measures on the region can be of great value to insurance industry.https://ir.lib.uwo.ca/wrrr/1004/thumbnail.jp

An investigation of the applicability of fuzzy logic techniques to real-time temporal classification for threat warning systems

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (p. 62).by Timothy James Boyd.M.S

Rough feature selection for intelligent classifiers

Abstract. The last two decades have seen many powerful classification systems being built for large-scale real-world applications. However, for all their accuracy, one of the persistent obstacles facing these systems is that of data dimensionality. To enable such systems to be effective, a redundancy-removing step is usually required to pre-process the given data. Rough set theory offers a useful, and formal, methodology that can be employed to reduce the dimensionality of datasets. It helps select the most information rich features in a dataset, without transforming the data, all the while attempting to minimise information loss during the selection process. Based on this observation, this paper discusses an approach for semantics-preserving dimensionality reduction, or feature selection, that simplifies domains to aid in developing fuzzy or neural classifiers. Computationally, the approach is highly efficient, relying on simple set operations only. The success of this work is illustrated by applying it to addressing two real-world problems: industrial plant monitoring and medical image analysis.

A sensor fusion layer to cope with reduced visibility in SLAM

Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained

A sensor fusion layer to cope with reduced visibility in SLAM

Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained

On nearness measures in fuzzy relational data models

AbstractIt has been widely recognized that the imprecision and incompleteness inherent in real-world data suggest a fuzzy extension for information management systems. Various attempts to enhance these systems by fuzzy extensions can be found in the literature. Varying approaches concerning the fuzzification of the concept of a relation are possible, two of which are referred to in this article as the generalized fuzzy approach and the fuzzy-set relation approach. In these enhanced models, items can no longer be retrieved by merely using equality-check operations between constants; instead, operations based on some kind of nearness measures have to be developed. In fact, these models require such a nearness measure to be established for each domain for the evaluation of queries made upon them. An investigation of proposed nearness measures, often fuzzy equivalences, is conducted. The unnaturalness and impracticality of these measures leads to the development of a new measure: the resemblance relation, which is defined to be a fuzzified version of a tolerance relation. Various aspects of this relation are analyzed and discussed. It is also shown how the resemblance relation can be used to reduce redundancy in fuzzy relational database systems

Segmenting breast cancerous regions in thermal images using fuzzy active contours

Breast cancer is the main cause of death among young women in developing countries. The human body temperature carries critical medical information related to the overall body status. Abnormal rise in total and regional body temperature is a natural symptom in diagnosing many diseases. Thermal imaging (Thermography) utilizes infrared beams which are fast, non-invasive, and non-contact and the output created images by this technique are flexible and useful to monitor the temperature of the human body. In some clinical studies and biopsy tests, it is necessary for the clinician to know the extent of the cancerous area. In such cases, the thermal image is very useful. In the same line, to detect the cancerous tissue core, thermal imaging is beneficial. This paper presents a fully automated approach to detect the thermal edge and core of the cancerous area in thermography images. In order to evaluate the proposed method, 60 patients with an average age of 44/9 were chosen. These cases were suspected of breast tissue disease. These patients referred to Tehran Imam Khomeini Imaging Center. Clinical examinations such as ultrasound, biopsy, questionnaire, and eventually thermography were done precisely on these individuals. Finally, the proposed model is applied for segmenting the proved abnormal area in thermal images. The proposed model is based on a fuzzy active contour designed by fuzzy logic. The presented method can segment cancerous tissue areas from its borders in thermal images of the breast area. In order to evaluate the proposed algorithm, Hausdorff and mean distance between manual and automatic method were used. Estimation of distance was conducted to accurately separate the thermal core and edge. Hausdorff distance between the proposed and the manual method for thermal core and edge was 0.4719 ± 0.4389, 0.3171± 0.1056 mm respectively, and the average distance between the proposed and the manual method for core and thermal edge was 0.0845± 0.0619, 0.0710 ± 0.0381 mm respectively. Furthermore, the sensitivity in recognizing the thermal pattern in breast tissue masses is 85 % and its accuracy is 91.98 %.A thermal imaging system has been proposed that is able to recognize abnormal breast tissue masses. This system utilizes fuzzy active contours to extract the abnormal regions automatically