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Fast and scalable method for distributed Boolean tensor factorization

VLDB JOURNAL 2019年第4期28卷 549-574页

作者： Park, Namyong Oh, Sejoon Kang, U. Carnegie Mellon Univ Dept Comp Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Computat Biol Dept Pittsburgh PA 15213 USA Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea

How can we analyze tensors that are composed of 0's and 1's? How can we efficiently analyze such Boolean tensors with millions or even billions of entries? Boolean tensors often represent relationship, membership, or occurrences of events such as subject-relation-object tuples in knowledge base data (e.g., 'Seoul'-'is the capital of'-'South Korea'). Boolean tensor factorization (BTF) is a useful tool for analyzing binary tensors to discover latent factors from them. Furthermore, BTF is known to produce more interpretable and sparser results than normal factorization methods. Although several BTF algorithms exist, they do not scale up for large-scale Boolean tensors. In this paper, we propose DBTF, a distributed method for Boolean CP (DBTF-CP) and Tucker (DBTF-TK) factorizations running on the Apache Spark framework. By distributed data generation with minimal network transfer, exploiting the characteristics of Boolean operations, and with careful partitioning, DBTF successfully tackles the high computational costs and minimizes the intermediate data. Experimental results show that DBTF-CP decomposes up to 16(3)-32(3) x larger tensors than existing methods in 82-180 x less time, and DBTF- TK decomposes up to 8(3)-16(3) x larger tensors than existing methods in 86-129 x less time. Furthermore, both DBTF- CP and DBTF- TK exhibit near- linear scalability in terms of tensor dimensionality, density, rank, and machines.

关键词： Tensor Tensor factorization Boolean CP factorization Boolean Tucker factorization distributed algorithm

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Onboard distributed autonomous orbit determination based on state caching approach

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ADVANCES IN SPACE RESEARCH 2019年第8期64卷 1518-1526页

作者： Lai, Jun Zhou, Yongbin Zhou, Yifan Yang, Jun Natl Univ Def Technol Changsha 410072 Hunan Peoples R China

Autonomous Orbit Determination (AOD) using inter-satellite ranging measurement can maintain the functionality of the Global Navigation Satellite System (GNSS) for a long time without the assistance of the ground facilities, even though it has the rank deficiency problem. This paper proposes an onboard distributed algorithm to implement AOD based on the extended Kalman filter. It exploits the inter-satellite ranging schedule as a priori information and maintains a cache of the estimated global states and the corresponding covariance on each satellite. Simulation results show that the mean User Range Error (URE) of a 31-satellite GPS constellation is less than 0.5 m in 120 days using our algorithm, which has a similar accuracy level as the centralized Kalman filter, and outperforms the conventional distributed algorithm. It also shows that our algorithm is robust to constellation size and ranging measurement noise level. Meanwhile, due to the distributed architecture, our algorithm does not require any additional communication among satellites. All these features make our algorithm more applicable to practical scenario compared to the existing algorithms. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Autonomous orbit determination distributed algorithm Inter-satellite link

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distributed control based power sharing strategy for an islanded AC microgrid

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第4期13卷 553-562页

作者： Negi, Nandini Sahoo, Soumya Ranjan Chakrabarti, Saikat North Carolina State Univ ECE Raleigh NC USA IIT Kanpur Elect Engn Kanpur Uttar Pradesh India

This study presents a novel secondary control strategy for a radial microgrid with end-to-end distributed energy resource (DER) interconnections, operating under islanded conditions. The control strategy utilises sparse communication and precludes the presence of a centralised controller. The distributed algorithm has a twofold objective. The first task is to determine the desired power injection at each DER bus for a load change occurring anywhere in the microgrid. The remaining task includes obtaining the bus angle set points at all the DER units such that the actual power injections converge to the desired power injections at each DER bus. The algorithm enables accurate system wide power injection in proportion to the respective DER base ratings. The control strategy is tested on a three-bus, three-DER system with local loads. The controller is shown to work for two different sets of microgrid ratings. The bounds are evaluated for each design parameter specified according to the system nominal ratings to maintain the stability of the distributed algorithm. Proofs are stated to show the convergence of algorithms to the correct power injection solution for all the nodes. Real-time simulation results are presented to corroborate accurate power sharing through the algorithm.

