An experimental study of adaptive control for evolutionary algorithms

In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimization problems. We introduce new high level reactive search strategies based on a generic algorithm's controller that is able to schedule the basic variation operators of the evolutionary algorithm, according to the observed state of the search. Our experiments on SAT instances show that reactive search strategies improve the performance of the solving algorithm.     

Friction compensation controller for load varying machine tool feed drive

The load applied to a machine tool feed drive changes during the machining process as material is removed. This load change alters the Coulomb friction of the feed drive. Because Coulomb friction accounts for a large part of the total friction the friction compensation control accuracy of the feed drives is limited if this nonlinear change in the applied load is not considered. This paper presents a new friction compensation method that estimates the machine tool load in real time and considers its effect on friction characteristics. A friction observer based on a Kalman filter with load estimation is proposed for friction compensation control considering the applied load change. A specially designed feed drive testbed that enables the applied load to be modified easily was constructed for experimental verification. Control performance and friction estimation accuracy are demonstrated experimentally using the testbed.     

Modelling,simulation and identification of an engine air path electromechanical actuator

This paper deals with the modelling and the identification of an electromechanical Diesel engine actuator. The studied Bosch GPA-S actuator is designed for swirl/tumble flaps to control the air amount entering into the cylinder. This study aims to design a complete simulator that reproduces, with sufficient accuracy, the actuator dynamics taking into account the effects of the friction phenomenon. Hence, an overview of the actuator structure and its operation principle is first given. Then, its mathematical model as well as the nonlinearity, related to its behaviour, is discussed. Next, three identification procedures, which allow estimating both the system parameters and the friction model coefficients, are introduced. Finally, simulation results, using MATLAB, and experimental results, using LabVIEW, are presented demonstrating the effectiveness of the proposed techniques.     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

基于模糊判别的指纹识别算法探讨

指纹识别在社会各个领域应用都非常广泛.本文从指纹识别的原理和应用出发,介绍一种基于模糊判别的指纹识别算法.     

Adaptive user modelling in intelligent telephone and email assistants

With the burgeoning complexity and capabilities of modern information appliances and services, user modelling is becoming an increasingly important research area. Simple user profiles already personalise many software products and consumer goods such as digital TV recorders and mobile phones. A user model should be easy to initialise, and it must adapt in the light of interaction with the user. In many cases, a large amount of training data is needed to generate a user model, and adaptation is equivalent to retraining the system. This paper briefly outlines the user modelling problem and work done at BTexact on an intelligent personal assistant (IPA) which incorporates a user profile. We go on to describe FILUM, a more flexible method of user modelling, and show its application to the telephone assistant and email assistant components of the IPA, with tests to illustrate its usefulness.     

Evaluating the transfer of technology between application domains: a critical evaluation of the human component in the system

Technology can take many forms from the development of sophisticated electronics to the application of basic procedural principles. Technology transfer can also take several forms, however, in this case, it is the transfer of technological principles between application domains that is of primary interest. This paper presents a framework (the Five ‘M’s framework) that can be used to consider the likely success of technology transfer. The framework is based upon a sociotechnical system approach. The framework is illustrated with an evaluation of the introduction of intelligent cruise control into motor vehicles. It is argued that this is a technological concept first developed as an aerospace application in the form of the autopilot. In this case, it is argued that the transfer of this technology in its current form may potentially compromise safety.     

跟踪系统中跟踪器延迟的自适应预测补偿方法

跟踪系统中跟踪器的延迟会对系统的稳定性和跟踪精度产生不利影响。为此以典型Ⅱ型系统为例，理论上定理地分析了跟踪器延迟对跟踪系统的影响，并提出了将采用LMS算法和横向滤波器结构的自适应滤波器用于延迟预测补偿的方法。仿真结果验证了方法的有效性。     

Passive least-squares-type estimation algorithm for direct adaptive control

The standard least squares (LS) estimation algorithm is modified in such a way that it becomes passive. Therefore the scope of application of the LS algorithm is extended to those adaptive schemes whose convergence proof is based on passivity. The passive version of LS is presented in both continuous and discrete time.