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This paper presents an approach to predict the operating conditions of machine based on\ud classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS)\ud in association with direct prediction strategy for multi-step ahead prediction of time series\ud techniques. In this study, the number of available observations and the number of predicted\ud steps are initially determined by using false nearest neighbor method and auto mutual\ud information technique, respectively. These values are subsequently utilized as inputs for\ud prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

This paper proposes an integrated system for unconstrained face recognition in complex scenes. The scale and orientation tolerant system comprises a face detector followed by a recognizer. Given a color input image of a person, the face detector encloses the face from the complex scene within a circular boundary, and locates the position of the nose. A radial grid mapping centered on the nose is then performed to extract a feature vector within the boundary. The feature vector is input to a radial basis function neural network classifier for face identification. The proposed face detector achieved an average detection rate of 95.8% while the face recognizer achieved an average recognition rate of 97.5% on a database of 21 persons with variations in scale, orientation, natural illumination and background. The two modules were combined to form an automatic face recognition system that was evaluated in the context of a security system using a video database of 21 users and 10 intruders, acquired in an unconstrained environment. A recognition rate of 93.5% with 0% false acceptance rate was achieved.

The paper deals with the invariant recognition of patterns, and aims at developing (i) their pulse-coded representation; and (ii) an algorithm for their recognition. The proposed pattern encoder utilizes the properties of complex logarithmic mapping (CLM) (computed with reference to the center of gravity, CoG, of the shape), which maps rotation and scaling in its domain to shifts in its range. The encoder, then, invokes a pulse-encoding scheme similar to the one proposed by Dodwell [1] in order to handle these shifts, thereby generating pulse-codes invariant to scaling, rotation, and shift in the input shape. These pulses are then fed to a novel multi-layered neural recognizer which (i) invokes template matching with a distinctly implemented architecture; and (ii) achieves robustness (to noise and pattern deformation) by virtue of its overlapping strategy for code classification. The proposed encoder–recognizer (E–R), which is hardware implementable by a high-speed electronic switching circuit, can add new patterns on-line to the existing ones. The E–R is illustrated with experimental results. While human visual system has been the main motivation to the proposed model, no claim, however, has been made on its direct biological plausibility.