关键词： centralised control power distribution faults distributed power generation distributed control power distribution control distributed control power sharing strategy islanded AC microgrid novel secondary control strategy radial microgrid end-to-end distributed energy resource interconnections islanded conditions sparse communication centralised controller distributed algorithm desired power injection DER bus load change remaining task bus angle set points DER units actual power injections accurate system wide power injection respective DER base ratings three-bus three-DER system microgrid ratings system nominal ratings correct power injection solution accurate power

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Relaxed hybrid consensus ADMM for distributed convex optimisation with coupling constraints

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IET CONTROL THEORY AND APPLICATIONS 2019年第17期13卷 2828-2837页

作者： Olama, Alireza Bastianello, Nicola Mendes, Paulo R. C. Camponogara, Eduardo Univ Fed Santa Catarina Dept Automat & Syst Engn Florianopolis SC Brazil Univ Padua Dept Informat Engn Via Gradenigo 6-B I-35131 Padua Italy

In this study, the solution of a convex distributed optimisation problem with a global coupling inequality constraint is considered. By using the Lagrange duality framework, the problem is transformed into a distributed consensus optimisation problem and then based on the recently proposed Hybrid Alternating Direction Method of Multipliers (H-ADMM), which merges distributed and centralised optimisation concepts problems, a novel distributed algorithm is developed. In particular, the authors offer a reformulation of the original H-ADMM in an operator theoretical framework, which exploits the known relationship between ADMM and Douglas-Rachford splitting. In addition, the authors' formulation allows us to generalise the H-ADMM by including a relaxation constant, not present in the original design of the algorithm. Moreover, an adaptive penalty parameter selection scheme that consistently improves the practical convergence properties of the algorithm is proposed. Finally, the convergence results of the proposed algorithm are discussed and moreover, in order to present the effectiveness and the major capabilities of the proposed algorithm in off-line and on-line scenarios, distributed quadratic programming and distributed model predictive control problems are considered in the simulation section.

关键词： gradient methods distributed algorithms convex programming distributed control iterative methods predictive control quadratic programming convergence of numerical methods convergence relaxed hybrid consensus ADMM distributed convex optimisation coupling constraints convex distributed optimisation problem global coupling inequality constraint Lagrange duality framework distributed consensus optimisation problem centralised optimisation concepts problems original H-ADMM operator theoretical framework Douglas-Rachford splitting adaptive penalty parameter selection scheme distributed quadratic programming distributed model predictive control problems distributed algorithm hybrid alternating direction method hybrid alternating direction method of multipliers

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KTMiner: distributed k-truss detection in big graphs

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INFORMATION SYSTEMS 2019年第Jul.期83卷 195-216页

作者： Alemi, Mehdi Haghighi, Hassan Shahid Beheshti Univ Fac Comp Sci & Engn GC Tehran Iran

Discovering cohesive subgraphs is an important issue in the analysis of massive graphs. A new type of cohesive subgraphs, k-truss, has gained a lot of attentions in recent years. Although different methods have been proposed to extract k-truss subgraphs, they are unable to deal with big graphs, and they suffer from the lack of efficiency. To deal with the problem of finding k-truss subgraphs in big graphs, we propose a novel efficient distributed algorithm, called KTMiner, based on the MapReduce paradigm and the key-value structure. KTMiner is deployed on the Spark platform, a big data framework. Given a specific k value, KTMiner finds edges belonging to the k-truss subgraphs. It includes three consequent phases. First, a novel distributed k-core routine is employed to prune unnecessary vertices from the input graph. Then, the triangle information per edge in the form of a new data structure, called Triangle Set (TSet), is produced. Finally, using a distributed iterative procedure, the desired k-truss subgraphs are detected. KTMiner perfectly caches the reusable data in the distributed memory. In addition, it forms an efficient load balancing mechanism by designing appropriate data structures. This results in a fine-grained parallelism. The superiority of our solution over the state-of-the-art methods is shown by the experiments on real-world graphs. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Cohesive subgraphs K-truss Big graphs Triangle counting K-core distributed algorithm

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distributed Optimal Microgrid Energy Management With Considering Stochastic Load

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2019年第2期10卷 729-737页

作者： Silani, Amirreza Yazdanpanah, Mohammad Javad Univ Tehran Control & Intelligent Proc Ctr Excellence Sch Elect & Comp Engn Adv Control Syst Lab Tehran *** Iran

The uncertainty of load is a challenging issue for power systems. These uncertainties can damage the power systems and even cause blackout. Therefore, it is necessary to handle this problem. In this paper, a method for energy management in microgrids with stochastic load is proposed. The design is performed through solving a nonlinear optimization problem. In the centralized optimization technique, high computational capabilities are needed and the customer's privacy might be infringed. On the contrary, in distributed techniques, the power network and its constraints are neglected since it is assumed that all loads and generations are connected to one bus. Consequently, in the proposed distributed energy management strategy (EMS), the optimization problem is decomposed into two optimization levels, which consist of the microgrid centralized controller and local controllers and the distribution network and the corresponding constraints are taken into account. Since the precise prediction of the load is not accessible, the load is assumed stochastic. The proposed distributed EMS is integrated with diesel generators, wind turbines, photovoltaics, and a battery storage system. The simulation results depict the efficiency of the proposed distributed EMS.

