Overlapping community detection using core label propagation algorithm and belonging functions

Applied Intelligence - The community detection in complex networks has become a major field of research. Disjoint community detection deals often with getting a partition of nodes where every node...     

Non‐minimum phase switched systems: HOSM‐based fault detection and fault identification via Volterra integral equation

In this paper, the problem of continuous and discrete state estimation for a class of linear switched systems with additive faults is studied. The class of systems under study can contain non‐minimum phase zeroes in some of their ‘operating modes’. The conditions for exact reconstruction of the discrete state are given using structural properties of the switched system. The state space is decomposed into the strongly observable part, the non‐strongly observable part, and the unobservable part, to analyze the effect of the unknown inputs. State observers based on high‐order sliding mode to exactly estimate the strongly observable part and Luenberger‐like observers to estimate the remaining parts are proposed. For the case when the exact estimation of the state cannot be achieved, the ultimate bounds on the estimation errors are provided. The proposed strategy includes a high‐order sliding‐mode‐based fault detection and a fault identification scheme via the solution of a Volterra integral equation. The feasibility of the proposed method is illustrated by simulations. Copyright © 2013 John Wiley & Sons, Ltd.     

A proposed expert system for word sense disambiguation: deductive ambiguity resolution based on data mining and forward chaining

One of the major issues in the process of machine translation is the problem of choosing the proper translation for a multi‐sense word referred to as word sense disambiguation (WSD). Two commonly used approaches to this problem are statistical and example‐based methods. In statistical methods, ambiguity resolution is mostly carried out by making use of some statistics extracted from previously translated documents or dual corpora of source and target languages. Example‐based methods follow a similar approach as they also make use of bilingual corpora. However, they perform the task of matching at run‐time (i.e. online matching). In this paper, by looking at the WSD problem from a different viewpoint, we propose a system, which consists of two main parts. The first part includes a data mining algorithm, which runs offline and extracts some useful knowledge about the co‐occurrences of the words. In this algorithm, each sentence is imagined as a transaction in Market Basket Data Analysis problem, and the words included in a sentence play the role of purchased items. The second part of the system is an expert system whose knowledge base consists of the set of association rules generated by the first part. Moreover, in order to deduce the correct senses of the words, we introduce an efficient algorithm based on forward chaining in order to be used in the inference engine of the proposed expert system. The encouraging performance of the system in terms of precision and recall as well as its efficiency will be analysed and discussed through a set of experiments.     

A control scheme to enhance low voltage ride‐through of brushless doubly‐fed induction generators

The use of brushless doubly‐fed induction generator has been recently proposed for wind turbines because of its variable speed operation with fractional size converter without the need to brush and slip ring. This paper introduces a control scheme to improve low voltage ride‐through capability of doubly‐fed induction generator considering grid code requirements. The proposed control strategy is based on analysis of flux linkages and back electromotive forces and intends to retain the control‐winding current below the safety limit (typically 2 pu) during severe voltage dips. The time‐domain simulations validate effectiveness of the proposed scheme to protect the converter against failure as well as support reactive power required by German grid code. Copyright © 2015 John Wiley & Sons, Ltd.     

A Fast Flux Search Controller for DTC-Based Induction Motor Drives

In this paper, a flux search controller is proposed to increase the efficiency of a direct torque-controlled induction motor in full load range. The value of the flux reference is determined through a minimization algorithm. The amplitude of stator current is used as the objective function since it is shown that samples of the stator current have better statistical properties than input power. In addition, the stator current has more sensitivity to the flux variation than input power. These two properties allow implementing an adaptive algorithm to determine the proper flux step without waste of time. The minimum allowable value of the flux step has been determined based on this process to prevent the divergence problem. This adaptive algorithm set a large flux step for transient state to speed up the convergence process and a small flux step for steady state to minimize the flux ripple. Simulation and experimental results presented in this paper confirm the fast dynamic of the proposed method.     

Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds

We propose “supervised principal component analysis (supervised PCA)”, a generalization of PCA that is uniquely effective for regression and classification problems with high-dimensional input data. It works by estimating a sequence of principal components that have maximal dependence on the response variable. The proposed supervised PCA is solvable in closed-form, and has a dual formulation that significantly reduces the computational complexity of problems in which the number of predictors greatly exceeds the number of observations (such as DNA microarray experiments). Furthermore, we show how the algorithm can be kernelized, which makes it applicable to non-linear dimensionality reduction tasks. Experimental results on various visualization, classification and regression problems show significant improvement over other supervised approaches both in accuracy and computational efficiency.     

A note on interval observer design for unknown input estimation

This paper addresses the problem of interval observer design for unknown input estimation in linear time-invariant systems. Although the problem of unknown input estimation has been widely studied in the literature, the design of joint state and unknown input observers has not been considered within a set-membership context. While conventional interval observers could be used to propagate with some additional conservatism, unknown inputs by considering them as disturbances, the proposed approach allows their estimation. Under the assumption that the measurement noise and the disturbances are bounded, lower and upper bounds for the unmeasured state and unknown inputs are computed. Numerical simulations are presented to show the efficiency of the proposed approach.     

One-off or batch upgrading of complex products and systems: concepts,data model and analysis framework

One-off or Batch upgrading is the term given to the process of improving one or several operational instances of complex products or systems (e.g., trains, aircrafts, etc.) during their life span. Other products such as cars or personal computers are regularly improved through redesign, generation after generation; however, redesign never concerns only one or several instance(s). The main challenge presented in this paper is to provide a suitable framework to analyse one-off and batch upgrading processes. This framework targets at containing required concepts and drivers for upgrade problem posing. The axiom beneath this research is that the goal of upgradings is to improve the performances of products or systems. Upgrade-to-performance, UtP in short, represents this paradigm that will be defined and illustrated through examples. To reach this goal, necessary concepts are defined and linked together through a generic data model. The UtP data model can be used in a conceptual way clarifying the scope of the upgrading process. It can also be specialized to specific industrial domain and used as the main driver of the design of a database for a given upgrading projects. The use case reported concerns the conversion of a passenger train into an express freight where the focus is put on enlarging a coach’ door. The results are then discussed to figure out the links between the UtP problem-posing and problem-solving methods and the gaps to fill. Finally, further research niches are described.     

A model-based technique for early and robust detection of oscillatory failure case in A380 actuators

This paper addresses the problem of Oscillatory Failure Cases (OFC) detection in the Electrical Flight Control System (EFCS) of the Airbus airplanes. OFC can lead to strong interactions with loads and aero-elasticity and consequently are to be detected very early in time. The work describes the status of on going research activity undertaken within a collaborative project between Bordeaux University (France) and Airbus. An hydraulic actuator model is currently used as the basis for a robust analytical redundancy-based technique implemented in A380 Flight Control Computer (FCC) for detecting unauthorized oscillatory events. For upcoming and future generation aircraft (A/C), it could be required to detect OFC earlier with less important amplitude. The method presented here is based on nonlinear state space modeling, associated with the same decision test as used by in-service Airbus A/C. It is shown that the model quality could be improved significantly by reliable estimating of some physical parameters. The fault indicating signals are compared on data set obtained from A380 computers during flight tests.     

Fault diagnosis and monitoring of oscillatory failure case in aircraft inertial system

A method for oscillatory fault detection and isolation is presented and used to detect oscillatory failures of redundant aircraft sensors involved in the computation of flight control laws. The objective is to switch off the erroneous sensor and to compute a consolidated parameter using data from valid sensors, in order to eliminate any anomaly before propagation in the control loop. The benefit of the presented method is to improve the consolidation process with a fault detection and isolation approach when only few sources (less than three) are valid. Different techniques are compared to accurately detect any behavioral change of the sensor outputs. The approach is validated on a normalized real flight data set.