Sharif Digital Repository

A utility function is an important tool for representing a DM’s preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model. © 2017, The Author(s)  

The human brain continuously tries to predict sensory input in order to prepare for responding to new events. The brain develops a model for the incoming sensory information and updates it as new inputs arrive. It is hypothesized that the brain deduces a distribution for the input which is made more accurate with new observations. A notable question is how the brain perceives and reacts to new information. The oddball paradigm task is a simple experiment that can reveal the brain's ability in predicting the incoming input. We analyzed the EEG response of the brain recorded during oddball visual and auditory tasks in order to characterize its response to surprising instances embedded in a... 

Semantic video analysis and automatic concept extraction play an important role in several applications; including content-based search engines, video indexing, and video summarization. As the Bayesian network is a powerful tool for learning complex patterns, a novel Bayesian network-based method is proposed for automatic event detection and summarization in soccer videos. The proposed method includes efficient algorithms for shot boundary detection, shot view classification, mid-level visual feature extraction, and construction of the related Bayesian network. The method contains of three main stages. In the first stage, the shot boundaries are detected. Using the hidden Markov model, the... 

Classifying streams of data, for instance financial transactions or emails, is an essential element in applications such as online advertising and spam or fraud detection. The data stream is often large or even unbounded; furthermore, the stream is in many instances non-stationary. Therefore, an adaptive approach is required that can manage concept drift in an online fashion. This paper presents a probabilistic non-parametric generative model for stream classification that can handle concept drift efficiently and adjust its complexity over time. Unlike recent methods, the proposed model handles concept drift by adapting data-concept association without unnecessary i.i.d. assumption among the... 

In this paper, we propose a Bayesian Pursuit algorithm for sparse representation. It uses both the simplicity of the pursuit algorithms and optimal Bayesian framework to determine active atoms in sparse representation of a signal. We show that using Bayesian Hypothesis testing to determine the active atoms from the correlations leads to an efficient activity measure. Simulation results show that our suggested algorithm has better performance among the algorithms which have been implemented in our simulations in most of the cases. ©2009 IEEE  

Automated measurement of the intensity of spontaneous facial Action Units (AU) defined by the Facial Action Coding System (FACS) in video sequences is a challenging problem. This paper proposes a person-adaptive methodology for the intensity estimation of spontaneous AUs. We formulate this problem as a source separation problem where we consider the observed AUs as the source signals to be separated from each other and other information given by a sequence of facial images. We first compute an initial estimation of the sources, called observations, using sparse linear regression functions. We then develop and apply a Bayesian source separation method that recruits the prior information of... 

Tolerances significantly affect the assemblability of components, the product's performance, and manufacturing cost in mechanical assemblies. Despite the importance of product reliability assessment, the reliability-based tolerance design of mechanical assemblies has not been previously considered in the literature. In this paper, a novel method based on Bayesian modeling is proposed for the tolerance-reliability analysis and allocation of complex assemblies where the explicit assembly functions are difficult or impossible to extract. To reach this aim, a Bayesian model is developed for tolerance-reliability analysis. Then, a multi-objective optimization formulation is proposed for obtaining... 

Line scratches are common artefacts in old motion pictures that may produce a very annoying effect on the viewer. In this paper, we will introduce a new method for fast detection and high quality restoration of this kind of degradation. Our detection method relies on the statistical characteristics of the intensity of pixels in the scratched areas of frames. For high quality restoration of the scratches we use a mixed adaptive stochastic AR-Median model to restore the scratched areas. The proposed detection algorithm is fast and more efficient compared to the other available approaches. The main contribution of our restoration algorithm is its ability to jointly adapt the image model to the... 

Precise modelling of the interaction of ions with materials is important for many applications including material characterization, ion implantation in devices, thermonuclear fusion, hadron therapy, secondary particle production (e.g. neutron), etc. In this study, a new approach using the Geant4 toolkit in combination with the Bayesian regularization (BR) learning algorithm of the feed-forward neural network (FFNN) is developed to estimate the range of ions in materials accurately and quickly. The different incident ions at different energies are interacted with the target materials. The Geant4 is utilized to model the interactions and to calculate the range of the ions. Afterward, the... 

Speech enhancement in real-time applications improves the quality and intelligibility of the speech and reduces communication fatigue. Nowadays, due to reactivity of the systems and spread of online real-time applications, including VoIP, state-space models have been used broadly. This paper presents a speech enhancement method based on adaptive Bayesian-Kalman filter and Bayesian-MAP estimation to improve the performance and the quality of the enhancement procedure. The enhancement method includes a combination of Bayesian-Kalman filter for noise reduction and Bayesian-MAP estimation for parameter estimation of the lost speech segments. Performance evaluation and result of the proposed... 

