Active Vibration Control of Launch Vehicle on Satellite Using Piezoelectric Stack Actuator

Satellites are subject to various severe vibration during different phases of flight. The concept of satellite smart adapter is proposed in this study to achieve active vibration control of launch vehicle on satellite. The satellite smart adapter has 18 active struts in which the middle section of each strut is made of piezoelectric stack actuator. Comprehensive conceptual design of the satellite smart adapter is presented to indicate the design parameters, requirements and philosophy applied which are based on the reliability and durability criterions to ensure successful functionality of the proposed system, also fabrication process of the proposed piezoelectric stack actuator is discussed in detail. The coupled electromechanical virtual work equation for the piezoelectric stack actuator in each active strut is derived by applying D'Alembert's principle to calculate the consistent mass matrix, the stiffness matrix and the load vector using finite element approximation. Modal analysis is performed to characterize the inherent properties of the smart adapter and extraction of a mathematical model of the system. Active vibration control analysis was conducted using fuzzy logic control with triangular membership functions and acceleration feedback. The control results conclude that the proposed satellite smart adapter configuration which benefits from piezoelectric stack actuator as elements of its 18 active struts has high strength and shows excellent robustness and effectiveness in vibration suppression of launch vehicle on satellite.Comment: 11 pages, 14 figures, 3 tables, 14 equation

Fuzzy linear assignment problem: an approach to vehicle fleet deployment

This paper proposes and examines a new approach using fuzzy logic to vehicle fleet deployment. Fleet deployment is viewed as a fuzzy linear assignment problem. It assigns each travel request to an available service vehicle through solving a linear assignment matrix of defuzzied cost entries. Each cost entry indicates the cost value of a travel request that "fuzzily aggregates" multiple criteria in simple rules incorporating human dispatching expertise. The approach is examined via extensive simulations anchored in a representative scenario of taxi deployment, and compared to the conventional case of using only distances (each from the taxi position to the source point and finally destination point of a travel request) as cost entries. Discussion in the context of related work examines the performance and practicality of the proposed approach

A New Geospatial Model Integrating a Fuzzy Rule-Based System in a GIS Platform to Partition a Complex Urban System in Homogeneous Urban Contexts

Here, we present a new unsupervised method aimed at obtaining a partition of a complex urbansysteminhomogenousurbanareas,calledurbancontexts.Ourmodelintegratesspatialanalysis processes and a fuzzy rule-based system applied to manage the knowledge of domain experts; it is implemented using a GIS platform. The area of study is initially partitioned in microzones, homogeneous portions of the urban system, which are the atomic reference elements for the census data. With the contribution of domain experts, we identify the physical, morphological, environmental, and socio-economic indicators needed to identify synthetic characteristics of urban contexts and create the fuzzy rule set necessary for determining the type of urban context. We implement the set of spatial analysis processes required to calculate the indicators for the microzones and apply a Mamdani fuzzy rule system to classify the microzones. Finally, the partition of the area of study in urban contexts is obtained by dissolving continuous microzones belonging to the same type of urban context. Tests are performed on the Municipality of Pozzuoli (Naples, Italy); the reliability of the out model is measured by comparing the results with the ones obtained through a detailed analysis

Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences

Mathematical fuzzy logic (MFL) specifically targets many-valued logic and has significantly contributed to the logical foundations of fuzzy set theory (FST). It explores the computational and philosophical rationale behind the uncertainty due to imprecision in the backdrop of traditional mathematical logic. Since uncertainty is present in almost every real-world application, it is essential to develop novel approaches and tools for efficient processing. This book is the collection of the publications in the Special Issue “Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences”, which aims to cover theoretical and practical aspects of MFL and FST. Specifically, this book addresses several problems, such as:- Industrial optimization problems- Multi-criteria decision-making- Financial forecasting problems- Image processing- Educational data mining- Explainable artificial intelligence, etc

The attractiveness of countries for FDI. A fuzzy approach

This paper presents a new method for measuring the attractiveness of countries for FDI. A ranking is built using a fuzzy expert system whereby the function producing the final evaluation is not necessarily linear and the weights of the variables, usually defined numerically, are replaced by linguistic rules. More precisely, weights derive from expert opinions and from econometric tests on the determinants of countries’ FDI. As a second step, the view-point of investors from two different investing economies, the UK and Italy, are taken into account. Country-specific factors, such as the geographic, cultural and institutional distances existing between the investing and the partner economies are included in the analysis. This shows how the base ranking changes with the investor’s perspective.foreign direct investments; fuzzy expert systems; attractiveness

