A pattern classification system for the identification of UV-visible synchronous fluorescence of petroleum oils is developed. The system is a composite of three phases, namely, feature extraction, feature selection and pattern classification. These phases are briefly described, focusing particularly on the classification method. A method called successive feature elimination process (SFEP) is used for feature selection and a proximity index classifier (PIC) is
developed for classification. The feature selection method extracts as many features from spectra as conveniently possible and then applies the SFEP process to remove the redundant features. From the remaining features a significantly smaller feature subset is selected that enhances the recognition performance of the PIC classifier. The SFEP and PIC methods are formally described. These methods are successfully applied to the classification of UV-visible synchronous fluorescence spectra. The features selected by the algorithm are used to classify twenty different sets of petroleum oils. The system was trained on the design set on which the recognition performance was 100%. The performance on the testing set was over 93% by successfully identifying 28 out of 30 samples in six classes. This performance is very encouraging. In addition, the method is computationally inexpensive and is equally useful for large data set problems as it always partitions the problem into a set of two class problems.
Spectral pattern recognition (SPR) methods are among the most powerful tools currently available for noriinvasively examhiin the spectroscopic and other chemical data for environmental analysis and monitoring. Using spectral data, these systems have found a variety of applications in chemometric systems such as gas chromatography, fluorescence spectroscopy, etc. An advantage of SPR approaches is that they make no a priori assumption regarding the structure of spectra. However, a majority o these systems rely on humanjudgment for parameter selection and classification. We considered a SPR problem as a composite of five subproblems: pattern acquisition, feature extraction, feature selection, knowledge organization, and pattern classification. One ofthe basic issues in SPR approaches is to determine and measure the features useful for successful classification. Selection of features that contain the most discriminatory information is important because the cost of pattern classification is directly related to the number offeatures used for classification. Various features present in a pattern and a large variety of classification algorithms could be used. A spectral pattern classification system combining the above components and multivariate decisiontheoretic approaches for classification is developed. It is shown how such a system can be used for large data analysis, warehousing, and interpretation. In a preliminary test, the system was used to classif' synchronous UV-vis fluorescence spectra ofrelatively similar petroleum oils with reasonable success.
The broader definition of chemometrics includes methods such as pattern recognition (PR) and signal/image processing for noninvasive analysis and interpretation of data. These methods are among the most powerful tools currently available for noninvasively examining spectroscopic and other chemical data. Using spectral data, these systems have found a variety of applications employing analytical techniques for gas chromatography, fluorescence IR or NMR spectroscopy, etc. An advantage of PR approaches is that they make no a priori assuniption regarding the stmcture of the spectra. However, a majority of these systems rely on hunianjudgment for parameter selection and classification of spectra. Generally a spectral pattern recognition (SPR) problem is considered as a group of several subproblems. We considered a SPR problem as a group of five subproblems: spectra acquisition, feature extraction, feature selection, spectra organization, and spectra classification. One of the basic issues in PR approaches is to determine and measure the discriminatory features useful for successful classification. A spectral pattern classification system, combining spectral feature extraction and selection, and decision-theoretic approaches, is developed. It is shown how such a system can be used for analysis of large data analysis, warehousing, and interpretation.
A significant phase in the development of an intelligent agent is the construction of its Knowledge Base (KB) on the basis of which it has to take the appropriate actions. The validation and verification (V&V) of KBs is an important part of any KB system development, ignoring it can result anomalies during run-time. The paper discusses the implementation of a utility for validation and verification of KBs'. The methodology transforms the rules in a KB to an equivalent Petri net representation and then applies the analytical tools ofthe Petri net theory for the detection of errors.
Various aspects of controlling the environment can be structured in a form of multiple-criteria decision making. The combination ofniathematical modeling, decisioninaking, and the use ofpattern recognition in the environmental modeling and management of complex systems is discussed from a historical perspective. Of particular interest is the use of objective space technology in modeling multiple criteria decisionmaking. This paper presents a model and solution algorithm for such type ofdecision problem using a multiple objective linear program. Several applications for environmental control can be modeled using this methodology.
