1,720,963 research outputs found
Neurofuzzy modelling and control
No full textAn increase in the sophistication of control systems, both in terms of performance and flexibility is required if the current desire for autonomy and inherent inbuilt intelligence in manufacturing and manufactured goods is to be realised. These new control systems will have to deal with ill-defined complex dynamics and non-linear relationships over wide operating envelopes. Recent developments in neural networks and fuzzy logic present opportunities for a radical innovation in control system design and management. The developments have already provided improved product quality features, and even more importantly they have presented the opportunity to provide near optimal control of complex hierarchical multi-faceted processes in a coherent manner. They provide this opportunity through an intelligent integration of both quantitative and qualitative aspects of the problem to be addressed
A helicopter obstacle avoidance system incorporating non-linear neurofuzzy multi-sensor data fusion
No full textHazardous weather conditions significantly limit the operational capability of civil helicopters. This limitation arises from the crew's inability to determine the location of obstacles in the environment by sight. In order to assist the crew in these circumstances a range of equipment and sensors may be installed in the helicopter. However, with multiple sensors on board, the problem of efficiently assimilating the large amount of imagery and data available generates a significant workload. A reduction of the workload may be achieved by the automation of this assimilation (sensor fusion) and the design of a system to guide the pilot along obstacle free paths. In order to provide the guidance to avoid obstacles a system must have knowledge about the obstacles' possible positions and likely future positions relative the system's own aircraft. Since the information being provided by the sensors will not be perfect, (i.e. it will have some uncertainty associated with it), and since the process model, which must be used to predict any future positions, will also be uncertain, the required positions must be estimated. As the dynamics of moving obstacles will be a priori unknown, it will be necessary to learn process models for them. The dynamics of the obstacles cannot be guaranteed to be linear, therefore these process models must be capable of reflecting this non-linear behaviour. The uncertain information produced by the various sensors will be related to required knowledge about the obstacles by a sensor model, however this relationship need not be linear, and may even have to be learned. Currently used estimation techniques (e.g. the ordinary extended Kalman filter) are inadequate for estimating the uncertainty involved in the obstacles' positions for the highly non-linear processes under consideration. Neural network approaches to non-linear estimation have recently allowed process and sensor models to be learned (sometimes implicitly), however these approaches have been quite ad hoc in their implementation and have been even more negligent in the estimation of uncertainty. The main contributions of this research are the design of non-linear estimators which may use process and sensor models that result from learning processes, and the use of the output of these estimators to determine guidance for obstacle free paths through the environment in 3 dimensions
Multi-Sensor Data Fusion for Helicopter Guidance
No full textHelicopter crews are required to carry out a wide range of duties most of which involve operational safety. One particular flight safety problem that has relevance to a variety of different types of rotorcraft is assisting the pilot in obstacle avoidance. Obstacles, in this context, may typically include other aircraft, terrain features, buildings, bridges, overhead cables and poles. The problem is especially difficult in bad weather conditions, (such as fog, snow, and heavy rain), in the presence of heavy smoke, or dust, or at night. Also it must be noted that the dynamics of the moving obstacles (other aircraft etc) will not in general be linear or indeed even known a priori. In order to avoid obstacles effectively a system must have knowledge about the obstacles' positions and likely future positions relative the system's own aircraft. Since the information being provided by the sensors will be imperfect, (i.e. it will have some uncertainty associated with it), and since the process model, which must be used to predict any future positions, will also be uncertain, the required positions must be robustly estimated. Since the dynamics of moving obstacles will be a priori unknown, it will be necessary to learn process models for them. Since the dynamics of the obstacles may not be linear, the process models must be capable of reflecting non-linear behaviour. The uncertain information produced by the various sensors will be related to required knowledge about the obstacles by a sensor model, however this relationship need not be linear, and may even have to be learnt. The information sources of interest may be distributed over many platforms, therefore the architecture of the data fusion system must reflect the spatially distributed nature of the problem. In essence then, the main aim of this research is the design an estimator which is capable of dealing with non-linear process and sensor models, which may result from learning processes, and with distributed information sources
Multi-sensor data fusion for situational assessment: a critical element of systems integration, some theory and application to collision avoidance
No full text
Kalman Filter
No full textThe Kalman filter is the general solution to the recursive, minimised mean square estimation problem within the class of linear estimators. The Kalman filter gives a linear, unbiased, and minimum error variance recursive algorithm to estimate the unknown states of a dynamic process from noisy data taken at discrete real-time intervals. States, in this context, refer to any quantities of interest involved in the dynamic process, e.g. position velocity, chemical concentration, etc. For Gaussian random variables the Kalman filter is the optimal linear predictor-estimator and for variables of forms other than Gaussian the estimator is the best only within the class of linear estimators. The filter requires a knowledge of the second-order statistics of the noise of process being observed and of the measurement noise in order to provide the solution that minimises the mean square error between the true state and the estimate of state. Kalman filtering provides a convenient means of determining the weightings (denoted as gains) to be given to input measurement data. It also provides an estimate of the estimated state's error statistics through a covariance matrix. Hence the Kalman filter chooses the gain sequence and estimates the estimated state's accuracy in accordance with the variations (in terms of accuracy and update rate) of input data and modelled process dynamics. It should be noted that the quality of the estimation, as described through the error covariance matrix can in many cases be determined a priori, and would therefore be independent of the observations made. The Kalman filter has been used extensively for many diverse applications. For example, Kalman filtering has proved useful in navigational and guidance systems, radar tracking, sonar ranging, and satellite orbit determination. This chapter is mainly concerned with the derivation of the Kalman filter algorithm from the point of view of it being a linear observer, and with how the filter algorithm may be used in practice. As the Kalman filter is generally implemented on digital computers this chapter concerns itself with the discrete time form of the algorithm. A derivation of the Extended Kalman filter, a variation of the Kalman filter applicable to non-linear problems, is described. Two important variations of the Kalman filter are introduced to provide some indication of the its versatility. Finally three simple, but detailed examples of the calculations involved in Kalman filter cycles are presented
Multi-Sensor Data Fusion for Helicopter Guidance using Neuro-Fuzzy Estimation Algorithms
No full textThe main objective of this paper is to present some algorithms to fuse information about obstacles, whose dynamics are a-priori unknown, in a helicopter's environment, provided by multiple spatially separate sensors. The fused information can then be used to help helicopters locate obstacles in hazardous conditions so that it can avoid them. Obstacle track estimation has been commonly carried out using the Kalman Filter (KF), a linear estimator, or one of its variations. The Extended Kalman Filter, one such variation designed for use on non-linear problems, produces the best linear approximation to the object track. However certain assumptions made in the derivation of these algorithms render them sub-optimal for aerial obstacle track estimation. Work produced by University of Southampton has highlighted a link between fuzzy networks and associative memory neural networks. This link is important as it allows new learning rules to be developed for training fuzzy rules, and the conditions under which convergence can be proved to be derived. This paper will explore methods for the fusion of estimates using these neurofuzzy models, and also address some of the weaknesses of the Kalman filter approximation introduced by the assumptions made in its derivation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
- …
