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Estimating Number of Critical Eigenvalues of Large-Scale Power System Based on Contour Integral

IEEE TRANSACTIONS ON POWER SYSTEMS 2021年第6期36卷 5854-5862页

作者： Gao, Shuai Du, Zhengchun Li, Yujun Xi An Jiao Tong Univ Dept Elect Engn Xian 710049 Peoples R China

This paper proposes an improved stochastic estimation method based on contour integral to estimate the number of critical eigenvalues. The stochastic estimation method transforms the eigenvalue numbers' calculation into summing the traces of a series of inverse matrices. And then, a stochastic strategy is used to estimate the trace of the inverse matrix. However, the method results in an unacceptable error when the state matrices of many practical power systems are ill-conditioned. To solve this problem, the improved algorithm is proposed by formulating a satisfactory matrix for trace calculation artificially instead of randomly with the help of the well-known greedy algorithm based on graph coloring. It is proved to be much more reliable when dealing with ill matrix. The proposed method based on contour integral is very suitable for large-scale power systems due to the element distribution of descriptor matrices makes it possible to construct only one fixed matrix at all integral points. Moreover, the calculation efficiency is further improved by calculating implicitly without destroying the sparsity of descriptor systems. Numerical experiments indicate that the proposed method can significantly improve accuracy without considerably increasing the calculation time. And the approximate eigenvalue number can fully meet the needs of subsequent algorithms based on contour integral to calculate the specific eigenvalues.

关键词： Eigenvalues and eigenfunctions Estimation Sparse matrices Power systems Power system stability Approximation algorithms Transforms Contour integral critical eigenvalues stochastic estimation method greedy algorithm graph coloring

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Orthogonal Least Absolute Value for Sparse Spike Deconvolution

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2021年第4期40卷 1948-1961页

作者： Had, A. Sabri, K. Univ Chouaib Doukkali STIC Fac Sci El Jadida Morocco

Several phenomena encountered in nature are characterized by very localized events occurring randomly at given times. Random pulses are an appropriate modelling tool for such events. Usually, the impulses are hidden in the noise due to unwanted convolution. In some cases, the problem is more complex because of the short time lag between the pulses. Considering these problems, the resulting signal is unclear and can lead to an erroneous analysis. Hence the need for deconvolution to restore the pulsed signal in order to obtain a more accurate diagnosis. The main objective of this study is to propose a new algorithm called orthogonal least absolute value. The particularity of this algorithm lies in its selection criterion. The algorithm iteratively selects the atom minimizing the absolute value of the approximation error. This allows the proposed algorithm to outperform classical greedy algorithms when the peaks are very close to each other. Numerical and experimental simulations are performed to study the proposed algorithm and compare its behavior to other greedy algorithms in deconvolution framework. Simulations results prove the performance of the proposed algorithm, especially when the impulses are very close to each other.

关键词： Sparsity Random impulses Deconvolution greedy algorithm Phonocardiogram signal

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Streamlining LEGO® model design: an automated optimisation approach

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Procedia CIRP 2024年 126卷 945-950页

作者： Nengsheng Bao Yongyi Zhang Yuchen Fan Alessandro Simeone Intelligent Manufacturing Key Laboratory of Ministry of Education Shantou University Shantou China Department of Management and Production Engineering Politecnico di Torino Corso Duca degli Abruzzi 24 Torino Italy

LEGO® models have become increasingly popular beyond their traditional toy and entertainment applications, and are now used in fields such as education, cognitive science, engineering and robotics. However, designing LEGO® models for such diverse applications poses new challenges in terms of automation and optimisation. To address such issues, this paper proposes a design support system that automatically generates LEGO® models from any 3D model of a target object using a specific set of standard LEGO® bricks in a layer-by-layer fashion. The system converts the 3D model into a LEGO® model using the minimum number of LEGO® bricks required. The core of the proposed system lies in the use of a greedy algorithm for merging optimisation of each layer of bricks and a depth-first search algorithm for optimising the connection between multiple layers of bricks. Experimental results on several 3D graphics validate the effectiveness of the proposed methodology and its ability to output the corresponding LEGO® model and detailed layout illustrations for each layer of bricks. The proposed system can provide LEGO® builders with an automated optimisation approach for designing LEGO® models, minimising errors compared to manual design.

关键词： Design optimisation greedy algorithm Graph optimisation

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Containment of rumor spread by selecting immune nodes in social networks

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第3期18卷 2614-2631页

作者： Wu, Hong Zhang, Zhijian Fang, Yabo Zhang, Shaotang Jiang, Zuo Huang, Jian Li, Ping Qujing Normal Univ Sch Informat Engn Qujing 655000 Peoples R China Kunming Univ Sci & Technol Fac Sci Kunming 650031 Yunnan Peoples R China Jilin Univ Sch Math Changchun 130012 Peoples R China Yunnan Minzu Univ Sch Math & Comp Sci Kunming 650031 Peoples R China

With the popularity of online social network these have become important platforms for the spread of information. This not only includes correct and useful information, but also false information, and even rumors which could result in panic. Therefore, the containment of rumor spread in social networks is important. In this paper, we propose an effective method that involves selecting a set of nodes in (k, eta)-cores and immunize these nodes for rumor containment. First, we study rumor influence propagation in social networks under the extended Independent Cascade (EIC) model, an extension of the classic Independent Cascade (IC) model. Then, we decompose a social network into subgraphs via core decomposition of uncertain graphs and compute the number of immune nodes in each subgraph. Further we greedily select nodes with the Maximum Marginal Covering Neighbors Set as immune nodes. Finally, we conduct experiments using real -world datasets to evaluate our method. Experimental results show the effectiveness of our method.

