Sharif Digital Repository

Lithium-ion batteries should continuously be operated at the optimum temperature range (15∼40∘C) for the best performance. Surface temperature monitoring is critical for the safe and efficient operation of the battery. In this study, initially, the electrical parameters of the battery are determined by applying a second-order equivalent circuit model. This model then is integrated with a thermal model based on the temperature dependent behavior of the electrical parameters and the heat generated. The input parameters to the electro-thermal model include the current, the ambient fluid temperature and the output parameters include the terminal voltage, state of charge, cell core temperature... 

Nowadays, Autonomous Underwater Vehicles (AUV) are used in environmental studies, ocean floor mapping, and measuring water properties. Navigation of these vehicles is one of the most challenging issues due to the unavailability of global positioning system (GPS) signal underwater. Inertial navigation is a method commonly used for underwater navigation. If a low-cost Inertial Measurement Unit (IMU) is used, navigation quality will decline rapidly due to sensor inherent error. Although using a Doppler Velocity Log (DVL) speedometer sensor helps limit this error to some extent, it does not yield acceptable accuracy in low-cost IMUs. Filtering the gyro based on the AUV rotational dynamics model... 

In the List Coloring problem, the input is a graph G and list of colors L: V(G) → N for each vertex v∈ V(G). The objective is to test the existence of a coloring λ: V(G) → N such that for each v∈ V(G), λ(v) ∈ L(v) and for each edge (u, v) ∈ E(G), λ(u) ≠ λ(v). Fiala et al. (TCS 2011) proved that List Coloring is W[1]-hard when parameterized by the vertex cover number of the input graph. Recently, Gutin et al. (STACS 2020, SIDMA 2021) designed an O∗(2 k) time randomized algorithm for List Coloring where k is the size of the given clique modulator of the input graph. Since List Coloring is W[1]-hard parameterized by the vertex cover number, List Coloring is W[1]-hard parameterized by the size... 

In the Partial Vertex Cover (PVC) problem, we are given an n-vertex graph G and a positive integer k, and the objective is to find a vertex subset S of size k maximizing the number of edges with at least one end-point in S. This problem is W[1]-hard on general graphs, but admits a parameterized subexponential time algorithm with running time 2O(k)nO(1) on planar and apex-minor free graphs [Fomin et al. (FSTTCS 2009, IPL 2011)], and a kO(k)nO(1) time algorithm on bounded degeneracy graphs [Amini et al. (FSTTCS 2009, JCSS 2011)]. Graphs of bounded degeneracy contain many sparse graph classes like planar graphs, H-minor free graphs, and bounded tree-width graphs (see Fig. 1). In this work, we... 

This paper addresses the problem of robust parameter estimation of simple linear profiles in multistage processes in the presence of outliers in Phase I. In this regard, two robust approaches, namely the Huber’s M-estimator and the MM estimator, are proposed to estimate the parameters of the process in Phase I in the presence of outliers in historical data. In addition, the U statistic is applied to the robust parameter estimates to remove the effect of the cascade property in multistage processes and as a result, to obtain adjusted robust estimates of the parameters of simple linear profiles. The performance of the proposed methods is evaluated under weak and strong autocorrelations... 

In this study, the traffic parameters were collected from three work zones in Iran in order to evaluate the queue length in the work zones. The work zones were observed at peak and non-peak hours. The results showed that abrupt changes in Freeway Free Speed (FFS) and arrival flow rate caused shockwaves and created a bot-tleneck in that section of the freeway. In addition, accel-eration reduction, abrupt change in the shockwave speed, abrupt change in the arrival flow rate and increase in the percentage of heavy vehicles have led to extreme queue lengths and delay. It has been found that using daily traffic data for scheduling the maintenance and rehabilita-tion projects could diminish the... 

Unlike a single-body approach, modeling based on a two-body approach has been employed to prepare the required system dynamic model as a time update equation in the applied filtering technology and measurement data for the estimation process. This more precise mathematical model enabled better understanding about the dynamics of the change in the aircraft mass properties during the airdropping operation. The problem is defined as estimation of the optimal mass properties parameters for the best possible fit of the model output to the real data. The parameter estimation problem is investigated by a nonlinear filtering methodology in two sequential steps. In the first step, the single extended... 

Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In this study to tackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology based on the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neural network (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and... 

Several reasons such as no free lunch theorem indicates that any learning algorithm in combination with a specific feature selection (FS) technique may give more accurate estimation than other learning algorithms. Therefore, there is not a universal approach that outperforms other algorithms. Moreover, due to the large number of FS techniques, some recommended solutions such as using synthetic dataset or combining different FS techniques are very tedious and time consuming. In this study to tackle the issue of more accurate estimation of NPPs parameters, the performance of the major supervised learning algorithms in combination with the different FS techniques which are appropriate for... 

In this study, accurate estimation of nuclear power plant (NPP) parameters is done using the new and simple technique. The proposed technique using the genetic algorithm (GA) in combination with the Bayesian regularization (BR) and Levenberg- Marquardt (LM) learning algorithms identifies the appropriate architecture for estimation of the target parameters. In the first step, the input patterns features are selected using the features selection (FS) technique. In the second step, the appropriate number of hidden neurons and hidden layers are investigated to provide a more efficient initial population of the architectures. In the third step, the estimation of the target parameter is done using... 