Pre-earthquake fuzzy logic-based rapid hazard assessment of reinforced concrete buildings

The main purpose of this paper is to present a rapid building assessment fuzzy logic (FL) modelling for risk assessment based on expert construction engineering verbal informatics. Before an earthquake, a set of input expert assessment variables are transformed into five types of hazard categorization as "no damage", "slight damage", "moderate damage", "severe damage", and "collapse". Main variables are reported by expert engineers based on visual inspection of structural components in addition to the building location's peak ground velocity (PGV) micro zonation numerical value, soil type and building's material information. Each input variable and output hazard class is fuzzified. A valid set of fuzzy rule base components is written based on input variables, each of which has an appropriate output hazard class. The fuzzy hazard assessment model has input and output variables in terms of fuzzy sets. Thus, the overall model output is in the form of a fuzzy set and then defuzzified to find the percentage of each hazard class for a single building. The application of this fuzzy logic model is presented for twenty existing reinforced concrete buildings, and the final hazard categories of these buildings are presented with interpretations and recommendations.Istanbul Medipol Universit

New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use

The Attractiveness of Countries for FDI. A Fuzzy Approach

This paper presents a new method for measuring the attractiveness of countries for FDI. A ranking is built using a fuzzy expert system whereby the function producing the final evaluation is not necessarily linear and the weights of the variables, usually defined numerically, are replaced by linguistic rules. More precisely, weights derive from expert opinions and from econometric tests on the determinants of countries’ FDI. As a second step, the view-point of investors from two different investing economies, the UK and Italy, are taken into account. Country-specific factors, such as the geographic, cultural and institutional distances existing between the investing and the partner economies are included in the analysis. This shows how the base ranking changes with the investor’s perspectiveforeign direct investments; fuzzy expert systems; attractiveness;

Traffic accident predictions based on fuzzy logic approach for safer urban environments, case study: İzmir Metropolitan Area

Thesis (Doctoral)--Izmir Institute of Technology, City and Regional Planning, Izmir, 2009Includes bibliographical references (leaves: 83-88)Text in English; Abstract: Turkish and Englishxii, 119, leavesDissertation has dealt with one of the most chaotic events of an urban life that is the traffic accidents. This study is a preliminary and an explorative effort to establish an Accident Prediction Model (APM) for road safety in İzmir urban environment. Aim of the dissertation is to prevent or decrease the amount of possible future traffic accidents in İzmir metropolitan region, by the help of the developed APM. Urban traffic accidents have spatial and other external reasons independent from the vehicles or drivers, and these reasons can be predicted by mathematical models. The study deals with the factors of the traffic accidents, which are not based on the human behavior or vehicle characteristics. Therefore the prediction model is established through the following external factors, such as traffic volume, rain status and the geometry of the roads. Fuzzy Logic Modeling (FLM) is applied as a prediction tool in the study. Familiarizing fuzzy logic approach to the planning discipline is the secondary aim of the thesis and contribution to the literature. The conformity of fuzzy logic enables modeling through verbal data and intuitive approach, which is important to achieve uncertainties of planning issues

Breast Tissue Classification via Interval Type 2 Fuzzy Logic Based Rough Set

BIRADS is a Breast Imaging, Reporting and Data System. A tool to standardize mammogram reports and minimizes ambiguity during mammogram image evaluation. Classification of BIRADS is one of the most challenging tasks to radiologist. An apt treatment can be administered to the patient by the oncologist upon acquiring sufficient information at BIRADS stage. This study aspired to build a model, which classifies BIRADS using mammograms images and reports. Through the implementation of type-2 fuzzy logic as classifier, an automatically generated rules will be applied to the model. Comparison of accuracy, specificity and sensitivity of the modal will be performed vis-à-vis rules given by the experts. The study encompasses a number of steps beginning with collection of the data from Radiology Department of National University of Malaysia Medical Center. The data was initially processed to remove noise and gaps. Then, an algorithm developed by selecting type-2 fuzzy logic using Mamdani model. Three types of membership functions were employed in the study. Among the rules that used by the model were obtained from experts as well as generated automatically by the system using rough set theory. Finally, the model was tested and trained to get the best result. The study shows that triangular membership function based on rough set rules obtains 89% whereas expert driven rules gains about 78% of accuracy rates. The sensitivity using expert rules is 98.24% whereas rough set rules obtained 93.94%. Specificity for using expert rules and rough set rules are 73.33%, 84.34% consecutively. Conclusion: Based on statistical analysis, the model which employed rules generated automatically by rough set theory fared better in comparison to the model using rules given by the experts.