Effects of Three Typical Resistivity Models on Pulsed Inductive Plasma Acceleration Modeling

The effects of three different typical resistivity models（Spitzer, Z＆L and M＆G） on the performance of pulsed inductive acceleration plasma are studied. Numerical results show that their influences decrease with the increase of the plasma temperature. The significant discriminations among them appear at the plasma temperature lower than 2.5 eV, and the maximum gap of the pulsed inductive plasma accelerated efficiency is approximately 2.5%.Moreover, the pulsed inductive plasma accelerated efficiency is absolutely related to the dynamic impedance parameters, such as voltage, inductance, capacitance and flow rate. However, the distribution of the efficiency as a function of plasma temperature with three resistivity models has nothing to do with the dynamic impedance parameter.

Model of DFIG Wind Farm and Study on Its LVRT Capability

As a typical clean and renewable energy, wind power is becoming more and more widely used in electrical industry. However, its characteristics of random and intermittent have brought serious problems to the power system, such as voltage fluctuation and insufficient reactive power. Based on the K-means clustering algorithm, this paper classifies the doubly-fed induction generators （DFIG） according to the operation of propeller pitch angle control. At the same time, to obtain the optimal parameter, advanced particle swarm optimization （PSO） is used. Then the dynamic model of DFIG under the network fault condition is built. What is more, the role that crowbar circuit plays in low voltage ride through （LVRT） is discussed. Finally, simulations in DigSILENT verify the model.

Efficiency of Model Induction Motor Using Various Non-Oriented Electrical Steels

The performance of a 3-phase 6-pole 400 W inverter-drive induction motor was investigated using a variety of non-oriented electrical steels for stator core at PWM inverter fundamental wave frequencies of 30 to 300 Hz. There existed an optimum Si content of the material depending on the tooth flux density. Both reduction of material thickness and stress-relief annealing of the stator core improved the motor efficiency. The influence of Si content on the efficiency was small at lower PWM frequencies, while at higher frequencies the motor efficiency increased with increasing Si content. The Cu loss WC increased and the Fe loss Wi counteractiveiy decreasedwith increasing Si content at lower frequencies; while at higher frequencies Wi had dominant effect on the efficiency. Newly developed materials RMA, having lower Fe losses after stress-relief annealing and higher flux densities with lower Si contents, showed motor efficiencies superior to conventional J1S grade materials with comparable Fe losses.

Causal association rule mining methods based on fuzzy state description

Aiming at the research that using more new knowledge to develope knowledge system with dynamic accordance, and under the background of using Fuzzy language field and Fuzzy language values structure as description framework, the generalized cell Automation that can synthetically process fuzzy indeterminacy and random indeterminacy and generalized inductive logic causal model is brought forward. On this basis, a kind of the new method that can discover causal association rules is provded. According to the causal information of standard sample space and commonly sample space, through constructing its state （abnormality） relation matrix, causal association rules can be gained by using inductive reasoning mechanism. The estimate of this algorithm complexity is given,and its validiw is proved through case.

Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Algorithms for numeric data classification have been applied for text classification. Usually the vector space model is used to represent text collections. The characteristics of this representation such as sparsity and high dimensionality sometimes impair the quality of general-purpose classifiers. Networks can be used to represent text collections, avoiding the high sparsity and allowing to model relationships among different objects that compose a text collection. Such network- based representations can improve the quality of the classification results. One of the simplest ways to represent textual collections by a network is through a bipartite heterogeneous network, which is composed of objects that represent the documents connected to objects that represent the terms. Heterogeneous bipartite networks do not require computation of similarities or relations among the objects and can be used to model any type of text collection. Due to the advantages of representing text collections through bipartite heterogeneous networks, in this article we present a text classifier which builds a classification model using the structure of a bipartite heterogeneous network. Such an algorithm, referred to as IMBHN （Inductive Model Based on Bipartite Heterogeneous Network）, induces a classification model assigning weights to objects that represent the terms for each class of the text collection. An empirical evaluation using a large amount of text collections from different domains shows that the proposed IMBHN algorithm produces significantly better results than k-NN, C4.5, SVM, and Naive Bayes algorithms.

Estimation of composite load model with aggregate induction motor dynamic load for an isolated hybrid power system

It is well recognized that the voltage stability of a power system is affected by the load model and hence, to effectively analyze the reactive power compensation of an isolated hybrid wind-diesel based power system, the loads need to be considered along with the generators in a transient analysis. This paper gives a detailed mathematical modeling to compute the reactive power response with small voltage perturbation for composite load. The composite load is a combination of the static and dynamic load model. To develop this composite load model, the exponential load is used as a static load model and induction motors （IMs） are used as a dynamic load model. To analyze the dynamics of IM load, the fifth, third and first order model of IM are formulated and compared using differential equations solver in Matlab coding. Since the decentralized areas have many small consumers which may consist large numbers of IMs of small rating, it is not realistic to model either a single large rating unit or all small rating IMs together that are placed in the system. In place of using a single large rating IM, a group of motors are considered and then the aggregate model of IM is developed using the law of energy conservation. This aggregate model is used as a dynamic load model. For different simulation studies, especially in the area of voltage stability with reactive power compensation of an isolated hybrid power system, the transfer function AQ/AV of the composite load is required. The transfer function of the composite load is derived in this paper by successive derivation for the exponential model of static load and for the fifth and third order IM dynamic load model using state space model.

A Novel Similarity Measure to Induce Semantic Classes and Its Application for Language Model Adaptation in a Dialogue System

In this paper,we propose a novel co-occurrence probabilities based similarity measure for inducing semantic classes.Clustering with the new similarity measure outperforms the widely used distance based on Kullback-Leibler divergence in precision,recall and F1 evaluation.In our experiments,we induced semantic classes from unannotated in-domain corpus and then used the induced classes and structures to generate large in-domain corpus which was then used for language model adaptation.Character recognition rate was improved from 85.2% to 91%.We imply a new measure to solve the lack of domain data problem by first induction then generation for a dialogue system.

An Algebraic Characterization of Inductive Soundness in Proof by Consistency

Kapur and Musser studied the theoretical basis for proof by consistency and ob-tained an inductive completeness result: p q if and only if p = q is true in everyinductive model. However, there is a loophole in their proof for the soundness part:p = q implies p = q is true in every inductive model. The aim of this paper is to give acorrect characterization of inductive soundness from an algebraic view by introducingstrong inductive models.