We experimentally show the performance of a Virtual Infinite Capacitor (VIC) in suppressing the voltage ripple. VIC mainly uses a bi-directional DC-DC converter with only two small capacitors inside to mimic the filte...
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ISBN:
(纸本)9788026106425
We experimentally show the performance of a Virtual Infinite Capacitor (VIC) in suppressing the voltage ripple. VIC mainly uses a bi-directional DC-DC converter with only two small capacitors inside to mimic the filtering process of a large capacitor by applying suitable control algorithms. It was firstly proposed in [1], [2] and then improved in [3] in both control algorithms and circuit designs. Based on VIC's control algorithm in [3], we injected the DC voltage together with a 50Hz, 85V (peak-to-peak) sinusoidal ripple to the VIC, and we achieved outstanding filtering performance in both the simulation and experiment. The 85V voltage ripple is eliminated and the DC voltage is extracted with no visible 50Hz ripples.
we herein propose a new collaborative framework, called cooperative co-evolutionary multi-agent system(CCEMAS), for solving multi-objective layout optimization problems. Every agent in CCEMAS is encoded as a multi-obj...
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ISBN:
(纸本)9781538616451
we herein propose a new collaborative framework, called cooperative co-evolutionary multi-agent system(CCEMAS), for solving multi-objective layout optimization problems. Every agent in CCEMAS is encoded as a multi-objective cooperative co-evolutionary strategy, including an algorithm and its setting. In the iterative procedure, the strategies will evolve along with the evolution of the agent team, and use different algorithms and settings during different stages of problem solving. A multi-objective optimization of satellite module is solved to validate the method and obtain the Pareto optimal solutions. Finally, the evolution process of multi-objective cooperative co-evolutionary strategies in agents is analyzed for constructing new multi-objective cooperative co-evolutionary algorithms for this problem in the future.
In a radio frequency identification system, The lower identification efficiency problem of system brought by multi tags collision has become the bottleneck problem of the application of RFID technolog
ISBN:
(纸本)9781467389808
In a radio frequency identification system, The lower identification efficiency problem of system brought by multi tags collision has become the bottleneck problem of the application of RFID technolog
Segregated targeting problems consist of a set of sources called internal sources and multiple sets of demands called zones. Each zone consists of a set of demands and a unique resource. Internal sources are allocated...
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ISBN:
(纸本)9781509043972
Segregated targeting problems consist of a set of sources called internal sources and multiple sets of demands called zones. Each zone consists of a set of demands and a unique resource. Internal sources are allocated to different zones such that the total resource requirement for the overall problem is minimized. In this paper, the problem definition is extended to include dedicated sources in each zone. These dedicated internal sources are specific to a zone in which they are present and are not shared with other zones. The primary objective of this paper is to develop an algorithm to determine the minimum resource requirement for the segregated problem with dedicated sources. Using rigorous mathematical arguments, a non-dimensional number is determined that dictates the allocation of an internal source to a zone for overall resource optimality. The applicability of the proposed algorithm is demonstrated through an illustrative example of carbon constrained energy sector planning.
Sparse subspace clustering (SSC) is an elegant approach for unsupervised segmentation if the data points of each cluster are located in linear subspaces. This model applies, for instance, in motion segmentation if som...
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ISBN:
(纸本)9781538628188
Sparse subspace clustering (SSC) is an elegant approach for unsupervised segmentation if the data points of each cluster are located in linear subspaces. This model applies, for instance, in motion segmentation if some restrictions on the camera model hold. SSC requires that problems based on the l(1)-norm are solved to infer which points belong to the same subspace. If these unknown subspaces are well-separated this algorithm is guaranteed to succeed. The question how the distribution of points on the same subspace effects their clustering has received less attention. One case has been reported in which points of the same model are erroneously classified to belong to different subspaces. In this work, it will be theoretically shown when and why such spurious clusters occur. This claim is further substantiated by experimental evidence. Two algorithms based on the Dantzig selector and subspace selector are proposed to overcome this problem, and good results are reported.
Automatic data classification is often performed by supervised learning algorithms, producing a model to classify new instances. Reflecting that labeled instances are expensive, semi supervised learning (SSL) methods ...
