distributed execution of algorithms over various terminals is a topic that regains increasing popularity;when tolerance to failures is also required, asynchronous operation is brought to the light, while probabilistic...
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distributed execution of algorithms over various terminals is a topic that regains increasing popularity;when tolerance to failures is also required, asynchronous operation is brought to the light, while probabilistic asynchronous operation can model the probability of failure for each terminal. This work focuses on the probabilistic asynchronous affine update model, applicable in a wide range of inference algorithms, possibly executed over distributed terminals. The existing literature focuses on the asymptotic properties of the expected mean. Instead, this work offers the asymptotic analysis for the arithmetic mean, utilized for discovering fixed points, as it is the only quantity that can be practically offered experimentally. It is shown that the asymptotic behavior of the arithmetic mean is different than the expected mean's and a sufficient condition is provided for convergence of the arithmetic mean to a fixed point. The lack of necessity for this condition is explained and the subcases, where the arithmetic mean converges, diverges or has an unpredictable behavior, are distinguished. Additionally, cases where the individual iterations never converge (e.g., oscillate infinitely) but their arithmetic mean does and offers fixed point, are also highlighted. This is another concrete example of the arithmetic mean utility. Applications of the affine model are also briefly discussed. Finally, simulations corroborate theoretical findings for various affine model setups.
The multicasting is defined as the distribution of the same information stream from one to many nodes concurrently. There has been an intensive research effort to design protocols and construct multicast routing graph...
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The multicasting is defined as the distribution of the same information stream from one to many nodes concurrently. There has been an intensive research effort to design protocols and construct multicast routing graphs for a single multicast group. In this paper, the multiple multicast tree allocation problem is discussed and algorithms are proposed to solve the congestion problem in the IP network. As the congestion measure the minimum residual capacity is considered. Two phase algorithm MMTA is investigated for multiple multicast tree allocation both for identical and different bandwidth requirement by the multicast groups. The central and distributed implementation of the multiple multicast tree is discussed for the deployment in the real IP network. The performance of the proposed MMTA is compared with other procedures. Computational results show that the two-phase MMTA outperforms other procedures. Approximately 3-7% improvement in the residual capacity is obtained by the MMTA. The solution gap from the upper bound by the well-known branch and bound is within 2-11% depending on the problem size. (C) 2003 Elsevier Ltd. All rights reserved.
This paper aims at minimizing economical cost of a microgrid by jointly scheduling various devices, e.g., appliances, batteries, thermal generators, and wind turbines. To properly model the system, the characteristics...
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This paper aims at minimizing economical cost of a microgrid by jointly scheduling various devices, e.g., appliances, batteries, thermal generators, and wind turbines. To properly model the system, the characteristics of all devices is fully investigated;in particular, the chance constraint is introduced to capture the randomness of power generation of wind turbines. Then, this problem is formulated as a large-scale mixed-integer program with coupling constraints. In order to solve this problem efficiently, it is decoupled via dual decomposition into a set of sub-problems to be solved distributedly on each appliance, battery, and generator. While the scheduling of generator is well studied in literature, this paper specially proposes an efficient method for appliance scheduling, and then employs Benders' decomposition for battery scheduling. The performance of the proposed approach is verified by numerical simulations.
Efficient spreading of important information through social media can be highly beneficial, while quick spreading of false content is alarming. Finding the users who are the most influential at information spreading c...
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Efficient spreading of important information through social media can be highly beneficial, while quick spreading of false content is alarming. Finding the users who are the most influential at information spreading can help develop efficient strategies. However, with the increasing growth of gigantic social networks, existing methods either lack accuracy or have high latency, sometimes being infeasible within limited memory. In this study, we find that rich user-specific information can guide us toward designing more effective methods. We propose UACD, a novel method for identifying the most influential spreaders on the Twitter social network by combining both user-specific and topological information. We provide a distributed implementation of our proposed algorithm on the Amazon EC2 and compare our ranking result with the state-of-the-art methods. Results suggest that UACD is scalable and can process a very large network while being on average 12.5% more accurate and 175x faster.
The rise of microgrid-based architectures is modifying significantly the energy control landscape in distribution systems, making distributed control mechanisms necessary to ensure reliable power system operations. In...
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The rise of microgrid-based architectures is modifying significantly the energy control landscape in distribution systems, making distributed control mechanisms necessary to ensure reliable power system operations. In this article, the use of Reinforcement Learning techniques is proposed to implement load frequency control (LFC) without requiring a central authority. To this end, a detailed model of power system dynamic behaviour is formulated by representing individual generator dynamics, generator rate and network constraints, renewable-based generation, and realistic load realisations. The LFC problem is recast as a Markov Decision Process, and the Multi-Agent Deep Deterministic Policy Gradient algorithm is used to approximate the optimal solution of all LFC layers, that is, primary, secondary and tertiary. The proposed LFC framework operates through centralised learning and distributed implementation. In particular, there is no information interchange between generating units during operation. Thus, no communication infrastructure is necessary and information privacy between them is respected. The proposed framework is validated through numerical results and it is shown that it can be used to implement LFC in a distributed and cost-efficient manner.
