Differential linear repetitive processes are a class of linear systems which can be used, for example, to model industrial processes such as long-wall coal cutting operations. The key feature of interest in this paper...
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Differential linear repetitive processes are a class of linear systems which can be used, for example, to model industrial processes such as long-wall coal cutting operations. The key feature of interest in this paper is the fact that information propagation in one of the two separate directions in such processes evolves continuously over a finite fixed duration and in the other it is, in effect, discrete. This paper develops discrete approximations for the dynamics of these processes and examines the effects of the approximation techniques used on two key control related properties, i.e. stability and the 2D linear systems structure of the resulting discrete stale space models.
We developed an alternate method for density-based load estimation and applied it to estimate hip joint load distributions for two femora. Two-dimensional finite element models were constructed from single energy quan...
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Repetitive, or multipass, processes are a class of 2D systems of both practical and algorithmic/theoretical interest whose dynamics cannot be analysed or controlled using standard (1D) systems theory. Recently it has ...
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Repetitive, or multipass, processes are a class of 2D systems of both practical and algorithmic/theoretical interest whose dynamics cannot be analysed or controlled using standard (1D) systems theory. Recently it has been shown that the modelling of the boundary conditions, also known as the process initial conditions, is a crucial feature in the analysis and control of these processes. This paper presents some further results on the effects of so-called 'dynamic' process initial conditions on the controllability and stability properties of discrete linear repetitive processes. Previous work has shown that these dynamic process initial conditions alone can destroy the stability properties of these processes. Hence their effects must be 'adequately' accounted for the process modelling stage in order to ensure that subsequent analysis does not lead to incorrect results/conclusions. The main results developed in this paper can be summarised as follows. (i) Computationally efficient stability tests which can, in effect, be applied using standard, or 1D, linear systems tests. (ii) Characterisation of so-called pass controllability in the form of matrix rank based conditions. (iii) Conditions under which the dynamic process initial conditions can be selected to ensure stability and pass controllability.
This paper investigates the implementation of a hybrid methodology, which combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for the modeling of the supervisor of Large Scale Systems. The description...
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This paper investigates the implementation of a hybrid methodology, which combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for the modeling of the supervisor of Large Scale Systems. The description and the construction of Fuzzy Cognitive Map will be extensively examined and it will be proposed a model for the supervisor. There is an oncoming need for more autonomous and intelligent systems, especially in Large Scale Systems and the application of Fuzzy Cognitive Map for the modeling of the Supervisor may contribute in the development of more autonomous systems.
The basic unique control problem for repetitive processes arises from the explicit interaction between successive pass profiles. In particular, the output sequence of pass profiles can contain oscillations that increa...
The basic unique control problem for repetitive processes arises from the explicit interaction between successive pass profiles. In particular, the output sequence of pass profiles can contain oscillations that increase in amplitude in the pass to pass direction. Such behaviour is easily generated in simulation studies and in experiments on scaled models of industrial examples such as long-wall coal cutting. In long-wall coal cutting this problem appears as severe undulations in the newly cut floor profile which means that cutting operations (i.e. productive work) must be suspended to enable their manual removal. This problem is one of the key factors behind the stop/start cutting pattern of a typical working cycle in a coal mine. In general, this problem cannot be removed by standard, i.e. 1D, control action. The basic reason for this is that such an approach essentially ignores their inherent 2D systems structure. Motivated by this key fact, Rogers and Owens (1992) have developed a stability theory for repetitive processes with linear dynamics and a constant pass length. This theory is based on an abstract model in a Banach space setting which includes all such processes as special cases. The results of applying this theory to a range of special cases are also known including discrete linear repetitive processes which are the subject of this paper.
The sound (engine, noise, etc.) of a working vehicle provides an important clue, e.g., for surveillance mission robots, to recognize the vehicle type. In this paper, we introduce the "eigenfaces method", ori...