关键词： social networks containment of rumor spread (k, eta) cores decomposition extended independent cascade model greedy algorithm

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Service Chaining Placement Based on Satellite Mission Planning in Ground Station Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2021年第3期18卷 3049-3063页

作者： Gao, Xiangqiang Liu, Rongke Kaushik, Aryan Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China UCL Dept Elect & Elect Engn London WC1E 7JE England

As the increase in satellite number and variety, satellite ground stations should be required to offer user services in a flexible and efficient manner. Network function virtualization (NFV) can provide a new paradigm to allocate network resources on-demand for user services over the underlying network. However, most of the existing work focuses on the virtual network function (VNF) placement and routing traffic problem for enterprise data center networks, the issue needs to further study in satellite communication scenarios. In this article, we investigate the VNF placement and routing traffic problem in satellite ground station networks. We formulate the problem of resource allocation as an integer nonlinear programming (INLP) model and the objective is to minimize the link resource utilization and the number of servers used. Considering the information about satellite orbit fixation and mission planning, we propose location-aware resource allocation (LARA) algorithms based on greedy and IBM CPLEX 12.10, respectively. The proposed LARA algorithm can assist in deploying VNFs and routing traffic flows by predicting the running conditions of user services. We evaluate the performance of our proposed LARA algorithm in three networks of Fat-Tree, BCube, and VL2. Simulation results show that our proposed LARA algorithm performs better than that without prediction, and can effectively decrease the average resource utilization of satellite ground station networks.

关键词： Satellites Resource management Planning Prediction algorithms Routing Satellite ground stations Servers Network function virtualization (NFV) satellite ground station resource allocation resource utilization greedy algorithm IBM CPLEX

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An approximation algorithm for the k-generalized Steiner forest problem

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OPTIMIZATION LETTERS 2021年第4期15卷 1475-1483页

作者： Gao, Jiawen Gao, Suogang Liu, Wen Wu, Weili Du, Ding-Zhu Hou, Bo Hebei Normal Univ Sch Math Sci Shijiazhuang 050024 Hebei Peoples R China Univ Texas Dallas Dept Comp Sci Dallas TX 75080 USA

In this paper, we introduce the k-generalized Steiner forest (k-GSF) problem, which is a natural generalization of the k-Steiner forest problem and the generalized Steiner forest problem. In this problem, we are given a connected graph G = (V, E) with non-negative costs c(e) for the edges e is an element of E, a set of disjoint vertex sets V = {V-1, V-2, ..., V-l} and a parameter k <= 1. The goal is to find a minimum-cost edge set F subset of E that connects at least k vertex sets in V. Our main work is to construct an O(root l)-approximation algorithm for the k-GSF problem based on a greedy approach and an LP-rounding technique.

关键词： k-generalized Steiner forest problem greedy algorithm LP-rounding algorithm

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Heuristic greedy Scheduling of Electric Vehicles in Vehicle-to-Grid Microgrid Owned Aggregators

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SENSORS 2022年第6期22卷 2408-2408页

作者： Abdel-Hakim, Alaa E. Abo-Elyousr, Farag K. Umm Al Qura Univ Dept Comp Sci Jamoum Mecca 25371 Saudi Arabia Assiut Univ Elect Engn Dept Assiut 71516 Egypt

In on-grid microgrids, electric vehicles (EVs) have to be efficiently scheduled for cost-effective electricity consumption and network operation. The stochastic nature of the involved parameters along with their large number and correlations make such scheduling a challenging task. This paper aims at identifying pertinent innovative solutions for reducing the relevant total costs of the on-grid EVs within hybrid microgrids. To optimally scale the EVs, a heuristic greedy approach is considered. Unlike most existing scheduling methodologies in the literature, the proposed greedy scheduler is model-free, training-free, and yet efficient. The proposed approach considers different factors such as the electricity price, on-grid EVs state of arrival and departure, and the total revenue to meet the load demands. The greedy-based approach behaves satisfactorily in terms of fulfilling its objective for the hybrid microgrid system, which is established of photovoltaic, wind turbine, and a local utility grid. Meanwhile, the on-grid EVs are being utilized as an energy storage exchange location. A real time hardware-in-the-loop experimentation is comprehensively conducted to maximize the earned profit. Through different uncertainty scenarios, the ability of the proposed greedy approach to obtain a global optimal solution is assessed. A data simulator was developed for the purposes of generating evaluation datasets, which captures uncertainties in the behaviors of the system's parameters. The greedy-based strategy is considered applicable, scalable, and efficient in terms of total operating expenditures. Furthermore, as EVs penetration became more versatile, total expenses decreased significantly. Using simulated data of an effective operational duration of 500 years, the proposed approach succeeded in cutting down the energy consumption costs by about 50-85%, beating existing state-of-the-arts results. The proposed approach is proved to be tolerant to the large amounts of uncerta

