The application based on P2P technology is one of the most important application of the Internet, but it is often affected by the node failure or abnormal nodes exit. In P2P network, node failure can cause data loss a...
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We report about the results of the application of modern computer algebra tools for construction of directed strongly regular graphs. The suggested techniques are based on the investigation of noncommutative associati...
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In this paper, we consider the asymptotic stabilization of a class of one dimensional boundary controlled port Hamiltonian systems by an immersion/reduction approach and the use of Casimir invariants. We first extend ...
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The reliability theory used in the design of complex systems including electric grids assumes random component failures and is thus unsuited to analyzing security risks due to attackers that intentionally damage sever...
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ISBN:
(纸本)9781479924004
The reliability theory used in the design of complex systems including electric grids assumes random component failures and is thus unsuited to analyzing security risks due to attackers that intentionally damage several components of the system. In this paper, a security risk analysis methodology is proposed consisting of vulnerability analysis and impact analysis. Vulnerability analysis is a method developed by security engineers to identify the attacks that are relevant for the system under study, and in this paper, the analysis is applied on the communications network topology of the electric grid automation system. Impact analysis is then performed through co-simulation of automation and the electric grid to assess the potential damage from the attacks. This paper makes an extensive review of vulnerability and impact analysis methods and relevant system modeling techniques from the fields of security and industrial automationengineering, with a focus on smart grid automation, and then applies and combines approaches to obtain a security risk analysis methodology. The methodology is demonstrated with a case study of fault location, isolation and supply restoration smart grid automation.
Context-aware plays a key role in the wisdom network information processing. Due to limited resources, context conflict is inevitable in context-aware. User satisfaction is a good reflection for the wisdom degree of t...
Context-aware plays a key role in the wisdom network information processing. Due to limited resources, context conflict is inevitable in context-aware. User satisfaction is a good reflection for the wisdom degree of the network. In this paper, through considering the user satisfaction, we propose a novel context-aware conflict solution model based on SPA(set pair Analysis). The model obtain the best server mode by the maximum satisfaction connection degree to solve the conflict in context-aware. The simulation analysis show that the proposed method is simple, effective and can solve many users compete for the same scene of conflict.
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.
This paper presents a framework for the nonlinear control of dual-stage actuators (DSA). Motivated by various nonlinear controllers that make use of sector bounded and £ ∞ nonlinearities for the control of satur...
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This paper presents a framework for the nonlinear control of dual-stage actuators (DSA). Motivated by various nonlinear controllers that make use of sector bounded and £ ∞ nonlinearities for the control of saturated linear systems, a methodology for integrating such nonlinear functions in order to improve the performance of DSA is presented. The stability of the closed-loop system is assessed by casting the nonlinearities in a mixed sector-bounded plus quasi-Linear Parameter Varying (LPV) framework, leading to a set of linear matrix inequalities (LMIs) to be satisfied by the controller parameters. Taking advantage of the developed framework, a new £ ∞ function is proposed to avoid the saturation of the secondary actuator. Simulation results illustrate the validity of the proposed framework and its potential for the performance improvement of DSA.
If potential contributors leading to system failure can be identified when a scientific workflow is modelled, a lot of system vulnerabilities may thus be revealed and improved. In this paper, we first use data depende...
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Sunspots are dark areas with respect to their surrounding area because the temperature of sunspot areas is lower than the average temperature of the solar surface. It provides essential information for many aspects of...
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ISBN:
(纸本)9781479947249
Sunspots are dark areas with respect to their surrounding area because the temperature of sunspot areas is lower than the average temperature of the solar surface. It provides essential information for many aspects of solar physics. A sunspot is composed of umbra and penumbra. It is a prerequisite for studying solar physics and spatial atmosphere to accurately segment and extract sunspot structures. Consequently, we propose an automated detection technique for segmenting and extracting them. The detection procedure is composed of two steps:(1) segmenting and extracting sunspot umbra with morphological reconstruction;(2) detecting and segmenting sunspot penumbra with region growing. For evaluating the accuracy of the detection procedure,we used a high-resolution observation obtained by Solar Optical Telescope onboard Hinode, and other obtained with the Dutch Open Telescope to illustrate the performance. The results demonstrate that our proposed technique is significant effective and accurate, and is suitable for studying the sunspot evolution and their physical phenomena.
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