After the TMI accident,many alarm reduction systems and diagnostic systems have been studied to reduce nuisance alarms and to detect the causes of an abnormal *** systems provide an operator with information on signif...
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After the TMI accident,many alarm reduction systems and diagnostic systems have been studied to reduce nuisance alarms and to detect the causes of an abnormal *** systems provide an operator with information on significant alarms or causes of an abnormal state for an operator to identify that *** this paper,an operator-aid system,Logic Alarm Cause Tracking System(LogACTs),is proposed for tracking the logics of an alarm,finding the causes of an alarm,displaying the highlighted alarm procedure related to the causes,and suppressing and filtering nuisance alarms due to the physical or logical connections between components or systems in an abnormal *** system can be used by an operator to identify the detailed causes of an alarm without checking all the causes of the candidates by *** proposed system will be applied to a Korean Standard Nuclear Power Plant of a PWR,ShinHanul 1&2 Nuclear Power Plant.
Clustering analysis is an important tool of data mining. The study on efficient clustering has great significance, especially in improving a clustering algorithm's adaptability and usefulness. Clustering ensemble ...
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Clustering analysis is an important tool of data mining. The study on efficient clustering has great significance, especially in improving a clustering algorithm's adaptability and usefulness. Clustering ensemble (CE) integrates several clustering algorithms such that the clustering results can be effectively improved. This work investigates similarity-based methods and proposes a new method called weight- incorporated similarity-based clustering ensemble (WSCE). Six classic data sets are used to test single clustering algorithms, similarity-based one, and the proposed one via simulation. The results prove the validity and performance advantage of the proposed method.
For most practical nonlinear state estimation problems, the conventional nonlinear filters do not usually work well for some cases, such as inaccurate system model, sudden change of state-interested and unknown varian...
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For most practical nonlinear state estimation problems, the conventional nonlinear filters do not usually work well for some cases, such as inaccurate system model, sudden change of state-interested and unknown variance of measurement noise. In this paper, an adaptive cubature strong tracking information filter using variational Bayesian (VB) method is proposed to cope with these complex cases. Firstly, the strong tracking filtering (STF) technology is used to integrally improve performance of cubature information filter (CIF) aiming at the first two cases and an iterative scheme is presented to effectively evaluate a strong tracking fading factor. Secondly, the VB method is introduced to iteratively evaluate the unknown variance of measurement noise. Finally, the novel adaptive cubature information filter is obtained by perfectly combining the STF technology with the VB method, where the variance estimation provided by the VB method guarantees normal running of the strong tracking functionality.
In this paper we deal with container assignment optimization on an intermodal network. We propose a linear programming model, following a frequency based approach, addressing both the maritime and the inland component...
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In this paper we deal with container assignment optimization on an intermodal network. We propose a linear programming model, following a frequency based approach, addressing both the maritime and the inland component, and taking into account custom times at ports and service frequencies. The proposed arc-based formulation, in which only variables related to arcs which actually exist are explicitly created, is particularly suitable for very large but sparse networks, typical in maritime long distance transport, because it allows strongly reducing the number of variables involved. Finally, we discuss computational results obtained on a real size instance.
Alzheimer's Disease (AD) and its preliminary stage - Mild Cognitive Impairment (MCI) - are the most widespread neurodegenerative disorders, and their investigation remains an open challenge. ElectroEncephalography...
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Alzheimer's Disease (AD) and its preliminary stage - Mild Cognitive Impairment (MCI) - are the most widespread neurodegenerative disorders, and their investigation remains an open challenge. ElectroEncephalography (EEG) appears as a non-invasive and repeatable technique to diagnose brain abnormalities. Despite technical advances, the analysis of EEG spectra is usually carried out by experts that must manually perform laborious interpretations. Computational methods may lead to a quantitative analysis of these signals and hence to characterize EEG time series. The aim of this work is to achieve an automatic patients classification from the EEG biomedical signals involved in AD and MCI in order to support medical doctors in the right diagnosis formulation. The analysis of the biological EEG signals requires effective and efficient computer science methods to extract relevant information. Data mining, which guides the automated knowledge discovery process, is a natural way to approach EEG data analysis. Specifically, in our work we apply the following analysis steps: (i) pre-processing of EEG data; (ii) processing of the EEG-signals by the application of time-frequency transforms; and (iii) classification by means of machine learning methods. We obtain promising results from the classification of AD, MCI, and control samples that can assist the medical doctors in identifying the pathology.
Repetitive processes are a class of two-dimensional systems that arise in the modeling of physical examples and also the control systems theory developed for them has, in the case of linear dynamics, been applied to d...
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In multi-media and social media communities, web topic detection poses two main difficulties that conventional approaches can barely handle: 1) there are large inter-topic variations among web topics;2) supervised inf...
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ISBN:
(纸本)9781479947607
In multi-media and social media communities, web topic detection poses two main difficulties that conventional approaches can barely handle: 1) there are large inter-topic variations among web topics;2) supervised information is rare to identify the real topics. In this paper, we address these problems from the similarity diffusion perspective among objects on web, and present a clustering-like pattern across similarity cascades (SCs). SCs are a series of subgraphs generated by truncating a weighted graph with a set of thresholds, and then maximal cliques are used to describe the topic candidates. Poisson deconvolution is adopted to efficiently identify the real topics from these topic candidates. Experiments demonstrate that our approach outperforms the state-of-the-arts on two datasets. In addition, we report accuracy v.s. false positives per topic (FPPT) curves for performance evaluation. To our knowledge, this is the first complete evaluation of web topic detection at the topic-wise level, and it establishes a new benchmark for this problem.
Network Functions Virtualization can enable each user (tenant) to define his desired set of network services, called (network) service graph. For instance, a User1may want his traffic to traverse a firewall before rea...
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Network Functions Virtualization can enable each user (tenant) to define his desired set of network services, called (network) service graph. For instance, a User1may want his traffic to traverse a firewall before reaching his terminal, while a User2 may be interested in a different type of firewall and in a network monitor as well. This paper presents a prototype of an SDN-enabled node that, given anew user connected to one of its physical ports, it is able to dynamically instantiate the user's network service graph and force all his traffic to traverse the proper set of network functions.
According to the property-rights model of cognitive radio, primary users （PUs） who own the spectrum resource have the right to lease part of spectrum to secondary users （SUs） in exchange for appropriate profit. In...
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According to the property-rights model of cognitive radio, primary users （PUs） who own the spectrum resource have the right to lease part of spectrum to secondary users （SUs） in exchange for appropriate profit. In this paper, we propose a pricing-based spectrum leasing framework between one PU and multiple SUs. In this scenario, the PU attempts to maximize its utility by setting the price of spectrum. Then, the selected SUs have the right to decide their power levels to help PU s transmission, aiming to obtain corresponding access time. The spectrum leasing problem can be cast into a stackelberg game, where the PU plays the seller-level game and the selected SUs play the buyer-level game. Through analysis based on the backward induction, we prove that there exists a unique equilibrium in the stackelberg game with certain constraints. Numerical results show that the proposed pricing-based spectrum leasing framework is effective, and the performance of both PU and SUs is improved, compared to the traditional mechanism without cooperation.
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