Unspecified distribution in single disorder problem

We register a stochastic sequence affected by one disorder. Monitoring of the sequence is made in the circumstances when not full information about distributions before and after the change is available. The initial problem of disorder detection is transformed to optimal stopping of observed sequence. Formula for optimal decision functions is derived.Comment: 23 page

Image-Driven Decision Making with Application to Control Gas Burners

Part 5: Industrial Management and Other ApplicationsInternational audienceOur aim is to propose a model-free approach to decision making that is based on the direct use of images. More, precisely, a content of each image is used – without further processing – in order to cluster them by the K-medoids method. Then, decisions are attached to each cluster by an expert. When a new image is acquired, it is firstly classified to one of the clusters and the corresponding decision is made. The approach is conceptually rather simple, but its success in on-line applications depends on the way of organizing learning and decision phases. We illustrate the approach by the example of a decision-making system for industrial gas burners

Nonparametric Estimation of Continuously Parametrized Families of Probability Density Functions—Computational Aspects

We consider a rather general problem of nonparametric estimation of an uncountable set of probability density functions (p.d.f.’s) of the form: f ( x ; r ) , where r is a non-random real variable and ranges from R 1 to R 2 . We put emphasis on the algorithmic aspects of this problem, since they are crucial for exploratory analysis of big data that are needed for the estimation. A specialized learning algorithm, based on the 2D FFT, is proposed and tested on observations that allow for estimate p.d.f.’s of a jet engine temperatures as a function of its rotation speed. We also derive theoretical results concerning the convergence of the estimation procedure that contains hints on selecting parameters of the estimation algorithm

A MODIFIED FILTER SQP METHOD AS A TOOL FOR OPTIMAL CONTROL OF NONLINEAR SYSTEMS WITH SPATIO–TEMPORAL DYNAMICS

Our aim is to adapt Fletcher’s filter approach to solve optimal control problems for systems described by nonlinear Partial Differential Equations (PDEs) with state constraints. To this end, we propose a number of modifications of the filter approach, which are well suited for our purposes. Then, we discuss possible ways of cooperation between the filter method and a PDE solver, and one of them is selected and tested

Local detection of defects from image sequences

Our aim is to discuss three approaches to the detection of defects in continuous production processes, which are based on local methods of processing image sequences. These approaches are motivated by and applicable to images of hot metals or other surfaces, which are uniform at a macroscopic level, when defects are not present. The first of them is based on the estimation of fractal dimensions of image cross-sections. The second and third approaches are compositions of known techniques, which are selected and tuned to our goal. We discuss their advantages and disadvantages, since they provide different information on defects. The results of their testing on 12 industrial images are also summarized

Simulations for Tuning a Laser Power Control System of the Cladding Process

Part 4: Optimization, TuningInternational audienceOur aim is present the methodology of simulations for repetitive processes and tuning control systems for them in the presence of noise. This methodology is applied for tuning a laser power control system of the cladding process. Even the simplest model of this process is nonlinear, making analytical tuning rather difficult. The proposed approach allows us to select quickly the structure of the control system and to optimize its parameters. Preliminary comparisons with experimental results on a robot-based laser cladding systems are also reported. These comparisons are based on the temperature measurements, observations by a camera and IR camera

One-Dimensional Kohonen's Lvq Nets for Multidimensional Patterns Recognition

A new neural network based pattern recognition algorithm is proposed. The method consists in preprocessing the multidimensional data, using a space-filling curve based transformation into the unit interval, and employing Kohonen's vector quantization algorithms (of SOM and LVQ types) in one dimension. The space-filling based transformation preserves the theoretical Bayes risk. Experiments show that such an approach can produce good or even better error rates than the classical LVQ performed in a multidimensional space