Pattern recognition (PR) and signal/image processing methods are among the most powerful tools currently available for noninvasively examining spectroscopic and other chemical data for environmental monitoring. Using spectral data, these systems have found a variety of applications employing analytical techniques for chemometrics such as gas chromatography, fluorescence spectroscopy, etc. An advantage of PR approaches is that they make no a prior assumption regarding the structure of the patterns. However, a majority of these systems rely on human judgment for parameter selection and classification. A PR problem is considered as a composite of four subproblems: pattern acquisition, feature extraction, feature selection, and pattern classification. One of the basic issues in PR approaches is to determine and measure the features useful for successful classification. Selection of features that contain the most discriminatory information is important because the cost of pattern classification is directly related to the number of features used in the decision rules. The state of the spectral techniques as applied to environmental monitoring is reviewed. A spectral pattern classification system combining the above components and automatic decision-theoretic approaches for classification is developed. It is shown how such a system can be used for analysis of large data sets, warehousing, and interpretation. In a preliminary test, the classifier was used to classify synchronous UV-vis fluorescence spectra of relatively similar petroleum oils with reasonable success.
Spectral pattern recognition (SPR) methods are among the most powerful tools currently available for noninvasively examining the spectroscopic and other chemical data for environmental monitoring. Using spectral data, these systems have found a variety of applications in chemometric systems such as gas chromatography, fluorescence spectroscopy, etc. An advantage of SPR approaches is that they made no a priori assumption regarding the structure of the spectra. However, a majority of these systems rely on human judgement for parameter selection and classification.
This paper develops a distributed knowledge-based spectral processing and classification system which functions in one of two modes, executive and assistant. In the executive mode the system functions as a stand-alone system, automatically performing all the tasks from spectral enhancement, feature extraction and selection, to spectral classification and interpretation using the optimally feasible algorithms. In the assistant mode the system leads the user through the entire spectral processing and classification process, allowing a user to select appropriate parameters, their weights, knowledge organization method and a classification algorithm. Thus, the latter mode can also be used for teaching and instruction. It is shown how novice users can select a set of parameters, adjust their weights, and examine the classification process. Since different classifiers have various underlying assumptions, provisions have been made to control these assumptions, allowing users to select the parameters individually and combined, and providing facilities to visualize the interrelationships among the parameters.
Frequently, real world data is degraded by under sampling of intrinsic periodicities, or by sampling with unevenly spaced intervals. This results in dropout or missing data, and such data sets are particularly difficult to process using conventional image processing methods. In many cases, one must still extract as much information as possible from a given data set, although available data may be sparse or noisy. In such cases, we suggest algorithms based on wavelet transform will offer a viable alternative as some early work in the area has indicated. An architecture of an image classification software system is suggested to implement an improved scheme for the analysis, representation, processing and classification of images. The scheme is based on considering the segments of images as wavelets so that small details in the images can be exploited. The objective is to implement this scheme automatically and rapidly decompose a 2D image into a combination of elemental images so that an array of processing methods can be applied. Thus, the scheme offers potential utility for analysis of images and compression of image data. Moreover, the elemental images may be considered patterns that the system is required to recognize, so that the scheme offers potential utility for industrial and military applications involving robot vision and/or automatic recognition of targets.
Modern image and signal processing methods strive to maximize signal to nose ratios, even in the presence of severe noise. Frequently, real world data is degraded by under sampling of intrinsic periodicities, or by sampling with unevenly spaced intervals. This results in dropout or missing data, and such data sets are particularly difficult to process using conventional image processing methods. In many cases, one must still extract as much information as possible from a given data set, although available data may be sparse or noisy. In such cases, we suggest algorithms based on wavelet transform and fractal theory will offer a viable alternative as some early work in the area has indicated. An architecture of a software system is suggested to implement an improved scheme for the analysis, representation, and processing of images. The scheme is based on considering the segments of images as wavelets and fractals so that small details in the images can be exploited and the data can be compressed. The objective is to improve this scheme automatically and rapidly decompose a 2D image into a combination of elemental images so that an array of processing methods can be applied. Thus, the scheme offers potential utility for analysis of image could be the patterns that the system is required to recognize, so that the scheme offers potential utility for industrial and military applications involving robot vision and/or automatic recognition of targets.
