Iran University of Science and Technology

About: Iran University of Science and Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Finite element method. The organization has 12917 authors who have published 24965 publications receiving 372013 citations. The organization is also known as: Governmental Technical Institute & Advanced Art College.

Adapted One-versus-All Decision Trees for Data Stream Classification

Abstract: One versus all (OVA) decision trees learn k individual binary classifiers, each one to distinguish the instances of a single class from the instances of all other classes. Thus OVA is different from existing data stream classification schemes whose majority use multiclass classifiers, each one to discriminate among all the classes. This paper advocates some outstanding advantages of OVA for data stream classification. First, there is low error correlation and hence high diversity among OVA's component classifiers, which leads to high classification accuracy. Second, OVA is adept at accommodating new class labels that often appear in data streams. However, there also remain many challenges to deploy traditional OVA for classifying data streams. First, as every instance is fed to all component classifiers, OVA is known as an inefficient model. Second, OVA's classification accuracy is adversely affected by the imbalanced class distribution in data streams. This paper addresses those key challenges and consequently proposes a new OVA scheme that is adapted for data stream classification. Theoretical analysis and empirical evidence reveal that the adapted OVA can offer faster training, faster updating and higher classification accuracy than many existing popular data stream classification algorithms.

GRP-HEFT: A Budget-Constrained Resource Provisioning Scheme for Workflow Scheduling in IaaS Clouds

Abstract: In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services according to a pay-per-use model. If users intend to run their workflow applications on cloud resources within a specific budget, they have to adjust their demands for cloud resources with respect to this budget. Although several scheduling approaches have introduced solutions to optimize the makespan of workflows on a set of heterogeneous IaaS cloud resources within a certain budget, the hourly-based cost model of some well-known cloud providers (e.g., Amazon EC2 Cloud) can easily lead to a higher makespan and some schedulers may not find any feasible solution. In this article, we propose a novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds. As a resource provisioning mechanism, we propose a greedy algorithm which lists the instance types according to their efficiency rate. For our scheduler, we modified the HEFT algorithm to consider a budget limit. GRP-HEFT is compared against state-of-the-art workflow scheduling techniques, including MOACS (Multi-Objective Ant Colony System), PSO (Particle Swarm Optimization), and GA (Genetic Algorithm). The experimental results demonstrate that GRP-HEFT outperforms GA, PSO, and MOACS for several well-known scientific workflow applications for different problem sizes on average by 13.64, 19.77, and 11.69 percent, respectively. Also in terms of time complexity, GRP-HEFT outperforms GA, PSO and MOACS.

Redundant Observability PMU Placement in the Presence of Flow Measurements Considering Contingencies

Abstract: Phasor measurement units (PMUs) have made it possible to observe and control wide-area power systems. In this paper, a new redundant observability method as a mixed-integer linear programming (MILP) is presented for optimal PMU placement. Redundant observation of buses enhances measurement reliability. The proposed method improves observability redundancy by a new objective function while using the same number of PMUs as the existing methods. Because of using MILP, the global optimal integer solution is achieved with a zero optimality gap. In addition, a systematic novel approach is proposed to incorporate already installed branch flow measurements in the PMU placement problem leading to a reduced number of PMUs required for system observability. This approach is able to handle both single and multiple flow measurements incident to a bus. PMU placement in case of PMU failure or branch outage is also studied. The proposed method along with an existing method is tested on four IEEE and Polish 3375-bus test systems. Obtained results, discussed in detail, show the efficiency of the proposed method in both speed and accuracy.

An isogeometrical approach to structural topology optimization by optimality criteria

Abstract: The Isogeometric Analysis (IA) method is applied for structural topology optimization instead of finite elements For this purpose, a control point based Solid Isotropic Material with Penalization (SIMP) method is employed and the material density is considered as a continuous function throughout the design domain and approximated by the Non-Uniform Rational B-Spline (NURBS) basis functions To prevent the formation of layouts with porous media, a penalization technique similar to the SIMP method is used For optimization an optimality criteria is derived and implemented A few examples are presented to demonstrate the performance of the method It is shown that, dissimilar to the element based SIMP topology optimization, the resulted layouts by this method are independent of the number of the discretizing control points and checkerboard free