关键词： distributed networks optimal power flow (OPF) stochastic load distributed algorithm energy management

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distributed Source Localization in Acoustic Sensor Networks Using the Coherent-to-Diffuse Power Ratio

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2019年第1期13卷 61-75页

作者： Brendel, Andreas Kellermann, Walter Friedrich Alexander Univ Erlangen Nurnberg Chair Multimedia Commun & Signal Proc D-91058 Erlangen Germany

In this paper, acoustic source localization in an acoustic sensor network (ASN) based on estimates of the coherent-to-diffuse power ratio (CDR) is investigated. It is shown that the CDR reflects the distance between an acoustic source and an observing microphone pair in real-world acoustic enclosures. As this relation is dependent on various physical parameters of the given acoustic environment which are unknown in practice, we estimate it by Gaussian process regression. Subsequently, the obtained distance estimates are used by an ASN with known relative node positions for estimating the position of the acoustic source. The algorithm is designed in a completely distributed fashion, i.e., no fusion center is needed. The computational complexity of the proposed algorithm is low and the amount of data to be shared between the sensor nodes is small and can be adjusted to the constraints imposed on the network by using a sparse Gaussian process regression method. A simulation study confirms, employing also real measurement data in a low-reverberant room, the efficacy of the proposed algorithm for a multitude of scenarios.

关键词： Acoustic sensor network distributed algorithm online Gaussian regression

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Maximizing Social Welfare Considering the Uncertainty of Wind Power Plants Using a distributed Consensus-based algorithm 26

Maximizing Social Welfare Considering the Uncertainty of Win...

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26th Iranian Conference on Electrical Engineering (ICEE)

作者： Tajalli, Seyede Zahra Niknam, Taher Aghaei, Jamshid Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

ISBN: (纸本)9781538649169

In this paper, a distributed consensus-based algorithm is introduced for social welfare maximization problems in the presence of responsive loads and Wind Turbines (WTs) in smart grids. This distributed algorithm eliminates the need for a central controller in the system. Consequently, it prevents the curse of dimensionality, the computational burden and the communication congestion which are some drawbacks of using centralized algorithms for controlling smart grids. The effect of uncertainty of output power of WTs generation is another discussed issue. Modeling this uncertainty is investigated using Unscented Transformation (UT) method. In MATLAB simulation, the performance of this distributed algorithm is illustrated in the presence of demand response in some multi-agent systems. Additionally, the impact of WTs uncertainty is inspected in a 29-node system.

关键词： smart grids UT method distributed algorithm social welfare

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distributed Optimal Economic Dispatch for Microgrids Considering Communication Delays

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2019年第8期49卷 1634-1642页

作者： Huang, Bonan Liu, Lining Zhang, Huaguang Li, Yushuai Sun, Qiuye Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Northeastern Univ Key Lab Integrated Automat Proc Ind Natl Educ Minist Shenyang 110004 Liaoning Peoples R China

This paper investigates the economic dispatch problem of microgrids in a distributed fashion. To address this issue, a delay-free-based distributed algorithm is presented to optimally assign the whole energy demand among local generation units with the objective of minimizing the agminated operation cost. By implementing the proposed algorithm, each component can find its own optimal operations by only requiring local computation and communication. As a result, it enhances the system robustness, flexibility, privacy, etc. More importantly, the time-varying delays model is considered and embedded into the design of our distributed algorithm, such that the components can employ the delays information to achieve the collaborative operation, which are more general and applicable for practical power systems. In addition, we have proved that the proposed algorithm can converge to the global optimal point under some sufficient conditions. Finally, several simulations are provided to demonstrate the correctness and effectiveness of the proposed algorithm.

关键词： Communication delays distributed algorithm economic dispatch (ED) microgrids multiagent systems

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distributed Gaussian Processes Hyperparameter Optimization for Big Data Using Proximal ADMM

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IEEE SIGNAL PROCESSING LETTERS 2019年第8期26卷 1197-1201页

作者： Xie, Ang Yin, Feng Xu, Yue Ai, Bo Chen, Tianshi Cui, Shuguang Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Chinese Univ Hong Kong Shenzhen 518172 Peoples R China Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

Hyperparameter optimization still remains the core issue in Gaussian processes (GPs) for machine learning. The classical hyperparameter optimization scheme based on maximum likelihood estimation is impractical for big data processing, as its computational complexity is cubic in terms of the number of data points. With the rapid development of efficient parallel data processing on ever cheaper and more powerful hardware, distributed models and algorithms will become ubiquitous. In this letter, we propose an alternative distributed GP hyperparameter optimization scheme using the efficient proximal alternating direction method of multipliers, proposed by Hong et al. in 2016, and we derive the closed-form solution for the local sub-problems. In contrast to the existing schemes of similar kind, our proposed one well balances the computational load on each local machine and the communication overhead required for global consensus of the local hyperparameter estimates. The proposed scheme can work in either a synchronous or an asynchronous manner, thus very flexible to be adopted in different computing facilities. Experimental results with both synthetic and real datasets validate the outstanding performance of the proposed scheme.

关键词： ADMM big data distributed algorithm Gaussian processes hyperparameter optimization

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