Thesis (Ph. D.)--Ohio State University, 1986. / Includes bibliographical references (leaves 166-170). Available online via OhioLINK's ETD Center

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Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 84-92).

Hand-printed Chinese character recognition presents an interesting problem for Artificial Intelligence research. Input data in the form of arrays of pixel values cannot be directly mapped to unique character identifications because of the complexity of the characters. Thus, intermediate data structures are necessary, which in turn lead to a need to represent knowledge of the characters' composition. Building the intermediate constructs for these hand-printed characters necessarily involves choices among ambiguities, the set of which is so large that an efficient search algorithm becomes central to the recognition process. Schema labelling is a theory of how knowledge should be organized for recognition tasks in which composition structure is inherent in the domain, the composition entails ambiguity, and the ambiguity generates large search spaces. This thesis describes an implementation of an enhanced version of schema labelling for Chinese characters. The specific problems addressed by the enhancements, with some success, are (i) the segmentation of real images into objects usable by the schema system, (ii) the definition of schemas which adequately describe the generic composition of hand-printed Chinese characters, as well as common variations or vagaries, and (iii) the inclusion of sufficient "control knowledge" to prevent combinatorial explosion of the backtracking recognition process. Test characters for recognition systems can be classified along several dimensions. On the spectrum from type-set, through hand-printed, to hand-written forms, our system was tested on restricted hand-print, at a level somewhat more difficult than is normally attempted. On the spectrum of input types, from grey-scale pixel input through on-line stroke representations, our system was fully tested only at the high end, with complete synthetic strokes. We obtained a success rate of 57%, 12 out of the 21 characters tested. The principal success of the work is that characters of the complexity tested could be recognized at all, and in the impact schema labelling techniques had on that recognition. / Science, Faculty of / Computer Science, Department of / Graduate

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Doctor of Philosophy / Department of Civil Engineering / Asadollah Esmaeily / Civil structures are susceptible to damages over their service lives due to aging, environmental loading, fatigue and excessive response. Such deterioration significantly affects the performance and safety of structure. Therefore, it is necessary to monitor the structural performance, detect and assess damages at the earliest possible stage in order to reduce the life-cycle cost of structure and improve its reliability. Over the last two decades, extensive research has been conducted on structural health monitoring and damage detection. In this study, a signal-based pattern-recognition method was applied to detect structural damages with a single or limited number of input/output signals. This method is based on the extraction of sensitive features of the structural response under a known excitation that present a unique pattern for any particular damage scenario. Frequency-based features and time-frequency-based features of the acceleration response were extracted from the measured vibration signals by Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) to form one-dimensional or two-dimensional patterns, respectively. Three pattern recognition algorithms were investigated when performing pattern-matching: (1) correlation, (2) least square distance, and (3) Cosh spectral distance. To demonstrate the validity and accuracy of the method, numerical and experimental studies were conducted on a simple small-scale three-story steel building. In addition, the efficiency of the features extracted by Wavelet Packet Transform (WPT) was examined in the experimental study. The results show that the features of the signal for different damage scenarios can be uniquely identified by these transformations. Suitable correlation algorithm can then be used to identify the most probable damage scenario. The proposed method is suitable for structural health monitoring, especially for the online monitoring applications. Meanwhile, the choice of wavelet function affects the resolution of the detection process and is discussed in the “experimental study part” of this report.