Sharif Digital Repository

Lung cancer is one of the most common diseases for human beings everywhere throughout the world. Early identification of this disease is the main conceivable approach to enhance the possibility of patients’ survival. In this paper, a k-Nearest-Neighbors technique, for which a genetic algorithm is applied for the efficient feature selection to reduce the dataset dimensions and enhance the classifier pace, is employed for diagnosing the stage of patients’ disease. To improve the accuracy of the proposed algorithm, the best value for k is determined using an experimental procedure. The implementation of the proposed approach on a lung cancer database reveals 100% accuracy. This implies that one... 

To address one of the most challenging industry problems, we develop an enhanced training algorithm for anomaly detection in unlabelled sequential data such as time-series. We propose the outputs of a well-designed system are drawn from an unknown probability distribution, U, in normal conditions. We introduce a probability criterion based on the classical central limit theorem that allows evaluation of the likelihood that a data-point is drawn from U. This enables the labelling of the data on the fly. Non-anomalous data is passed to train a deep Long Short-Term Memory (LSTM) autoencoder that distinguishes anomalies when the reconstruction error exceeds a threshold. To illustrate our... 

Recently, bonded composite patch repair, because of its significant advantages over traditional methods, has been highly accepted in several industries, particularly in aerospace applications. In this paper, a multi-scale finite element algorithm is proposed to simulate crack growth of repaired plates under fatigue load by considering the effects of composite micro-scale properties. The algorithm is verified through conducting an experimental set up and the proposed model is in reasonable agreement with experiments. The influences of different fiber volume fractions (VF), number of layers and fiber orientation of composite patch on the fatigue responses of adhesively bonded patch are... 

In this article, we present a cost-effective solution for switching short-circuit fault diagnosis in a three-phase quasi-Z-source inverter (qZSI). The fault is announced to the processor utilizing a peripheral circuit that covers the fault detection in all switches of the inverter. After the fault detection, a nonmaskable interrupt is activated, cutting the CPU routine, and a fault-diagnosis algorithm is initiated. The exact location of the failed switch is identified through the proposed algorithm. The entire process is accomplished in advance of the critical overcurrent condition, which typically arises after a short-circuit fault. Thanks to this overtake, the proposed technique prevents... 

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... 

Technology scaling has exacerbated the aging impact on the performance and reliability of integrated circuits. By entering into nanotechnology era in recent years, the power density per unit of area has increased, which leads to a higher chip temperature. Aging in a chip is originated from multiple phenomena; all of them are intensified by increased temperature. Several circuit- and architecture-level schemes tried to mitigate the aging in the literature. However, these schemes are not sufficient for multi-core systems due to their unawareness of the unique constraints and features of these platforms. In this paper, we propose a system-level aging mitigation method, so-called Adaptive Task... 

Tackling the dark silicon problem in a heterogeneous multicore system, the temperature constraints across the system should be addressed carefully by assigning a proper set of tasks to a pool of the heterogeneous cores during the run-time. When such a system is utilized in a reliable/real-time application, the reliability/timing constraints of the application should also be augmented to the temperature constraints and make the tasks mapping problem more and more complex. To solve the mapping problem in such a situation, we propose READY; an online reliability-and deadline-aware mapping and scheduling algorithm for heterogeneous multicore systems. READY utilizes an adaptive power constraint... 

Electric power systems try to maximize resilience by various enhancement strategies as preventive and corrective actions against extreme weather events. This paper presents an operational network reconfiguration strategy during a high wind event to strengthen the resiliency of distribution networks. In the proposed resilience enhancement strategy, a bi-level optimization problem is formulated with two conflicting objectives (i) maximizing grid resilience and (ii) realize the primary objective by a minimum number of out-of-service lines (OSLs) switching operations. Furthermore, the algorithm considers the priority of loads, which is an important characteristic of modern distribution grids.... 

In this paper, we propose a new method to reconstruct a signal corrupted by noise where both signal and noise are sparse but in different domains. The main contribution of our algorithm is its low complexity; it has much lower run-time than most other algorithms. The reconstruction quality of our algorithm is both objectively (in terms of PSNR and SSIM) and subjectively better or comparable to other state-of-the-art algorithms. We provide a cost function for our problem, present an iterative method to find its local minimum, and provide the analysis of the algorithm. As an application of this problem, we apply our algorithm for Salt-and-Pepper noise (SPN) and Random-Valued Impulsive Noise... 

We first propose a quantitative approach to detect high risk outbreaks of independent and coinfective SIR dynamics on three empirical networks: A school, a conference and a hospital contact network. This measurement is based on the k-means clustering method and identifies proper samples for calculating the mean outbreak size and the outbreak probability. Then we systematically study the impact of different temporal correlations on high risk outbreaks over the original and differently shuffled counterparts of each network. We observe that, on the one hand, in the coinfection process, randomization of the sequence of the events increases the mean outbreak size of high-risk cases. On the other... 