Electroencephalogram (EEG) pattern recognition problem is considered as a composite of three subproblems: feature extraction, feature selection, and pattern classification. Focusing particularly on the feature selection issue, each subproblem is reviewed briefly and a new method for feature selection is proposed. The method suggests that first one shall extract as much information (features) as conveniently possible in several pattern information domains and then apply the proposed unbiased successive feature elimination process to remove redundant and poor features. From this set select a significantly smaller, yet useful, feature subset that enhances the performance of the classifier. The successive feature elimination process is formally described. The method is successfully applied to an EEG signal classification problem. The features selected by the algorithm are used to classify three signal classes. The classes identified were eye artifacts, muscle artifacts, and clean (subject in stationary state). Two hundred samples for each of the three classes were selected and the data set was arbitrarily divided into two subsets: design subset, and testing subset. A proximity index classifier using Mahalanobis distance as the proximity criterion was developed using the smaller feature subset. The system was trained on the design set. The recognition performance on the design set was 92.33%. The recognition performance on the testing set was 88.67% by successfully identifying the samples in eye-blinks, muscle response, and clean classes, respectively, with 80%, 97%, and 89%. This performance is very encouraging. In addition, the method is computationally inexpensive and particularly useful for large data set problems. The method further reduces the need for a careful feature determination problem that a system designer usually encounters during the initial design phase of a pattern classifier.
Pattern classification of UV-visible synchronous fluorescence of petroleum oils is performed using a composite system developed by the authors. The system consists of three phases, namely, feature extraction, feature selection and pattern classification. Each of these phases are briefly reviewed, focusing particularly on the feature selection method. Without assuming any particular classification algorithm the method extracts as much information (features) from spectra as conveniently possible and then applies the proposed successive feature elimination process to remove the redundant features. From the remaining features a significantly smaller, yet optimal, feature subset is selected that enhances the recognition performance of the classifier. The successive feature elimination process and optimal feature selection method are formally described. These methods are successfully applied for the classification of UV-visible synchronous fluorescence spectra. The features selected by the algorithm are used to classify twenty different sets of petroleum oils (the design set). A proximity index classifier using the Mahalanobis distance as the proximity criterion is developed using the smaller feature subset. The system was trained on the design set. The recognition performance on the design set was 100%. The recognition performance on the testing set was over 93% by successfully identifying 28 out of 30 samples in six classes. This performance is very encouraging. In addition, the method is computationally inexpensive and is equally useful for large data set problems as it always partitions the problem into a set of two class problems. The method further reduces the need for a careful feature determination problem which a system designer usually encounters during the initial design phase of a pattern classifier.
Although physician observation is usually the most sensitive method for diagnosing and monitoring a patient''s medical condition human observation cannot be conducted continuously and consistently. It can be helpful therefore to employ specialized automated techniques for the continuous reliable and noninvasive monitoring of those parameters useful for the enhancement of physicians'' diagnostic capabilities. Signal processing systems are among the most powerful of those techniques currently available for noninvasively examining the internal structure of living biological systems. Nonetheless the capability of these systems can be substantially enhanced if supplemented with automated classification and interpretation precedures. An intelligent EEG signal sensing and interpretation system using typical signal processing techniques supplemented with heuristics and identification techniques has been designed. The system is comprised of five major components namely: the fact gathering system the knowledge/rule base the knowledge organization/learning phase the inference engine and the expert/user interface. The fact gathering system collects raw waveforms preprocesses these for noise elimination and extracts the pertinent information from the waveforms. The knowledge/rule base is an information and knowledge bank wherein the appropriate knowledge parameters useful for the decision making process are stored. The knowledge organization/learning phase structures the knowledge In the order determined by the association among pattern classes and trains the Inference engine. The structure of the inference engine is based on a hierarchical pattern classifier which categorizes the unknown signals using a layered decision making strategy
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