A new positioning algorithm based on RSS measurement is proposed. The algorithm adopts maximum likelihood estimation and semi-definite programming. The received signal strength model is transformed to a non-convex est...
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A new positioning algorithm based on RSS measurement is proposed. The algorithm adopts maximum likelihood estimation and semi-definite programming. The received signal strength model is transformed to a non-convex estimator for the positioning of the target using the maximum likelihood estimation. The non-convex estimator is then transformed into a convex estimator by semi-definite programming, and the global minimum of the target location estimation is obtained. This algorithm aims at the L-0 known problem and then extends its application to the case of L-0 unknown. The simulations and experimental results show that the proposed algorithm has better accuracy than the existing positioning algorithms.
The increasing penetration of renewable resources via power-electronic converters is turning the modern power grid into a multi-converter system (MCS). In an MCS, most renewable resources currently use grid-following ...
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The increasing penetration of renewable resources via power-electronic converters is turning the modern power grid into a multi-converter system (MCS). In an MCS, most renewable resources currently use grid-following converters (GFLCs) for grid synchronization. The increasing integration of renewable resources via GFLCs can cause small-signal stability problems, especially under weak grid conditions. One way to prevent the stability issue is limiting the capacity of GFLCs in an MCS. Thus, it is important to assess the maximal capacity of GFLCs in the MCS while considering small signal stability constraints (SSSCs). This assessment is challenging due to 1) the complexity of assessing the small signal stability resulting from the complicated interaction between the power network and a large number of GFLCs, especially for a heterogeneous GFLCs with unknown inner parameters;2) the difficulty of finding the optimal solution to the relevant nonlinear optimization problem for maximal capacity assessment. To address these challenges, this paper proposes a semi-definite programming (SDP)-based method to assess the maximal capacity of GFLCs with SSSCs. In the proposed method, we first formulate the SSSCs based on the generalized short-circuit ratio (gSCR), which is a grid strength metric that can significantly reduce the complexity of quantifying small signal stability in an MCS. Then, we convert the formulated gSCR-based nonlinear optimization problem into an SDP that can conveniently find the optimal solution to the maximal capacity of GFLCs and their optimal allocations in the MCS. The efficacy of the proposed method is demonstrated on a 39-bus test system and a practical wind power system.
This work presents a novel framework for random access (RA) in crowded scenarios of massive multiple-input multiple-output (MIMO) systems. A huge portion of the system resources is dedicated as orthogonal pilots for a...
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This work presents a novel framework for random access (RA) in crowded scenarios of massive multiple-input multiple-output (MIMO) systems. A huge portion of the system resources is dedicated as orthogonal pilots for accurate channel estimation which imposes a huge training overhead. This overhead can be highly mitigated by exploiting intrinsic angular domain sparsity of massive MIMO channels and the sporadic traffic of users, i.e., few number of users are active to send or receive data in each coherence interval. Besides, the continuous-valued angles of arrival (AoA) corresponding to each active user are alongside each other forming a specific cluster. To exploit these features in this work, we propose a blind clustering algorithm based on super-resolution techniques that not only detects the spatial features of the active users but also provides accurate channel estimation. Specifically, an off-grid atomic norm minimization is proposed to obtain the AoAs and then a clustering-based approach is employed to identify which AoAs correspond to which active users. After active user detection, an alternating-based optimization approach is performed to obtain the channels and transmitted data. Simulation results demonstrate the effectiveness of our approach in AoA detection as well as data recovery which indeed provides a high performance spatial-based RA in crowded massive MIMO systems.
Visible light positioning (VLP) as a promising technology can provide high-precision indoor positioning services without electromagnetic interference, especially in multiple light-emitting diode (LED) scenarios. Howev...
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This paper proposes a semidefiniteprogramming approach for reactive power dispatch to minimize power losses in distribution systems with high-penetration of photovoltaic systems. The proposed control scheme utilizes ...
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The Noise Transfer Function (NTF) of Delta Sigma modulators is typically designed after the features of the input signal. We suggest that in many applications, and notably those involving D/D and D/A conversion or act...
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The Noise Transfer Function (NTF) of Delta Sigma modulators is typically designed after the features of the input signal. We suggest that in many applications, and notably those involving D/D and D/A conversion or actuation, the NTF should instead be shaped after the properties of the output/reconstruction filter. To this aim, we propose a framework for optimal design based on the Kalman-Yakubovich-Popov (KYP) lemma and semi-definite programming. Some examples illustrate how in practical cases the proposed strategy can outperform more standard approaches.
