Sharif Digital Repository

Disease prediction is a well-known classification problem in medical applications. Graph Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients’ features relative to each other. This can be achieved by modeling the problem as a graph node classification task, where each node is a patient. Due to the nature of such medical datasets, class imbalance is a prevalent issue in the field of disease prediction, where the distribution of classes is skewed. When the class imbalance is present in the data, the existing graph-based classifiers tend to be biased towards the major class(es) and neglect the samples in the minor class(es). On the other hand, the correct diagnosis... 

In this article, a high-accuracy hybrid approach for short-term wind power forecasting is proposed using historical data of wind farm and Numerical Weather Prediction (NWP) data. The power forecasting is carried out in three stages: wind direction forecasting, wind speed forecasting, and wind power forecasting. In all three phases, the same hybrid method is used, and the only difference is in the input data set. The main steps of the proposed method are constituted of outlier detection, decomposition of time series using wavelet transform, effective feature selection and prediction of each time series decomposed using Multilayer Perceptron (MLP) neural network. The combination of automatic... 

The performance of several regression methods is investigated to estimate the distribution of engineering demand parameters conditioned on intensity measures (EDP|IM) for small record sets. In particular, the performance of the multivariate ordinary least squares (OLS), a simultaneous mean-variance regression (MVR) done by a penalized weighted least-square loss function, and a mean-covariance/variance regression based on expectation maximization method (EM) are assessed. The efficiency of the introduced methods is compared with FEMA-P58 methodology. Performance assessment of EM and MVR methods shows that the overall increase in efficiency is about 25–45% for maximum inter-story drift ratios,... 

The trade-off between the fuel consumption and drag coefficient makes the investigations of drag reduction of utmost importance. In this paper, the rear-end shape optimization of Ahmed body is performed. Before changing the geometry, to identify the suitable simulation method and validate it, the standard Ahmed body is simulated using k − ω shear stress transport (SST) and k-epsilon turbulence models. The slant angle, rear box angle, and rear box length as variables were optimized simultaneously. Optimizations conducted by genetic algorithm (GA) and particle swarm optimization (PSO) methods indicate a 26.3% decrease in the drag coefficient. To ensure the validity of the results, a... 

Flexible manufacturing systems (FMS) should be able to respond to changing manufacturing requirements and environments. From the layout point of view, FMS need to be rearranged to fit the new requirements. However, rearranging the layout is often undesirable due to its unpredicted high costs and production disruption. This paper proposes a practical approach to mitigate the effects and repercussions of changing environments and avoid rearranging the layout. A robust layout approach is presented, where changes in product demand and mix are absorbed by altering product routes and not rearranging the layout. In this approach, the problem is decomposed into two sub-problems: sub-problem 1 (SP1)... 

This letter develops an adaptive actuator failure compensation method for nonlinear systems with unmatched parametric uncertainty based on contraction metrics. The proposed method, which is constructed by benefiting from the recent achievements on contraction metrics based adaptive control techniques, ensures the closed-loop stability and asymptotic tracking of the desired trajectory in the presence of actuator failures. In particular, a sufficient convex condition is derived for constructing a valid metric, by which a quadratic program-based controller is obtained to determine the inputs of the actuators. The introduced method is more general than the common adaptive actuator failure... 

Embedded Systems (ESs) have migrated from special-purpose hardware to commodity hardware. These systems have also tended to Mixed-Criticality (MC) implementations, executing applications of different criticalities upon a shared platform. Multi-cores, which are commonly used to design MC Systems (MCSs), bring out new challenges due to the Process Variation (PV). Power and frequency asymmetry affects the predictability of ESs. In this work, variation-aware techniques are explored to not only improve the reliability of MCSs, but also aid the scheduling and energy saving of them. We leverage the Core-to-Core (C2C) variations to protect high-criticality tasks and provide full service for a high... 

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... 

A multi-objective economic-statistical design is aimed in this article for simple linear profiles. In this design, the interval between two successive sampling intervals, the sample size and the number of adjustment points alongside, the parameters of the monitoring scheme are determined such that not only the implementation cost is minimized, but also the profile exhibits desired statistical performances. To this aim, three objective functions are considered in the multi-objective optimization model of the problem. The Lorenzen–Vance cost function is used to model the implementation cost as the first objective function to be minimized. The second objective function maximizes the in-control... 

The optimum design of reinforced concrete cantilever retaining walls subjected to seismic loads is an extremely important challenge in structural and geotechnical engineering, especially in seismic zones. This study proposes an adaptive sperm swarm optimization algorithm (ASSO) for economic design of retaining structure under static and seismic loading. The proposed ASSO algorithm utilizes a time-varying velocity damping factor to provide a fine balance between the explorative and exploitative behavior of the original method. In addition, the new method considers a reasonable velocity limitation to avoid the divergence of the sperm movement. The proposed algorithm is benchmarked with a set... 

Heat-bath algorithmic cooling (HBAC) techniques are techniques that are used to purify a target element in a quantum system. These methods compress and transfer entropy away from the target element into auxiliary elements of the system. The performance of algorithmic cooling has been investigated under ideal noiseless conditions. However, realistic implementations are imperfect, and for practical purposes, noise should be taken into account. Here we analyze HBAC techniques under realistic noise models. Surprisingly, we find that noise can, in some cases, enhance the performance and improve the cooling limit of HBAC techniques. We numerically simulate the noisy algorithmic cooling for the two... 