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The sound (engine, noise, etc.) of a working vehicle provides an important clue, e.g., for surveillance mission robots, to recognize the vehicle type. In this paper, we introduce the "eigenfaces method", originally used in human face recognition, to model the sound frequency distribution features. We show that it can be a simple and reliable acoustic identification method if the training samples can be properly chosen and classified. We treat the frequency spectra of about 200 ms of sound (a "frame") as a vector in a high-dimensional frequency feature space. In this space, we study the vector distribution for each kind of vehicle sound produced under similar working conditions. A collection of typical sound samples is used as the training data set. The mean frequency vector of the training set is first calculated, and subtracted from each vector in the set. To capture the frequency vectors' variation within the training set, we then calculate the eigenvectors of the covariance matrix of the zero-mean-adjusted sample data set. These eigenvectors represent the principal components of the vector distribution: for each such eigenvector, its corresponding eigenvalue indicates its importance in capturing the variation distribution, with the largest eigenvalues accounting for the most variance within this data set. Thus for each set of training data, its mean vector and its moat important eigenvectors together characterize its sound signature. When a new frame (not in the training set) is tested, its spectrum vector is compared against the mean vector; the difference vector is then projected into the principal component directions, and the residual is found. The coefficients of the unknown vector, in the training set eigenvector basis subspace, identify the unknown vehicle noise in terms of the classes represented in the training set. The magnitude of the residual vector measures the extent to which the unknown vehicle sound cannot be well characterized by the vehicle soun
We present the results of an empirical study in which we evaluated cost-sensitive learning algorithms on a rooftop detection task, which is one level of processing in a building detection system. Specifically, we inve...
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We present the results of an empirical study in which we evaluated cost-sensitive learning algorithms on a rooftop detection task, which is one level of processing in a building detection system. Specifically, we investigated how well machine learning methods generalized to unseen images that differed in location and in aspect. For the purpose of comparison, we included in our evaluation a handcrafted linear classifier, which is the selection heuristic currently used in the building detection system. ROC analysis showed that, when generalizing to unseen images that differed in location and aspect, a naive Bayesian classifier outperformed nearest neighbor and the handcrafted solution.
作者：
Wu, BCYoung, GSSchmidt, WChoppella, KDr. Bi-Chu Wu:received a PhD in Mechanical Engineering from the University of Maryland
College Park in 1991. She has worked on projects involving naval architecture design optimization solid mechanics and database development. Presently a senwr engineer with Angle Incorporated Dr Wu's research interests are in design optimization and fuzzy logic applications. Dr. Gin-Shu Young:
a senior engineer with Angle Incorporated holds a PhD in Mechanical Engineering from the University of Maryland College Park. As a guest researcher with National Institute of Standards and Technologies from 1990 to 1993 he worked on vision-based navigation for autonomous vehicles. His experience also includes applications of optimization fuzzy logic neural network and genetic algorithm methods to engineering system design Mr. William Schmidt:co-founded Angle Incorporated in 1990 and has served as Vice PresidentlChiefScientist during this tame. He holds a B.Sc. in Applied Science from the Naval Acadt?my and an M.Sc. in Physics from the Naval Post Graduate School. He has cner 20 years experience in technical leadership
material and personnel management. He has led the application of computer aided design (CAD) and Product Model Information Exchange to the shipbuilding industry. His experience also includes leading the amlication of model based operational analysis to support the Live Fire Test Program for DDG 51 Class Destroyers. Mr. Krishna M. Choppella:is a Sofware Engineer at Eidea Laboratories
Incotporated where he works on componentbased distributed enterpvise frameworks. He has been involved in creating data analysis tools for the US Nay by integrating CAD modeis databases and graphical front ends. His work in the Masters degree program in Mechanical Engineering at the University of Texas at Austin was in di0ddase.r spectroscopy of combustion products in porous-matri burners. He received his Bachelors degree in Electrical Engineering in India. He was a Research Associate at the Centre for Laser Technology and Project Engi
Ship design is often multidisciplinary involving several design elements with various types of objectives and constraints (O/C) some easily described as mathematical formulas, others better modeled as descriptive asse...
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Ship design is often multidisciplinary involving several design elements with various types of objectives and constraints (O/C) some easily described as mathematical formulas, others better modeled as descriptive assertions. This paper describes a method based on fuzzy functions and an integrated performance index to model O/C using descriptive assertions to be used with mathematical formulas in optimization. Another issue addressed in this paper concerns the coordination of design elements when sequentially coupled, that is, when one leads the other and the performance of the follower depends greatly on the design of the leader. Based on neuro-fuzzy techniques, the method described here coordinates and optimizes sequentially coupled elements. The two methods are applied to machinery arrangement (MA) and pipe routing (PR). Preliminary models for optimization of MA and PR are described considering convenience, producibility: engine room size, interference and location as factors in the O/C set. Some test results from MA/PR applications are presented and discussed. The methods are generic and can be extended to other elements in ship design. They are mutually independent and may be used separately Two advantages of their use are an improvement in overall performance and a reduction in the need for redesign of elements.
A hierarchical intelligent control system paradigm for a vision-based autonomous driving scheme is presented for a leader-following HMMWV. This article shows how fuzzy logic is employed to represent knowledge at the o...
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