Sharif Digital Repository

In this paper, the problem of statistical reconstruction and prediction of chaotic systems with unknown governing equations using stochastic Markov models is investigated. Using the time series of only one measurable state, an algorithm is proposed to design any orders of Markov models and the approach is state transition matrix extraction. Using this modeling, two goals are followed: first, using the time series, statistical reconstruction is performed through which the probability density and conditional probability density functions are reconstructed; and second, prediction is performed. For this problem, some estimators are required and here the maximum likelihood and the conditional... 

A new heuristic controller, called the continuous ant colony controller, is proposed for non-linear stochastic Gaussian/non-Gaussian systems. The new controller formulates the state estimation and the model predictive control problems as a single stochastic dynamic optimization problem, and utilizes a colony of virtual ants to find and track the best estimated state and the best control signal. For this purpose, an augmented state space is defined. An integrated cost function is also defined to evaluate the points of the augmented state space, explored by the ants. This function minimizes simultaneously the state estimation error, tracking error, control effort and control smoothness. Ants... 

This paper develops a stochastic geometry-based approach for the modeling and analysis of finite millimeter wave (mmWave) wireless networks where a random number of transmitters and receivers are randomly located inside a finite region. We consider a selection strategy to serve a reference receiver by the transmitter providing the maximum average received power among all transmitters. In our system model, we employ the unique features of mmWave communications such as directional transmit and receive beamforming and different channels for line-of-sight (LOS) and non-line-of-sight (NLOS) links. Accordingly, deploying a blockage process suitable for mmWave networks, we study the coverage... 

Continuous-time Stochastic Game (CTSG) can be seen as a proper model to analyze probabilistic, non-deterministic, and competitive behaviors as found in Stochastic Multi-Player Game (SMG). The difference is that in an SMG the system under design is analyzed in the discrete-time setting. In comparison, in a CTSG, the system transitions occur in the continuous-time setting with exponentially distributed delays. This paper focuses on the model checking of CTSGs and presents a uniformization-based algorithm, called TPA, to approximate the time-bounded reachability probabilities with an arbitrary error bound. We concentrate on CTSGs with bounded transition rates. To illustrate the strength points... 

In this paper, a method for apnea bradycardia detection in preterm infants is presented based on coupled hidden semi Markov model (CHSMM). CHSMM is a generalization of hidden Markov models (HMM) used for modeling mutual interactions among different observations of a stochastic process through using finite number of hidden states with corresponding resting time. We introduce a new set of equations for CHSMM to be integrated in a detection algorithm. The detection algorithm was evaluated on a simulated data to detect a specific dynamic and on a clinical dataset of electrocardiogram signals collected from preterm infants for early detection of apnea bradycardia episodes. For simulated data, the... 

This article presents a mixed-integer linear stochastic model for the optimal expansion planning of electricity distribution networks and distributed generation (DG) units. In the proposed framework, autonomous DG units are aggregated and modeled using the well-known energy hub concept. In this model, the uncertainties of heat and electricity demand as well as renewable generation are represented using various scenarios. Although this is a standard technique to capture the uncertainties, it drastically increases the dimensions of this optimization problem and makes it practically intractable. In order to address this issue, a novel iterative method is developed in this article to enhance the... 

Despite that the border between chaotic and stochastic systems is exactly defined, scientists, use high dimensional chaotic dynamics to model numerous stochastic models and sometimes use stochastic models to study chaotic systems. In this paper, we have investigated chaotic systems with a stochastic approach and proposed an estimator for the chaotic system which is used to present different algorithms for chaos control, extreme event prediction and extreme event mitigation. The stochastic estimator is constructed by meshing the phase space and applying the cell mapping method (with some considerations) which provides us with a model-free approximation of the systems. The algorithms are ideal... 

Capacitance-Resistive Model (CRM), as a fast yet efficient proxy model, suffers from some limitations in modeling relatively complex reservoirs. Some current improvements on this proxy made it a more powerful simulator with updating parameters over time. However, the model's intrinsic uncertainty arisen from simplifying fluid-flow modeling by some limited number of constant parameters is not addressed yet. In this study, this structural limitation of CRM has been addressed by introducing a time-correlated model error, including stochastic and non-stochastic parameters, embedded into this proxy's formulation. The error term's non-stochastic parameters have been tuned to be used in forecasting... 

This article addresses a centralized generation expansion planning problem, accounting for both long- and short-term uncertainties. The long-term uncertainty (demand growth) is modeled via a set of scenarios, while the short-term uncertainty (wind power generation) is described by a family of probability distributions with the same first- and second-order moments obtained from historical data. The resulting model is a distributionally robust chance-constrained optimization problem, which selects the conventional generating units to be built among predefined discrete options. This model includes a detailed representation of unit commitment constraints. To achieve computational tractability,... 

This paper presents a stochastic model for optimal generating units' restoration planning. Restoration plans will be performed based on different objectives i.e. total system risk, total served energy in the period of restoration, and total restoration time. The proposed model is a multi objective stochastic program in which generating units' restoration plan is assumed to optimize different kinds of objective function. The proposed approach to the optimal restoration of generating units can be used by vertically integrated utilities as well as the ISOs in electricity markets. The approach coordinates the optimal start up of generating units within the restoration period. Physical... 