The intensive research and development efforts directed towards large-scale complex industrial systems in the context of Industry 4.0 indicate that safety and reliability issues pose significant challenges. During onl...
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The intensive research and development efforts directed towards large-scale complex industrial systems in the context of Industry 4.0 indicate that safety and reliability issues pose significant challenges. During online operation, system performance degradation will lead, not only to economic losses, but also potential safety hazards. In the existing research and technical routes, the target of the fault diagnosis systems is to trigger alarms to report the fact of the existence of malfunctions as well as the underlying reasons accurately. However, it remains unanswered how urgent it is to fix it, and what degrees of fault-tolerance, maintenance, and fault recovery are needed. Further analyses are necessary to evaluate the impact of the detected fault on the plant-wide performance. In this article, to enable a more comprehensive and precise description of the plant-wide operational status, the roles of the commonly used performance metrics, the state-of-the-art performance evaluation approaches, as well as the performance-oriented and plant-wide process monitoring techniques are investigated. On this basis, an alternative straightforward technical route, embedded in the cyber-physical-social system framework is proposed. A roadmap including the key research questions, the future research directions, and an outlook about the future vision is presented.
A distributed implementation of Dang's Fixed-Point iterative method is proposed to solve the market split problem which has been considered as a benchmark and a challenge to the algorithms solving linear systems w...
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ISBN:
(纸本)9781479913343
A distributed implementation of Dang's Fixed-Point iterative method is proposed to solve the market split problem which has been considered as a benchmark and a challenge to the algorithms solving linear systems with 0/1 variables. There are two steps to solve the market split problem in this paper. The first step is converting the problem to a reformulated polytope judgement problem based on lattice basis reduction. In the next step, the distributed Dang's method is used to judge whether there exits an integer point in the polytope. This is the first distributed implementation to solve the market split problem to our knowledge. Numerical results show that the approach is effective and it is more powerful than CPLEX to solve the market split problem.
In the design of a heterogeneous multiprocessor system on chip, we face a new design problem;scheduler implementation. In this paper, we present an approach to implementing a static scheduler, which controls all the t...
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ISBN:
(纸本)0780387368
In the design of a heterogeneous multiprocessor system on chip, we face a new design problem;scheduler implementation. In this paper, we present an approach to implementing a static scheduler, which controls all the task executions and communication transactions of a system according to a pre-determined schedule. For the scheduler implementation, we consider both intra-processor and inter-processor synchronization. We also consider scheduler overhead, which is often neglected. In particular, we address the issue of centralized implementation versus distributed implementation. We investigate the pros and cons of the two different scheduler implementations. Through experiments with synthetic examples and a real world multimedia application, we show the effectiveness of our approach.
In this paper an efficient dynamic segregative genetic algorithm for optimizing variable order in Reduced Ordered Binary Decision Diagrams is presented. The approach integrates a basic genetic algorithm and uses a fea...
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ISBN:
(纸本)9781450311779
In this paper an efficient dynamic segregative genetic algorithm for optimizing variable order in Reduced Ordered Binary Decision Diagrams is presented. The approach integrates a basic genetic algorithm and uses a feature function in order to define a similarity measure between chromosomes. Subpopulations of individuals, formed by applying a clustering procedure in the feature space, are explored in parallel by multiple copies of the basic genetic algorithm. A communication protocol preserves the similarity inside each subpopulation during the evolution process. The redundant exploration of the search space is avoided by using a tabu search associative memory. Genetic material from yet unexplored regions of the search space is managed and organized in order to explicitly guide the search process to yet undiscovered local optima. The experimental evaluation of the algorithm uses classical benchmark problems, known to be very difficult. Experiments suggest that our approach has a better performance in terms of stability and quality of the solution, when compared to other heuristics, such as local search methods, basic genetic algorithms, a cellular genetic algorithm and even the static segregative genetic algorithm that was the starting point of this work. The quality of the distributed implementation and the communication protocol are thoroughly analyzed.
We propose a system-level design flow for building automation and control (BAC) systems. The input to the design flow is a high level description of the control algorithms given in a model-based environment such as Si...
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ISBN:
(纸本)9780769542980
We propose a system-level design flow for building automation and control (BAC) systems. The input to the design flow is a high level description of the control algorithms given in a model-based environment such as Simulink. The input specification is translated into an intermediate format, and then automatically refined into a distributed implementation. Refinement includes optimal mapping of the functional specification on a set of computation and communication resources, and software synthesis, which generates code for each component in the mapped design while guaranteeing semantic equivalence with the original specification. Experiments with a temperature control system are presented to illustrate the flow.
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