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... 

Despite FPGAs rapidly evolving to support the requirements of the most demanding emerging applications, their high static power consumption, concentrated within the routing resources, still presents a major hurdle for low-power applications. Augmenting the FPGAs with power-gating ability is a promising way to effectively address the power-consumption obstacle. However, the main challenge when implementing power gating is in choosing the clusters of resources in a way that would allow the most power-saving opportunities. In this paper, we take advantage of machine learning approaches, such as K-means clustering, to propose efficient algorithms for creating power-gating clusters of FPGA... 

A state-of-the-art algorithm for perspective projection reconstruction of non-rigid surfaces from single-view and realistic videos is proposed. It overcomes the limitations arising from the usage of orthographic camera model and also the complexity and non-linearity issues of perspective projection equation. Unlike traditional non-rigid structure-from-motion (NRSfM) methods, which have been studied only on synthetic datasets and controlled lab environments that require some prior constraints (such as manually segmented objects, limited rotations and occlusions, and full-length trajectories); the proposed method can be used in realistic video sequences. In addition, contrary to previous... 

The performance is presented of the reconstruction and identification algorithms for electrons and photons with the CMS experiment at the LHC. The reported results are based on proton-proton collision data collected at a center-of-mass energy of 13 TeV and recorded in 2016-2018, corresponding to an integrated luminosity of 136 fb-1. Results obtained from lead-lead collision data collected at √sNN=5.02 TeV are also presented. Innovative techniques are used to reconstruct the electron and photon signals in the detector and to optimize the energy resolution. Events with electrons and photons in the final state are used to measure the energy resolution and energy scale uncertainty in the... 

The growing number of Internet users and the prevalence of web applications make it necessary to deal with very complex software and applications in the network. This results in an increasing number of new vulnerabilities in the systems, and leading to an increase in cyber threats and, in particular, zero-day attacks. The cost of generating appropriate signatures for these attacks is a potential motive for using machine learning-based methodologies. Although there are many studies on using learning-based methods for attack detection, they generally use extracted features and overlook raw contents. This approach can lessen the performance of detection systems against content-based attacks... 

Deficit in imitation skills is one of the core symptoms of children with Autism Spectrum Disorder (ASD). In this study, we have tried to look closer at the body gesture imitation performance of 20 participants with autism, i.e. ASD group, and 20 typically developing subjects, i.e. TD group, in a set of robot-child and human-child gross imitation tasks. The results of manual scoring by two specialists indicated that while the TD group showed a significantly better imitation performance than the ASD group during the tasks, both ASD and TD groups performed better in the human-child mode than the robot-child mode in our experimental setup. Next, to introduce an automated imitation assessment... 

Optimal investment and operations of integrated local energy systems (ILESs) require medium to long-term prediction of energy consumption. To forecast load profiles, deep recurrent neural networks (DRNNs) are becoming increasingly useful due to their capability of learning uncertainty and high variability of load profiles. However, to explore and choose a DRNN model, out of conceivably numerous configurations, depends entirely on the performing task. In this regard, we tune and compare seven DRNN variants on the task of medium and long-term predictions for heating and electricity consumption. The ultimate DRNN model outperforms two state-of-the-art machine learning techniques, namely... 

We propose an inexact nonmonotone successive quadratic programming (SQP) algorithm for solving nonlinear programming problems with equality constraints and bounded variables. Regarding the value of the current feasibility violation and the minimum value of its linear approximation over a trust region, several scenarios are envisaged. In one scenario, a possible infeasible stationary point is detected. In other scenarios, the search direction is computed using an inexact (truncated) solution of a feasible strictly convex quadratic program (QP). The search direction is shown to be a descent direction for the objective function or the feasibility violation in the feasible or infeasible... 

We propose an inexact nonmonotone successive quadratic programming (SQP) algorithm for solving nonlinear programming problems with equality constraints and bounded variables. Regarding the value of the current feasibility violation and the minimum value of its linear approximation over a trust region, several scenarios are envisaged. In one scenario, a possible infeasible stationary point is detected. In other scenarios, the search direction is computed using an inexact (truncated) solution of a feasible strictly convex quadratic program (QP). The search direction is shown to be a descent direction for the objective function or the feasibility violation in the feasible or infeasible... 

This paper proposes a robust investment and operation model to attend the power and heat needs of a microgrid (MG) connected to the distribution system. The optimization algorithm decides on the best investment and operation of combined heat and power (CHP), boilers, PV power generation and battery energy storage systems (BESS). For the BESS, the algorithm estimates the optimal energy storage capacity (MWh) as well as the maximum hourly delivery capacity (MW). The non-linear and non-concave heat rate chart is recast by a mix-integer linear model to have a tractable and precise model. The model considers the uncertain in some parameters using probability density function (pdf) to portrait its...