Estimation of distribution algorithms have evolved as a technique for estimating population distribution in evolutionary algorithms. They estimate the distribution of the candidate solutions and then sample the next generation from the estimated distribution. Bayesian optimization algorithm is an estimation of distribution algorithm, which uses a Bayesian network to estimate the distribution of candidate solutions and then generates the next generation by sampling from the constructed network. The experimental results show that the Bayesian optimization algorithms are capable of identifying correct linkage between the variables of optimization problems. Since the problem of finding the... 

In this paper, a heuristic threshold policy is developed to detect and classify the states of a multivariate quality control system. In this approach, a probability measure called belief is first assigned to the quality characteristics and then the posterior belief of out-of-control characteristics is updated by taking new observations and using a Bayesian rule. If the posterior belief is more than a decision threshold, called minimum acceptable belief determined using a heuristic threshold policy, then the corresponding quality characteristic is classified out-of-control. Besides using a different approach, the main difference between the current research and previous works is that the... 

The noise exists in almost all environments such as cellular mobile telephone systems. Various types of noise can be introduced such as speech additive noise which is the main factor of degradation in perceived speech quality. At some applications for example at the receiver of a telecommunication system, the direct value of interfering noise is not available and there is just access to noisy speech. In these cases the noise cannot be cancelled totally but it may be possible to reduce the noise in a sensible way by utilizing the statistics of the noise and speech signal. In this paper the proposed method for noise reduction is Bayesian recursive state-space Kalman filter, which is a method... 

In this paper, we address the problem of data description using a Bayesian framework. The goal of data description is to draw a boundary around objects of a certain class of interest to discriminate that class from the rest of the feature space. Data description is also known as one-class learning and has a wide range of applications. The proposed approach uses a Bayesian framework to precisely compute the class boundary and therefore can utilize domain information in form of prior knowledge in the framework. It can also operate in the kernel space and therefore recognize arbitrary boundary shapes. Moreover, the proposed method can utilize unlabeled data in order to improve accuracy of... 

A TI-ADC is a circuitry to achieve high sampling rates by passing the signal and its shifted versions through a number of parallel ADCs with lower sampling rates. When the time shifts between the C channels of a TI-ADC are properly tuned, the aggregate of the obtained samples is equivalent to that of a single ADC with C-times the sampling rate. However, the performance of a TI-ADC can be seriously degraded under interchannel timing mismatch. As this non-ideality cannot be avoided in practice, we need to first estimate the mismatch value, and then, compensate it. In this paper, by adopting a stochastic bandlimited signal model we study the signal recovery problem from the samples of a TI-ADC... 

The QoS attributes of Grid services play important roles in several tasks in Grid computing such as QoS-aware service composition, service negotiation, resource management, service discovery and scheduling. By considering the dynamic aspects of the Grid environments and also the uncertainty related to Grid services, in this paper, we present BNQM, a Bayesian network based probabilistic QoS Model for Grid service composition. Application of Bayesian network in QoS management makes it possible to indicate the conditional independence relationships among QoS attributes and to provide an effective probabilistic approach to predict new values for some QoS attributes while others are changed.... 

In this paper, we propose a sampling policy considering Bayesian risks. Various definitions of producer's risk and consumer's risk have been made. Bayesian risks for both producer and consumer are proven to give better information to decision-makers than classical definitions of the risks. So considering the Bayesian risk constraints, we seek to find optimal acceptance sampling policy by minimizing total cost, including the cost of rejecting the batch, the cost of inspection, and the cost of defective items detected during the operation. Proper distributions to construct the objective function of the model are specified. In order to demonstrate the application of the proposed model, we... 

Purpose: The purpose of this paper is to present a new efficient method for the tolerance–reliability analysis and quality control of complex nonlinear assemblies where explicit assembly functions are difficult or impossible to extract based on Bayesian modeling. Design/methodology/approach: In the proposed method, first, tolerances are modelled as the random uncertain variables. Then, based on the assembly data, the explicit assembly function can be expressed by the Bayesian model in terms of manufacturing and assembly tolerances. According to the obtained assembly tolerance, reliability of the mechanical assembly to meet the assembly requirement can be estimated by a proper first-order... 

We study the problem of reconstructing a block-sparse signal from compressively sampled measurements. In certain applications, in addition to the inherent block-sparse structure of the signal, some prior information about the block support, i.e., blocks containing non-zero elements, might be available. Although many block-sparse recovery algorithms have been investigated in the Bayesian framework, it is still unclear how to incorporate the information about the probability of occurrence into regularization-based block-sparse recovery in an optimal sense. In this letter, we bridge between these fields by the aid of a new concept in conic integral geometry. Specifically, we solve a weighted... 

In the binary majority voting problem, each node initially chooses between two alternative choices. The goal is to design a distributed algorithm that informs nodes which choice is in majority. In this study, the authors formulate this problem as a hypothesis testing problem and propose fixed-size and sequential solutions using classical and Bayesian approaches. In the sequential version, the proposed mechanism enables nodes to test which choice is in majority, successively in time. Hence, termination of the algorithm is embedded within it, contrary to the existing approaches which require a monitoring algorithm to indicate the termination. This property makes the algorithm more efficient in...