作者:
Al-Homidan, SulimanKFUPM
Dept Math Dhahran 31261 Saudi Arabia KFUPM
Ctr Smart Mobil & Logist Dhahran 31261 Saudi Arabia
Circulant matrices that are positive semi-definite emerge in many engineering applications, where the data collected in a matrix do not maintain the specified structure. A special case application is the circulant mat...
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Circulant matrices that are positive semi-definite emerge in many engineering applications, where the data collected in a matrix do not maintain the specified structure. A special case application is the circulant matrices in mechanical structures vibration modeling. The goal is to recover initial matrix while preserving the physical structure. In this paper, after reformulating the problem into different formulas first as a semi-definite problem, then as a mixed semi-definite and second-order cone problem, subsequently, interior point method will be used to solve the problem. The results are compared with the interior point methods and the modified alternating projection method. Numerical results for the special circulant matrices generated from mechanical structures vibration modeling are presented.
Nowadays, there is a critical and urgent need for developing smart and robust OPF solvers since the conventional options currently available for OPF problems are quite limited. This research is based on AC Optimal Pow...
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ISBN:
(纸本)9781728186122
Nowadays, there is a critical and urgent need for developing smart and robust OPF solvers since the conventional options currently available for OPF problems are quite limited. This research is based on AC Optimal Power Flow (ACOPF) with active and reactive quadratically constrained quadratic programming optimization problems of a form that arises in operation and planning applications in power systems. Besides being non-convex, these problems are identified to be NP-hard. This paper first utilized semi-definite programming (SDP) relaxation to convexify the original ACOPF problems and then solve the SDP relaxation problem with "moment-based" algorithm to get the rank-1 solutions of the W matrix. However, the computation time will increase exponentially with higher order of the moment matrix. To improve the computation efficiency, we added some penalty terms in the objective function to push the rank of the moment matrix reach to 1 by using the proposed SLP(SLPBB) algorithms. The proposed algorithm is verified by simulating on small scale test cases and NP-hard topologies in MATLAB. Also, the results were compared with the ones obtained by only using SLP (SLPBB) algorithms and the local solutions (The SLP and SLPBB algorithms were denoted as SLP(BB) afterwards). Numerical simulations illustrate that the SDP moment-based SLP(BB) algorithm can obtain the global optimal solutions which can guarantee the rank-1 solutions of the moment and W matrices.
This work introduces a cutting plane algorithm to solve the maximization of the minimum frequency of truss structures subject to volume and compliance constraints. Multiple load cases and multiple scenarios of non-str...
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This work introduces a cutting plane algorithm to solve the maximization of the minimum frequency of truss structures subject to volume and compliance constraints. Multiple load cases and multiple scenarios of non-structural mass distributions are considered. This problem is formulated as a non-convex semi-definite programming problem with Bi-linear Matrix Inequality (BMI) constraints. The proposed algorithm consists of iteratively tightening a linear relaxation of that problem. A new family of linear constraints (cutting planes) is defined as a linearization of BMI constraints. It is proved that the algorithm can find a violated valid cut for any infeasible solution that could be found in any iteration. Implementation details of the algorithm are given. We show the robustness of the method with some numerical examples and compare its performance with other available solvers. The reported results indicate that the new method outperforms the previous ones when the number of non-structural mass scenarios is large.
The accuracy and complexity of machine learning algorithms based on kernel optimization are determined by the set of kernels over which they are able to optimize. An ideal set of kernels should: admit a linear paramet...
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The accuracy and complexity of machine learning algorithms based on kernel optimization are determined by the set of kernels over which they are able to optimize. An ideal set of kernels should: admit a linear parameterization (for tractability); be dense in the set of all kernels (for robustness); be universal (for accuracy). Recently, a framework was proposed for using positive matrices to parameterize a class of positive semi-separable kernels. Although this class can be shown to meet all three criteria, previous algorithms for optimization of such kernels were limited to classification and furthermore relied on computationally complex semidefiniteprogramming (SDP) algorithms. In this paper, we pose the problem of learning semiseparable kernels as a minimax optimization problem and propose a SVD-QCQP primal-dual algorithm which dramatically reduces the computational complexity as compared with previous SDP-based approaches. Furthermore, we provide an efficient implementation of this algorithm for both classification and regression - an implementation which enables us to solve problems with 100 features and up to 30,000 datums. Finally, when applied to benchmark data, the algorithm demonstrates the potential for significant improvement in accuracy over typical (but non-convex) approaches such as Neural Nets and Random Forest with similar or better computation time.
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