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ISBN:
(纸本)9781509049066
Automatic data classification is often performed by supervised learning algorithms, producing a model to classify new instances. Reflecting that labeled instances are expensive, semi supervised learning (SSL) methods prove to be an alternative to performing data classification, once the learning demands only a few labeled instances. There are many SSL algorithms, and graph-based ones have significant features. In particular, graph-based models grant to identify classes of different distributions without prior knowledge of statistical model parameters. However, a drawback that might influence their classification performance relays on the construction of the graph, which requires the measurement of distances (or similarities) between instances. Since a particular distance function can enhance the performance for some data sets and decrease to others, here, we introduce a novel approach, called GEAD, a Grammatical Evolution for Automatically designing Distance functions for Graph-based semi-supervised learning. We perform extensive experiments with 100 public data sets to assess the performance of our approach, and we compare it with traditional distance functions in the literature. Results show that GEAD is capable of designing distance functions that significantly outperform the baseline manually-designed ones regarding different predictive measures, such as Micro-F-1, and Macro-F-1.
There are several known methods for detecting overlapping communities in graphs, each one having their advantages and limitations. The Clique Percolation Method (CPM) is one such method. CPM works by joining highly co...
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ISBN:
(纸本)9781509046010
There are several known methods for detecting overlapping communities in graphs, each one having their advantages and limitations. The Clique Percolation Method (CPM) is one such method. CPM works by joining highly connected subgraphs (cliques) and using it to find the graph communities. However, the clique enumeration problem is NP-Hard, taking exponential time to be solved. This makes its use impractical in large real-world networks and applications. The aim of this paper is to present an efficient heuristic to enumerate cliques. This enables the Clique Percolation Method to detect overlapping communities in networks containing thousands of nodes. The analyses showed that our novel heuristic is competitive with other known methods regarding solution quality and we also make the CPM more scalable.
Traditional data mining algorithms are exceptional at seeing patterns in data that humans cannot, but are often confused by details that are obvious to the organic eye. algorithms that include humans "in-the-loop...
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ISBN:
(纸本)9781538627150
Traditional data mining algorithms are exceptional at seeing patterns in data that humans cannot, but are often confused by details that are obvious to the organic eye. algorithms that include humans "in-the-loop" have proved beneficial for accuracy by allowing a user to provide direction in these situations, but the slowness of human interactions causes execution times to increase exponentially. Thus, we seek to formalize frameworks that include humans "over-the-loop", giving the user an option to intervene when they deem it necessary while not having user feedback be an execution requirement. With this strategy, we hope to increase the accuracy of solutions with minimal losses in execution time. This paper describes our vision of this strategy and associated problems.
With cloud computing, efficient resource management is of great importance, as it has a direct impact on the scalability of the cloud application, and can result in significant energy and cost reductions. In recent ye...
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ISBN:
(纸本)9781538627846
With cloud computing, efficient resource management is of great importance, as it has a direct impact on the scalability of the cloud application, and can result in significant energy and cost reductions. In recent years, a lot of research has been done regarding the management of cloud resources, resulting in multiple novel resource allocation strategies. Validation of these strategies however is often only based on simulations, as large experiments using real cloud infrastructure are both expensive and time-consuming. In this demo we present RPiaaS, a low-cost and energy-efficient cloud testbed built using Raspberry Pi's. The testbed provides an easy-to-use environment for the initial evaluation of novel cloud resource management strategies, and is designed to facilitate the step from simulations towards experimental evaluations on larger cloud testbeds.
The enumeration of all maximal cliques in an undirected graph is a fundamental problem arising in several research areas. We consider maximal clique enumeration on shared-memory, multi-core architectures and introduce...
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ISBN:
(纸本)9781538606179
The enumeration of all maximal cliques in an undirected graph is a fundamental problem arising in several research areas. We consider maximal clique enumeration on shared-memory, multi-core architectures and introduce an approach consisting entirely of data-parallel operations, in an effort to achieve efficient and portable performance across different architectures. We study the performance of the algorithm via experiments varying over benchmark graphs and architectures. Overall, we observe that our algorithm achieves up to a 33-time speedup and 9-time speedup over state-of-the-art distributed and serial algorithms, respectively, for graphs with higher ratios of maximal cliques to total cliques. Further, we attain additional speedups on a GPU architecture, demonstrating the portable performance of our data-parallel design.
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