Sharif Digital Repository

In the presence of modeling errors, the mainstream Bayesian methods seldom give a realistic account of uncertainties as they commonly underestimate the inherent variability of parameters. This problem is not due to any misconceptions in the Bayesian framework since it is robust with respect to the modeling assumptions and the observed data. Rather, this issue has deep roots in users’ inability to develop an appropriate class of probabilistic models. This paper bridges this significant gap, introducing a novel Bayesian hierarchical setting, which breaks time-history vibration responses into several segments so as to capture and identify the variability of inferred parameters over the... 

This paper presents a hierarchical Bayesian modeling framework for the uncertainty quantification in modal identification of linear dynamical systems using multiple vibration data sets. This novel framework integrates the state-of-the-art Bayesian formulations into a hierarchical setting aiming to capture both the identification precision and the variability prompted due to modeling errors. Such developments have been absent from the modal identification literature, sustained as a long-standing problem at the research spotlight. Central to this framework is a Gaussian hyper probability model, whose mean and covariance matrix are unknown, encapsulating the uncertainty of the modal parameters.... 

In this paper, a hierarchical predictive controller is designed in order to solve the tracking problem of a moving ground target by a quad-rotor in an unknown and uneven environment. This controller has internal and external predictive controller levels. In the lower layer of the controller, a constrained predictive controller is designed that is capable of rejecting perturbations and quickly tracking the reference path, and in the outer loop, a model predictive controller is designed to optimally detect the moving ground target where, the sub-cost functions were defined so that the quad-rotor would be able to track the moving ground target even if it was temporarily out of sight of the... 

In this paper, a hierarchical predictive controller is designed in order to solve the tracking problem of a moving ground target by a quad-rotor in an unknown and uneven environment. This controller has internal and external predictive controller levels. In the lower layer of the controller, a constrained predictive controller is designed that is capable of rejecting perturbations and quickly tracking the reference path, and in the outer loop, a model predictive controller is designed to optimally detect the moving ground target where, the sub-cost functions were defined so that the quad-rotor would be able to track the moving ground target even if it was temporarily out of sight of the... 

In hierarchical service networks, facilities at different levels provide different types of service. For example, in health care systems, general centers provide low-level services, such as primary health care, while specialized hospitals provide high-level services. Because of the demand congestion at service networks, the location of servers and their allocation of demand nodes can have a strong impact on the length of the queue at each server, as well as on the response time to service calls. This study attempts to develop hierarchical location-allocation models for congested systems by employing a queueing theory in a fuzzy framework. The parameters of each model are approximately... 

Stochastic activity networks (SANs) are a powerful and flexible extension of Petri nets. These models can be used for the modelling and analysis of various kinds and different aspects of distributed real-time systems. Similar to other classical extensions of Petri nets, SANs have some limitations for modelling complex and large-scale systems. In order to remove some of these limitations and provide high-level modelling constructs, we have defined a new extension for SANs, called hierarchical stochastic activity networks (HSANs). HSAN models provide a construct for composing a hierarchy of SAN submodels that is called macro activity. HSANs encapsulate hierarchies and a key benefit of these... 

The aim of this paper is developing a new hierarchical model to evaluate and rank the sawability (power consumption) of carbonate rock with the use of effective and major criteria, and simultaneously taking subjective judgments of decision makers into consideration. The proposed approach is based on the combination of Fuzzy Analytic Hierarchy Process (FAHP) method with TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods. FAHP is used for determining the weights of the criteria by decision makers, and then rankings of carbonate rocks are determined by TOPSIS. The proposed method is applied for Iranian ornamental stone to evaluate the power consumption in rock... 

Aim of study: Glioblastoma Multiforme (GBM) is an aggressive brain cancer in adults that kills most patients in the first year due to ineffective treatment. Different clinical, biomedical, and image data features are needed to analyze GBM, increasing complexities. Besides, they lead to weak performances for machine learning models due to ignoring physicians' knowledge. Therefore, this paper proposes a hierarchical model based on Fuzzy C-mean (FCM) clustering, Wrapper feature selection, and twelve classifiers to analyze treatment plans. Methodology/Approach: The proposed method finds the effectiveness of previous and current treatment plans, hierarchically determining the best decision for...