This paper studies the quantized consensus problem for a group of agents over directed networks with switching topologies. We propose an effective distributed protocol with an adaptive finite-level uniform quantized s...
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ISBN:
(纸本)9781479900305
This paper studies the quantized consensus problem for a group of agents over directed networks with switching topologies. We propose an effective distributed protocol with an adaptive finite-level uniform quantized strategy, under which consensus among agents can be guaranteed without utilizing existing symmetry error-compensation method. We conduct convergence analysis based on related input-to-output stability result, which avoids the typical common left eigenvector requirement for the existence of common quadratic lyapunov function. In particular, it is established that, provided the duration of link failure in the directed network is bounded, then at each time instant, each agent (may be nonreciprocally) sends 5-level quantization information to each of its intimate neighbors, together with 3-level quantization information to itself, which suffices for ensuring consensus with an exponential convergence rate. The proposed quantized protocol features little communication protocol overhead and fits well into the digital network framework.
Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide...
Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China.
Based on biophoton analytical technology, the ultraweak photons emitted from the normal and insects-contaminated wheat are measured separately. Nine parameters of wheat self-illuminating characteristic are used as whe...
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Service provenance can be defined as a profile of service execution history. Queries of service provenance data can answer questions such as when and by whom a server is invoked? which services operate on this data? W...
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ISBN:
(纸本)9781479953288
Service provenance can be defined as a profile of service execution history. Queries of service provenance data can answer questions such as when and by whom a server is invoked? which services operate on this data? What might be the root cause for the service failure? Most of the organizations today collect and manage their own service provenance in order to trace service execution failures, locate service bottlenecks, guide resource allocation, detect and prevent abnormal behaviors. As services become ubiquitous, there is an increasing demand for proving service provenance management as a service. This paper describes ProvenanceLens, a two-tier service provenance management framework. The top tier is the service provenance capturing and storage subsystem and the next tier provides analysis and inference capabilities of service provenance data, which are value-added functionality for service health diagnosis and remedy. Both tiers are built based on the service provenance data model, an essential and core component of ProvenanceLens, which categorizes all service provenance data into three broad categories: basic provenance, composite provenance and application provenance. In addition, ProvenanceLens provides a suite of basic provenance operations, such as select, trace, aggregate. The basic provenance data is collected through a light-weight service provenance capturing subsystem that monitors service execution workflows, collects service profiling data, encapsulates service invocation dependencies. The composite and application provenance data are aggregated through a selection of provenance operations. We demonstrate the effectiveness of ProvenanceLens using a real world educational service currently in operation for a dozen universities in China.
The process flow and system structure of automatic batch weighing system are presented. In order to increase production speed and dosing accuracy, the multi-level dosing control model (high/low speed dosing + inching ...
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The process flow and system structure of automatic batch weighing system are presented. In order to increase production speed and dosing accuracy, the multi-level dosing control model (high/low speed dosing + inching dosing) is designed. Besides, the inching dosing mode is adopted to accurately compensate the weight deviation. In order to solve the problem that the fall of materials in-air cannot be easily controlled and out of tolerance. The multi-level dosing control model and preact will correct after each dosing dynamically with iteration method, moreover, the target value is predicted with second-order estimator, so as to increase the dosing speed with high weighing accuracy. The successful application proves that the control model can realize the rapid and accurate control of batch weighing process and has quite favourable control and reliability.
Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose...
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Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose a novel approach for the analysis of motion patterns by clustering the tracklets using an unsupervised hierarchical clustering algorithm, where the similarity between tracklets is measured by the Longest Common Subsequences. The tracklets are obtained by tracking dense points under three effective rules, therefore enabling it to capture the motion patterns in crowded scenes. The analysis of motion patterns is implemented in a completely unsupervised way, and the tracklets are clustered automatically through hierarchical clustering algorithm based on a graphic model. To validate the performance of our approach, we conducted experimental evaluations on two datasets. The results reveal the precise distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.
In this paper we present a new content-based retrieval descriptor, density-based silhouette descriptor (DBS). It characterizes a 3D object with multivariate probability functions of its 2D silhouette features. The new...
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ISBN:
(纸本)9789898565419
In this paper we present a new content-based retrieval descriptor, density-based silhouette descriptor (DBS). It characterizes a 3D object with multivariate probability functions of its 2D silhouette features. The new descriptor is computationally efficient and induces a permutation property that guarantees invariance at the matching stage. Also, it is insensitive to small shape perturbations and mesh resolution. The retrieval performance on several 3D databases shows that the DBS provides state-of-art discrimination over a broad and heterogeneous set of shape categories.
In this paper, development of ultra supercritical unit control is summarized. Based on analyzing the control difficulties and the input-output relationship of Ultra-supercritical Units, a model predictive control sche...
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Based on analyzing the characteristics of Ultra-supercritical unit, this paper introduced a multiple model MCPC (Multivariable Constrained Predictive control) structure with three inputs and three outputs for coordina...
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In the existing adaptively secure ABE (attribute-based encryption) schemes, the decryption cost goes linearly with the number of attributes that are used in decryption. An adaptively secure key-policy ABE (FKP-ABE) sc...
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