Algorithmic Scheme for Concurrent Detection and Classification of Printed Circuit Board Defects

An ideal printed circuit board (PCB) defect inspection system can detect defects and classify PCB defect types. Existing defect inspection technologies can identify defects but fail to classify all PCB defect types. This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types. In the proposed algorithmic scheme, fuzzy c-means clustering is used for image segmentation via image subtraction prior to defect detection. Arithmetic and logic operations, the circle hough transform (CHT), morphological reconstruction (MR), and connected component labeling (CCL) are used in defect classification. The algorithmic scheme achieves 100% defect detection and 99.05% defect classification accuracies. The novelty of this research lies in the concurrent use of CHT, MR, and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location, area, and nature of defects. This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process. Moreover, the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process, improve the PCB quality, and lower the production cost.     

Boosting-based ensemble learning with penalty profiles for automatic Thai unknown word recognition

A boosting-based ensemble learning can be used to improve classification accuracy by using multiple classification models constructed to cope with errors obtained from their preceding steps. This paper proposes a method to improve boosting-based ensemble learning with penalty profiles via an application of automatic unknown word recognition in Thai language. Treating a sequential problem as a non-sequential problem, the unknown word recognition is required to include a process to rank a set of generated candidates for a potential unknown word position. To strengthen the recognition process with ensemble classification, the penalty profiles are defined to make it more efficient to construct a succeeding classification model which tends to re-rank a set of ranked candidates into a suitable order. As an evaluation, a number of alternative penalty profiles are introduced and their performances are compared for the task of extracting unknown words from a large Thai medical text. Using the Naïve Bayes as the base classifier for ensemble learning, the proposed method with the best setting achieves an accuracy of 90.19%, which is an accuracy gap of 12.88, 10.59, and 6.05 over conventional Naïve Bayes, non-ensemble version, and the flat-penalty profile.