Joseph Aguilar-Martin

Process situation assessment: From a fuzzy partition to a finite state machine

Abstract Process situation assessment plays a major role in supervision of complex systems. The knowledge of the system behavior is relevant to support operators in their decision tasks. For complex industrial processes such as chemical or petrochemical ones, most of supervision approaches are based on data acquisition techniques and specifically on clustering methods to cope with the difficulty of modeling the process. Consequently, the system behavior can be characterized by a state space partition. This way, situation assessment is performed online through the tracking of the system evolution from one class to another. Furthermore, a finite state machine that is a support tool for process operators is elaborated to model the system behavior. This article presents theoretical aspects according to which the intuition that the trajectory observation of a dynamical system by a sequence of classes, to which the actual state belongs, gives valuable information about the real behavior of the system is substantiated. Thus, practical aspects are developed on the state machine construction and illustrated by two simple applications in the domain of chemical processes.

Similarity-margin based feature selection for symbolic interval data

In this paper we propose a feature selection method for symbolic interval data based on similarity margin. In this method, classes are parameterized by an interval prototype based on an appropriate learning process. A similarity measure is defined in order to estimate the similarity between the interval feature value and each class prototype. Then, a similarity margin concept has been introduced. The heuristic search is avoided by optimizing an objective function to evaluate the importance (weight) of each interval feature in a similarity margin framework. The experimental results show that the proposed method selects meaningful features for interval data. In particular, the method we propose yields a significant improvement on classification task of three real-world datasets.

Variation points in pattern recognition

Abstract The present work completes a Pattern Recognition Algorithm in which the marginal knowledge aggregation operators are linearly compensated mixed connectives (m.c.). As m.c. are dependent on a parameter α, many partitions of the same data base can be obtained. This parameter is a free choice by the user and two questions arise: how can we choose a finite set of values of α such that the partitions obtained be different and such that all partitions be obtained. These two problems are solved by the introduction of the concept of variation points.

Automaton based on fuzzy clustering methods for monitoring industrial processes

Fuzzy clustering allows finding classes through the historical data in order to associate them with functional states useful to represent the complex industrial processes behavior. By means of classes, an automaton can be established that determines the current and the next connections of functional states of a process. When fuzzy clustering is used, the connections in the historical data are considered but it does not find other important connections. To solve this limitation, a new method to seek the most important connections among functional states is proposed. Initially, the approach defines an initial transition degrees matrix, where all connections are taken into account. Through a proposed update step, the most important connections are obtained, which they describe the real behavior of a process. In addition, a new distance criterion is defined to improve the update step. The final transition degrees matrix is used to construct a fuzzy automaton that it is validated by human operators experience. The approach was tested in a steam generator process. Applying three fuzzy clustering algorithms in case of study, the proposed method finds the same transition matrix. The new connections were validated by the human operator.

Image color segmentation using the fuzzy tree algorithm T-LAMDA

The image segmentation is very sensitive to the features used in the similarity measure and the objects type. In this paper we introduce a new segmentation algorithm based on fuzzy clustering. This method allows to incorporate spatial information which yield the result more accurate and more robust to noise. It is completely automatized with respect to the number of clusters and the setting up of membership functions. The data structure based on a Fuzzy Tree Algorithm allows to reduce the CPU time.

Membership-margin based feature selection for mixed type and high-dimensional data

Definition of membership-margin based objective function.Weighting the antecedents of fuzzy if-then rules to improve classification performance.Evaluate the feature importance within membership-margin framework.Feature selection algorithm for mixed-type and high dimensional data.The effectiveness of the approach is proved through an extensive experimental study. The present paper describes a new feature weighting method based on a membership margin. Distinctive properties of the proposed method include its capability to process problems characterized by mixed-type data (quantitative, qualitative and interval) as well as a huge number of features. The key idea is to map simultaneously all the features of different types into a common space; the membership space. Once all features are represented in a homogeneous space, a feature weighting task can be performed in unified way. This weighting approach is integrated here within a fuzzy classifier through a fuzzy rule weighted concept in order to improve its performance. Each antecedent fuzzy set in the fuzzy if-then rule is weighted to characterize the importance of each proposition and therefore its corresponding feature. Weight estimation process is based on membership margin maximization to estimate a fuzzy weight of each feature in the membership space. Experiments on low and high dimensional real-world datasets demonstrate that the proposed approach can improve significantly the performance of the fuzzy rule-based as well as other state of the art classifiers and can even outperform classical feature weighting approaches. In particular, we show that this approach can yield meaningful results on two real-world applications for cancer prognosis and industrial process diagnosis.

TOWARDS A UNIFIED PRINCIPLE FOR REASONING ABOUT HETEROGENEOUS DATA: A FUZZY LOGIC FRAMEWORK

Human knowledge about monitoring process variables is usually incomplete. To deal with this partial knowledge many types of representation other than the quantitative one are used to describe process variables (qualitative, symbolic interval). Thus, the development of automatic reasoning mechanisms about the process is faced with this problem of multiple data representations. In this paper, a unified principle for reasoning about heterogeneous data is introduced. This principle is based on a simultaneous mapping of data from initially heterogeneous spaces into only one homogeneous space based on a relative measure using appropriate characteristic functions. Once the heterogeneous data are represented in a unified space, a single processing for various analysis purposes can be performed using simple reasoning mechanisms. An application of this principle within a fuzzy logic framework is performed here to demonstrate its effectiveness. We show that simple fuzzy reasoning mechanisms can be used to reason in a unified way about heterogeneous data in three well known machine learning problems.

Improving fuzzy systems identification with data transformations

Abstract A practical problem in the identification of fuzzy systems from data, is the design and the tuning of the membership functions. We demonstrate that if the data is properly transformed before the identification process, the resulting fuzzy model can be improved to the point it may not need a further tuning. The significance of the data transform can be validated using statistical methods. The method is demonstrated on a time series prediction problem, using the Box–Cox transform.

From Chemical Process Diagnosis to Cancer Prognosis: An Integrated Approach for Diagnosis and Sensor/Marker Selection

Abstract Classification techniques have shown recently their usefulness for complex process diagnosis. Besides the fact that no physical model for the process is required, they enable to study the problem of sensor location. Preliminary studies made previously in the domain of chemical process diagnosis have been the initial key point to extend its application to the medical diagnosis framework. Despite the behavioral difference, both domains exhibit many common practices. However, medical diagnosis recently has brought serious challenges such as high dimensionality (gene expression profiling) and heterogeneity of data (symbolic histo-pathological factors). We show here that both challenges can be overcome and used in return to improve complex process diagnosis.

Qualitative Event-Based Expert Supervision for Transient ConditionMonitoring

This paper addresses the following supervisory problem: a continuous plant (P) is to be supervised via symbolic (or quantised) actions. These symbolic actions suggest the set points for the lower level control loops. The system dynamic is analysed on the supervisory level (K) by a qualitative approach. The relationships between variables and the steady-state references are known. These problems are especially common in chemical process control. The supervisor handles start-up and shut-down procedures and takes appropriate action to solve the sequential or parallel tasks of a basic procedure. The object of this paper is to introduce an approach to solving the problem of how to derive a set of rules from a physical process.The solutions for supervising start-up and shut-down operations in close loop are suitable for large industrial systems, as are as the batch and semi-continuous processes used in order to maintain operations in a dynamic mode. This paper considers the qualitative event-based expert supervision approach to distillation column problems. The development of a general supervision in this work is based on an events generator and a corrective actions generator. The qualitative symbols are based on fuzzy sets. In particular, there are mechanisms for processing the changes in the system variables from qualitative symbols.