Soccer model and relation of players and coach has been analyzed by a learning automata-based method, called Soccer Mobility Estimator (SME), who estimates the mobility model of soccer players. During a soccer match, players play according to a certain program designed by coach. The pattern of players' mobility is not stochastic and it can be assumed that they are playing with a certain mobility model. Since knowledge about mobility model of nodes in mobile ad-hoc networks has a substantial effect on its performance evaluation, knowledge about mobility model of soccer players can be useful for coaches and experts for game analysis. In fact the mobility model of players could be an important... 

The quality of an architectural design of a software system has a great influence on achieving non-functional requirements of a system. A regular software development project is often influenced by non-functional factors such as the customers' expectations about the performance and reliability of the software as well as the reduction of underlying risks. The evaluation of non- functional parameters of a software system at the early stages of design and its development process are often considered as major factors in dealing with these issues. Because these evaluations can help us to choose the most proper model which is the securest and the most reliable.In this paper, a method is presented... 

In this paper, the evolution of elastic–plastic damage in the composite laminates under fatigue conditions is modeled. Continuum damage mechanics (CDM) has been coupled with the bridge micromechanics model to estimate the fatigue damage and life for laminated composite structures. Based on the elastic–plastic bridging model, three damage variables are defined. These variables estimate the fiber, matrix, and fiber/matrix damage response at the ply scale. To model the beginning of plastic deformation, a yield function is utilized, and evolution equations of the damage variables are obtained. Then the developed deformation plastic model is calculated. The model parameters are calibrated by... 

Companies depend on mining data to grow their business more than ever. To achieve optimal performance of Big Data analytics workloads, a careful configuration of the cluster and the employed software framework is required. The lack of flexible and accurate performance models, however, render this a challenging task. This paper fills this gap by presenting accurate performance prediction models based on Stochastic Activity Networks (SANs). In contrast to existing work, the presented models consider multiple work queues, a critical feature to achieve high accuracy in realistic usage scenarios. We first introduce a monolithic analytical model for a multi-queue YARN cluster running DAG-based Big... 

This paper presents a new model for optimal scheduling of renewable-based multi-energy microgrid (MEM) systems incorporated with emerging high-efficient technologies such as electric vehicle (EVs) parking lots, power-to-gas (P2G) facility, and demand response programs. The proposed MEM is equipped with wind energy, multi-carrier energy storage technologies, boiler, combined heat and power unit, P2G, EVs, and demand response with the aim of total operational cost minimization. Meanwhile, the system operator can participate in three electricity, heat, and gas market to meet local demands as well as achieve desired profits through energy exchanges. The proposed MEM is exposed to high-level... 

Capacitance-Resistive Model (CRM), as a fast yet efficient proxy model, suffers from some limitations in modeling relatively complex reservoirs. Some current improvements on this proxy made it a more powerful simulator with updating parameters over time. However, the model's intrinsic uncertainty arisen from simplifying fluid-flow modeling by some limited number of constant parameters is not addressed yet. In this study, this structural limitation of CRM has been addressed by introducing a time-correlated model error, including stochastic and non-stochastic parameters, embedded into this proxy's formulation. The error term's non-stochastic parameters have been tuned to be used in forecasting... 

Carbon nanotubes (CNTs) are promising candidates for high-resolution mass nanosensors owing to their unique vibrational behavior. The structural characteristic (e.g. defect type and density) and working temperature have a significant effect on the natural frequency of CNT-based sensors. Herein, a stochastic approach based on novel finite element and molecular mechanics simulations is implemented to model the effect of temperature and structural characteristics of single-wall CNTs including defects (vacancy defect with different densities) and chirality (zigzag and armchair) on their vibrational behavior. The results show that the vacancy defects exert a significant deterioration of the... 

Road roughness-induced vibrations are the main source of fatigue damage accumulation in vehicles. Hence, road surface profile modeling and simulation are of high importance when it comes to vehicle fatigue damage assessment. This research focuses on uncovering the statistical distributions that describe the characteristics of road loads that affect fatigue damage accumulation. Particle filtering is deployed to estimate the log-volatility of the road surface profile by assuming a random walk behavior for the hidden log-volatility. The skew-normal distribution with three parameters is fitted to the estimated log-volatility. The inferred parameters are used for synthesizing artificial road... 

Data-to-Text Generation (D2T) is one of the most important sub-fields of Natural Language Generation where structured data is transcribed into natural language text. Several solutions have been proposed for D2T so far with relative success, including template-based, phrase structure grammar-based, and neural attention models. However, these methods also have problems such as grammatical flaws, limited naturalness, and semantic deficiencies. In this work, we propose a stochastic corpus-based model for the data-to-text generation that produces a tree-form structure for sentences based on dependency information. This information includes the dependency relations between words and meaning labels... 

Due to the accelerated climate change, it is anticipated that the number and severity of natural disasters such as hurricanes, blizzards, and floods will be increased in the coming years. In this regard, this paper presents a distribution system planning model to improve the system resilience against hurricane. A scenario-based mathematical model is proposed to capture the random nature of weather events. Moreover, a stochastic optimization model is developed to simultaneously harden the distribution lines and place different types of distributed generation (DG) units such as microturbines (MTs), wind turbines (WTs), and photovoltaic cells (PVs). The conditional value at risk (CVaR) is used...