The classical methods utilized for modeling nano-scale systems are not practical because of the enlarged surface e ects that appear at small dimensions. Contrarily, implementing more accurate methods is followed by prolonged computations as these methods are highly dependent on quantum and atomistic models, and they can be employed for very small sizes in brief time periods. In order to speed up the Molecular Dynamics (MD) simulations of the silicon structures, Coarse-Graining (CG) models are put forward in this research. The procedure involves establishing a map between the main structure's atoms and the beads comprising the CG model and modifying the parameters of the system so that the... 

At least four source impedances are required to characterize the noise parameters of a two-port network. The accuracy of the noise parameters depends on the configuration of the employed source impedances (pattern). The configuration of a specified pattern, generated generally by an impedance tuner, changes with frequency, and as such, the accuracy is usually degraded in a wideband characterization. To realize a low-error wideband noise characterization, the present article thoroughly investigates the method of choosing a four-source impedance pattern. A Monte Carlo simulation-based approach is proposed to obtain the desired impedance patterns. The measurement and simulation results show... 

Localization-capable inertial motion capture algorithms rely on zero-velocity updates (ZUPT), usually as measurements in a Kalman filtering scheme, for position and attitude error control. As ZUPTs are only applicable during the static phases a link goes through, estimation errors grow during dynamic ones. This error growth may somewhat be mitigated by imposing biomechanical constraints in multi-sensor systems. Error reduction is also possible by optimization-based methods that incorporate the dynamic and static constraints governing the system behavior over a period of time (e.g. the dynamic network algorithm); when this period includes multiple static phases for a link, its estimation... 

We consider distributed statistical optimization in one-shot setting, where there are m machines each observing n i.i.d. samples. Based on its observed samples, each machine sends a B-bit-long message to a server. The server then collects messages from all machines, and estimates a parameter that minimizes an expected convex loss function. We investigate the impact of communication constraint, B, on the expected error and derive a tight lower bound on the error achievable by any algorithm. We then propose an estimator, which we call Multi-Resolution Estimator (MRE), whose expected error (when B ≥ d log mn where d is the dimension of parameter) meets the aforementioned lower bound up to a... 

Particle Swarm Optimization (PSO) algorithm is a nature-inspired meta-heuristic that has been utilized as a powerful optimization tool in a wide range of applications since its inception in 1995. Due to the flexibility of its parameters and concepts, PSO has appeared in many variants, probably more than any other meta-heuristic algorithm. This paper introduces the Generalized Particle Swarm Optimization (GEPSO) algorithm as a new version of the PSO algorithm for continuous space optimization, which enriches the original PSO by incorporating two new terms into the velocity updating equation. These terms aim to deepen the interrelations of particles and their knowledge sharing, increase... 

The huge availability of data in the last decade has raised the opportunity for the better use of data in decision-making processes. The idea of using the existing data to achieve a more coherent reality solution has led to a branch of optimization called data-driven optimization. On the one hand, the presence of uncertain variables in these datasets makes it crucial to design robust optimization methods in this area. On the other hand, in many real-world problems, the closed-form of the objective function is not available and a meta-model based framework is necessary. Motivated by the above points, in this paper a Gaussian process is used in a Bayesian optimization framework to design a... 

Compute-and-forward is a promising strategy to tackle interference and obtain high rates between the transmitting users in a wireless network. However, the quality of the wireless channels between the users substantially limits the achievable computation rate in such systems. In this paper, we introduce the idea of using intelligent reflecting surfaces (IRSs) to enhance the computing capability of the compute-and-forward systems. For this purpose, we consider a multiple access channel (MAC) where a number of users aim to send data to a base station (BS) in a wireless network, where the BS is interested in decoding a linear combination of the data from different users in the corresponding... 

A viscoelastic microcantilever beam is analytically analyzed based on the modified strain gradient theory. Kelvin-Voigt scheme is used to model beam viscoelasticity. By applying Euler-Bernoulli inextensibility of the centerline condition based on Hamilton's principle, the nonlinear equation of motion and the related boundary conditions are derived from shortening effect theory and discretized by Galerkin method. Inner damping, nonlinear curvature effect, and nonlinear inertia terms are also taken into account. In the present study, the generalized derived formulation allows modeling any nonlinear combination such as nonlinear terms that arise due to inertia, damping, and stiffness, as well... 

Online selection of the appropriate model and identifying its parameters based on measured vibrational data are among the challenging issues in dynamic system identification. After a severe earthquake, quick monitoring and assessment of structural health status play a crucial role in effective critical risk management for the building owners and decision-makers. The Bayesian multiple modeling approach is a suitable tool for optimal model class selection, which is used in this article mainly for improving data fitting precision, decreasing dimensions of structural unknown vector through removing unnecessary parameters, detecting the occurrence and type of predominant phenomenon related to... 

Rapid assessment of structural safety and performance right after the occurrence of significant earthquake shaking is crucial for building owners and decision-makers to make informed risk management decisions. Hence, it is vital to develop online and pseudo-online health monitoring methods to quantify the health of the building right after significant earthquake shaking. Many Bayesian inference–based methods have been developed in the past which allow the users to estimate the unknown states and parameters. However, one of the most challenging part of the Bayesian inference–based methods is the determination of the parameter noise covariance matrix. It is especially difficult when the number...