关键词： hybrid microgrids energy scheduling heuristic optimization electric vehicle greedy algorithm uncertainty

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Mostly Exploration-Free algorithms for Contextual Bandits

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MANAGEMENT SCIENCE 2021年第3期67卷 1329-1349页

作者： Bastani, Hamsa Bayati, Mohsen Khosravi, Khashayar Univ Penn Wharton Sch Philadelphia PA 19104 USA Stanford Univ Stanford Grad Sch Business Stanford CA 94305 USA Stanford Univ Stanford Univ Elect Engn Stanford CA 94305 USA

The contextual bandit literature has traditionally focused on algorithms that address the exploration-exploitation tradeoff. In particular, greedy algorithms that exploit current estimates without any exploration may be suboptimal in general. However, exploration-free greedy algorithms are desirable in practical settings where exploration may be costly or unethical (e.g., clinical trials). Surprisingly, we find that a simple greedy algorithm can be rate optimal (achieves asymptotically optimal regret) if there is sufficient randomness in the observed contexts (covariates). We prove that this is always the case for a two-armed bandit under a general class of context distributions that satisfy a condition we term covariate diversity. Furthermore, even absent this condition, we show that a greedy algorithm can be rate optimal with positive probability. Thus, standard bandit algorithms may unnecessarily explore. Motivated by these results, we introduce greedy-First, a new algorithm that uses only observed contexts and rewards to determine whether to follow a greedy algorithm or to explore. We prove that this algorithm is rate optimal without any additional assumptions on the context distribution or the number of arms. Extensive simulations demonstrate that greedy-First successfully reduces exploration and outperforms existing (exploration-based) contextual bandit algorithms such as Thompson sampling or upper confidence bound.

关键词： sequential decision making contextual bandit greedy algorithm exploration-exploitation

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Multi-Tier CloudVR: Leveraging Edge Computing in Remote Rendered Virtual Reality

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2021年第2期17卷 1–24页

作者： Mehrabi, Abbas Siekkinen, Matti Kamarainen, Teemu Yla-Jaaski, Antti Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NEI 8ST Tyne & Wear England Aalto Univ Dept Comp Sci Espoo 02150 Finland Univ Helsinki Dept Comp Sci Yliopistonkatu 4 Helsinki 00100 Finland

The availability of high bandwidth with low-latency communication in 5G mobile networks enables remote rendered real-time virtual reality (VR) applications. Remote rendering of VR graphics in a cloud removes the need for local personal computer for graphics rendering and augments weak graphics processing unit capacity of stand-alone VR headsets. However, to prevent the added network latency of remote rendering from ruining user experience, rendering a locally navigable viewport that is larger than the field of view of the HMD is necessary. The size of the viewport required depends on latency: Longer latency requires rendering a larger viewport and streaming more content. In this article, we aim to utilize multi-access edge computing to assist the backend cloud in such remote rendered interactive VR. Given the dependency between latency and amount and quality of the content streamed, our objective is to jointly optimize the tradeoff between average video quality and delivery latency. Formulating the problem as mixed integer nonlinear programming, we leverage the interpolation between client's field of view frame size and overall latency to convert the problem to integer nonlinear programming model and then design efficient online algorithms to solve it. The results of our simulations supplemented by real-world user data reveal that enabling a desired balance between video quality and latency, our algorithm particularly achieves the improvements of on average about 22% and 12% in term of video delivery latency and 8% in term of video quality compared to respectively order-of-arrival, threshold-based, and random-location strategies.

关键词： Multi-access edge computing (MEC) rendered virtual reality (VR) joint optimization integer nonlinear programming (INLP) greedy algorithm

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A scaling algorithm for optimizing arbitrary functions over vertices of polytopes (Jun, 10.1007/s10107-020-01522-0, 2021)

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MATHEMATICAL PROGRAMMING 2021年第1-2期190卷 103-104页

作者： Chubanov, Sergei Bosch Ctr Artificial Intelligence Renningen Germany

In this paper we present a scaling algorithm for minimizing arbitrary functions over vertices of polytopes in an oracle model of computation which includes an augmentation oracle. For the binary case, when the vertices are 0–1 vectors, we show that the oracle time is polynomial. Also, this algorithm allows us to generalize some concepts of combinatorial optimization concerning performance bounds of greedy algorithms and leads to new bounds for the complexity of the simplex method.

关键词： Nonlinear optimization Binary optimization Simplex method Diameter Scaling greedy algorithm

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