Costly and time-consuming approaches for solder joint lifetime estimation in electronic systems along with the limited availability and incoherency of data challenge the reliability considerations to be among the primary design criteria of electronic devices. In this article, an iterative machine learning framework is designed to predict the useful lifetime of the solder joint using a set of self-healing data that reinforce the machine learning predictive model with thermal loading specifications, material properties, and geometry of the solder joint. The self-healing dataset is iteratively injected through a correlation-driven neural network (CDNN) to fulfill the data diversity. Outcomes... 

To meet the requirements of the emerging applications, future networks must transmit multiple class of services traffic with diverse quality of service (QoS) requirements. In this paper, we formulate an integer linear programming (ILP) to design multilayer internet protocol (IP) over elastic optical networks (EONs), IP-over-EONs to support multi-class services with different latency and availability requirements. The objective of the ILP is to minimize capital expenditure (CAPEX) and spectrum usage while satisfying all QoS constraints in terms of latency and survivability mechanism. Furthermore, a heuristic algorithm is proposed to solve this problem in large-scale networks. We compare the... 

In this article, a new formation for swarm robotic systems is introduced. This formation, which is made up of a portion of swarm members and encircles the whole swarm, is called the shell formation. In this regard, an effective algorithm for developing the shell formation in swarm robotic systems is established. The interaction mechanism among swarm agents is based on the method of artificial potential fields and the local rule of the nearest neighbor. Subsequently, inspired by the thermodynamic science and based on the introduced shell formation, the thermodynamic concept of pressure is generalized to swarm robotic systems. Finally, the efficacy of the introduced shell formation in solving... 

This paper aims to identify an optimum bone fracture stabilizer. For this purpose, three design variables including the ratio of the screw diameter to the plate width at three levels, the ratio of the plate thickness to the plate width at three levels, and the diameter of the bone at two levels were selected for analysis. Eighteen 3D verified finite element models were developed to examine the effects of these parameters on the weight, maximum displacement and maximum von Mises stress of the fixation structure. Considering the relations between the inputs and outputs using multivariate regression, a genetic algorithm was used to find the optimal choices. Results showed that the diameter of... 

In the last decade, the scale of heterogeneous computing (HC) systems such as heterogeneous cloud computing environments was growing like never before. So network failures are unavoidable in such systems, which affect system reliability. Since the task scheduling algorithm in HC is challenging, we investigate a new reliability-aware task scheduling algorithm (RATSA) in this paper. RATSA is designed to schedule tasks on directed acyclic graphs (DAGs) by using the shuffled frog-leaping algorithm (SFLA) and genetic algorithm (GA) as evolutionary algorithms. The population-based SFLA-GA is applied to optimize makespan in the RATSA as an NP-complete problem. Moreover, the proposed algorithm... 

Sensors are one of the most vital instruments in Nuclear Power Plants (NPPs), and operators and safety systems monitor and analyze various parameters reported by them. Failure to detect sensors malfunctions or anomalies would lead to the considerable consequences. In this research, a new method based on thermal–hydraulic simulation by RELAP5 code and Feed-Forward Neural Networks (FFNN) is introduced to detect faulty sensors and estimate their correct value. For design an efficient neural net, seven feature selectors (i.e., Information gain, ReliefF, F-regression, mRMR, Plus-L Minus-R, GA, and PSO), three sigmoid activation functions (i.e., Logistic, Tanh and Elliot), and three training... 

The aim of this paper is to provide a solution to decrease water consumption in the slab-cooling unit of Mobarakeh Steel Complex in Iran. The plan should give an hourly decline in water consumption during a one-year operation period to calculate the annual reduction in water consumption of the proposed process. Recommended solutions for the conversion scheme of an existing wet cooling tower to a dry or hybrid cooling system require modeling of the slab cooling process. The curves of temperature drop in slabs are extracted in this paper by modeling the transient heat transfer of the slabs in the cooling process. This will reduce the computational volume. Then, the design and optimization of... 

Algorithmic trading based on machine learning is a developing and promising field of research. Financial markets have a complex, uncertain, and dynamic nature, making them challenging for trading. Some financial theories, such as the fractal market hypothesis, believe that the markets behave based on the collective psychology of investors who trade with different investment horizons and interpretations of information. Accordingly, a multi-agent deep reinforcement learning framework is proposed in this paper to trade on the collective intelligence of multiple agents, each of which is an expert trader on a specific timeframe. The proposed framework works in a hierarchical structure in which... 

Bio-sequence comparators are one of the most basic and significant methods for assessing biological data, and so, due to the importance of proteins, protein sequence comparators are particularly crucial. On the other hand, the complexity of the problem, the growing number of extracted protein sequences, and the growth of studies and data analysis applications addressing protein sequences have necessitated the development of a rapid and accurate approach to account for the complexities in this field. As a result, we propose a protein sequence comparison approach, called PCV, which improves comparison accuracy by producing vectors that encode sequence data as well as physicochemical properties...