摘要 :
Text summarization task involves condensing a given input single document or a set of documents into a shorter piece of textual summary(a.k.a. single-document or multi-document summarization),which preserves the main contents of t...
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Text summarization task involves condensing a given input single document or a set of documents into a shorter piece of textual summary(a.k.a. single-document or multi-document summarization),which preserves the main contents of the input.There are different Automatic Document Summarization(ADS,hereafter)is primarily a text compression mechanism to produce a shorter document to quickly access the important goals and main features of the input document. ADS is gaining researchers attention with the increasing volume of text documents all around us. With the advent of the 5G era,the text data generated from different sources including news,comments,and literature are growing explosively. Therefore,we have to spend a lot of time for finding the interesting information we need. Hence,it is very crucial to extract effective summary from these massive textual data.
Text summarization methods can be classified into abstractive and extractive summarization. An Abstractive Text Summarization(ATS)is an arbitrary text that describes the contexts of the source document. Extractive Text Summarization(ETS)consists in selecting the most important units(normally sentences)from the original text,but it must be done as closer as humans do. From these two summarization approaches,ETS has captured the research community attention,as it results the textual summary closer to the human being generated summary. However,ETS has multiple challenges:(ⅰ)generic formulations for text extraction,which leads to erroneous summarization;(ⅱ)existing methods generates domain-specific document summaries;and(ⅲ)mostly existing approaches are one-dimensional(i.e.,these approaches are static/fixed/biased).
In literature on ETS,several significant automatic approaches are suggested for text summarization,but few of them are focused on generating a better result rather than giving some assumptions about what human being use when producing a summary. In this thesis,a novel approach is suggested for a single document summarization using ETS. The proposed approach is based on particle swarm intelligence algorithm involving clustering mechanism. The most promising side of the proposed approach is that it dynamically extracts text using an efficient fitness function. The proposed algorithm works in three main phases. In first phase,the input document is preprocessed to make it ready for clustering and particle swarm intelligence processing. In next step,the preprocessed document is clustered using k-mean clustering algorithm using Google Normalized Distance as a distance measurement among the sentences. Once the clustered of the sentences are formed,then the proposed algorithm computes the different characteristics values of each sentence in the clusters. Further,these clusters are feed into particle swarm intelligence algorithm. The swarm intelligence returns the significant sentences as a summary of the document from the different clusters. Furthermore,the proposed approach sort the whole input document in such a way each sentence in the final output is similar to its neighboring sentences. Hence,it results in clusters of identical sentences. The importance of the sentences depends upon the density of the cluster,the denser the cluster is,the more important these sentences are.
The quality of the summary computed evaluated using different measures including ROUGE,F1-Score,Precision,and Recall. The computed results show the supremacy of the proposed approach. Furthermore,the proposed approach is also compared with the state-of-the-art ETS techniques. The results show that the proposed approach is efficient than the others.
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摘要 :
In the Mianhuatan hydro Power Plant,China,there is a growing interest in the investigation concerning the static and dynamic behaviour of the power plants.For this investigation,correctly developed nonlinear dynamic models of the ...
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In the Mianhuatan hydro Power Plant,China,there is a growing interest in the investigation concerning the static and dynamic behaviour of the power plants.For this investigation,correctly developed nonlinear dynamic models of the power plants are necessary.These models have to be reliable for all operating conditions of the system from zero to full loading.On the other hand,the complexity and the order of the models should not be too high,because,in some investigations,for example restoration scenarios,a lot of power plants will act together in reality oriented simulations.
The identification of the physical parameters of a synchronous generator was presented.The method is based on the fact that for the synchronous generator well-defined linear and nonlinear model structures exist.The Heffron-Phillips model is used to extract physical parameters from the identified transfer functions.The proposed method requires very simple test on the machine during normal operating conditions.
In this study,two identification methods(Particle swarm optimization,PSO and Genetic Algorithms,GA)have been used for the parameter identification of the hydro power plant,including the synchronous generator and its control(Excitation system and Govemor-Turbine System).In these methods,no matter what kind of input signals are used,the PSO can always obtain satisfactory identification results even if a strong noise exists.
It is clear from the results presented in this Dissertation that the advantages of PSO over other traditional optimization techniques can he summarized as follows:
a)PSO is a population-based search algorithm(i.e.,PSO works in an implicit parallel form).This property ensures that PSO is less susceptible by the local minimum trapping.
b)PSO uses payoff performance index(or objective function)information to guide the search in the problem space.Therefore,PSO can easily deal with non-differentiable objective functions.Additionally,this property relieves PSO from assumptions and approximations,which are often required for the traditional optimization models.
c)PSO works on the probabilistic transition rules and not deterministic rules.Hence,it is a kind of stochastic optimization algorithm that can search a complicated and uncertain area.This makes PSO more flexible than conventional methods.
d)Unlike Genetic Algorithm(GA)and other heuristic algorithms,PSO has the flexibility to control the balance between the global and local exploration in the search space.This unique feature of the PSO overcomes the premature convergence problem and enhances the search capability.
e)Unlike the traditional methods,the solution quality of the proposed approach doesn't rely on the initial population.Starting anywhere in the search space,the algorithm can ensure the convergence of the optimal solution.
In this dissertation,the modeling of hydro power plants is presented.It has been proved that the practical and user-friendly mathematical model of the complex system as it is a hydro power plant can be created.
The results of the work are very useful for the Mianhuatan hydro Power Plant.The results will be used for investigations in the following fields:
Black Start Capabilities after Black Out
Behavior in isolated operation
Primary and secondary power reserve capabilities
Reduction of losses in the plants
Overall dynamic behavior of the whole system including stability investigations.
Furthermore,these models can be practically used for expert's analysis and staff training in Mianhuatan hydro Power Plant.
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