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The task of community detection over complex networks is of paramount importance in a multitude of applications. The present work puts forward a top-to-bottom community identification approach, termed DC-EgoTen, in which an egonet-tensor (EgoTen) based algorithm is developed in a divide-and-conquer (DC) fashion for breaking the network into smaller subgraphs, out of which the underlying communities...
This paper proposes a method to determine the droop settings of dispatchable distributed generation units for optimal operation of a droop-controlled DC microgrid. Presently, it is necessary not only to generate power economically but also with minimum emission. Therefore, the objectives considered in this paper are 1) minimizing the total operating cost (TOC); 2) minimizing the emissions from generation...
Distribution system reconfiguration is very important operational aspect of distribution systems. When distribution system is fed from different substations/feeders, and various types of generating stations owned by various agencies, energy purchase cost may vary from owner to owner. In such cases formulating reconfiguration problem with loss minimization may not be acceptable. It is important to...
Image denoising is an extensively studied problem and can be used in many applications. In this paper, we propose an image denoising method using GMM and gradient sparse priors. GMM is a generic model and usually built based on image patches. Despite the high likelihood of GMM for image patches, the model is learned from image patches only. The relationship of neighboring patches is not learned. The...
LiDAR (Light detection and ranging) sensors are widely used in research and development. As such, they build the base for the evaluation of newly developed ADAS (Advanced Driver Assistance Systems) functions in the automotive field where they are used for ground truth establishment. However, the factory calibration provided for the sensors is not able to satisfy the high accuracy requirements by such...
Processes showing an initial response that is opposite to the final steady-state are present in industrial applications. Control of such processes involves difficulties and recently researchers have started paying great attention to the control of such processes. This paper reports on deriving optimal tuning rules for controlling integrating processes with inverse response. Simple and optimal analytical...
Bipolar DC system provides flexible connection and decreased line-to-ground voltage level at the expense of potential voltage unbalance between positive pole and negative pole. Voltage balancer works as “Dependent Voltage Source” to eliminate the unbalance voltage. It is a critical grid-forming component for bipolar system. Considering the need of system expansion and “N+1” redundancy design, bipolar...
In this paper, we study the energy-efficient user access control (UAC) based on resource allocation (RA) in heterogeneous cellular networks (HCNs) with the required downlink data rate under non-ideal power amplifiers (PAs) and circuit power. It is proved that the energy consumption minimization is achieved when the typical user accesses only one base station (BS), while the other BSs remain in idle...
In this paper, a novel autofocus imaging method is proposed to achieve high-resolution for inverse synthetic aperture radar (ISAR) in the compressive sensing (CS) framework. Firstly, we fomulate the ISAR CS imaging in a Multiple Measurement Vector (MMV) sparse optimization problem. Then, by utilizing the structure sparsity of ISAR image, i.e. row sparsity and column sparsity simutaneously, our method...
Software defined networking (SDN) has been proposed as a novel centralized architecture which is capable of offering flexible network management and user quality-of-service (QoS) guaranteed applications. The problem of controller placement in SDN, i.e., determining the locations of the controllers has received considerable attention in recent years. In this paper, we jointly consider the control plane...
In an iterative and incremental development environment software regression testing plays an important role; it helps to ensure the reliability in the building process of a software product. The optimization of a regression test depends on the size of the test suite to be executed. Regression testing helps to verify existing modifications (fixing bugs) or verify new features added to a software product...
As an emerging technology, software defined networking (SDN) allows flexible control of network devices and supports user applications with guaranteed quality of service (QoS). To achieve flow transmission between two non-adjacent switches in SDN, efficient routing algorithm should be designed. In this paper, we jointly consider route selection and flow allocation problem. To stress the service sensitivity...
The optimal capacitor allocation in electricity distribution networks (EDN) plays a meaningful role in voltage profile improvement, power factor amelioration and also power losses minimization. This work presents two comprehensive optimization algorithms based on metaheuristics as a comparison to solve the capacitor allocation problem in modern distribution networks. Thus, the problem of optimal capacitor...
Dual-active-bridge (DAB) DC-DC isolated converter is widely used in many applications e.g. power supplies, transportations and renewable power systems. In this paper, general modulation characterization and optimization of DAB converters have been analyzed and advanced modulation methods are proposed. Simulations and experiments are implemented to verify the effectiveness of the proposed modulation...
This paper focuses on addressing the decentralized data fusion (DDF) problem in dynamic sensor networks based on Chernoff rule. Generally, the Chernoff rule is challenging to implement since the fused probability density functions (pdfs) that cannot be obtained in closed form. Besides, the existing works for implementing Chernoff rule are mostly confined to iterative fusion of two sensors. To address...
Stochastic optimization is playing an increasingly important in machine learning in the big data era. In this paper, we use forward-backward splitting for the stochastic optimization problems, where the objective is the sum of two functions: one is the expected risk function, another is a regularized term. At each iteration of this method, we just use a single sample to adjust the variables. We prove...
Nonuniform norm constraint (NNC)-based methods have numerous potential applications for sparse signal recovery from a small number of measurements. In this study, we propose a novel NNC sparse recovery algorithm. First, a particular solution is attained by the gradient-descent-like method, which searches for the minimum NNC solution. Second, general solutions can be derived in the framework of underdetermined...
In text mining, document clustering describes the efforts to assign unstructured documents to clusters, which in turn usually refer to topics. Clustering is widely used in science for data retrieval and organisation. In this paper we present a new graph theoretical approach to document clustering and its application on a real-world data set. We will show that the well-known graph partition to stable...
In spite of the increasing awareness apparent in the literature regarding the selection of sustainable suppliers, there are limitations in incorporating the sustainable performance in terms of traditional, environmental and social aspects in supplier selection and order allocation. Thus, an integrated fuzzy TOPSIS-multi objective optimization (MOO) approach was developed to integrate sustainable performance...
The problem of minimizing roundoff noise and pole sensitivity simultaneously subject to l2-norm scaling constraints of dynamic range for 2-D digital filters with separable denominator is investigated. A novel measure for the evaluation of roundoff noise and pole sensitivity is introduced and then an effective method for minimizing the measure is explored by converting the constrained optimization...
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