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Edible bird nest (EBN) production is a major industry in South East Asia, motivated by the perception that consumption of this delicacy can help improve the human immune system while slowing down the aging process. However, processing the raw EBN requires a tremendous amount of human effort, making it a very labour-intensive process. The most critical part of this process is the cleaning of the EBN and this is also the most labour intensive and time consuming. Impurities in each sample have to be painstakingly identified and then removed from the rest of the nest. This paper proposed a method to detect the impurities in edible bird nest using K-Means segmentation to help improve the effectiveness of the conventional cleaning process by accurately identifying the impurities of various shapes and sizes. The accuracy of the proposed method has been compared with Fuzzy C-Means.

Partial discharge (PD) pattern recognition is useful to diagnose insulation condition. PD measurement data is commonly represented in phase-resolved partial discharge (PRPD) format. PRPD is useful as it provides a visible pattern for different PD source and various features can be extracted for PD pattern recognition. Shorter PRPD duration will enable more training data but the information in each data is less and vice versa. This works aims to investigate the effects of using very short duration PRPD data on the accuracy of PD pattern recognition. The results conclude that machine learning models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are robust enough such that reduction of PRPD duration from 15-seconds to 1-second causes less than 5 % drop in the classification accuracy. However, this is only true for noise free condition. When the same PD data is overlapped with random noise, the classification accuracy suffers a significant reduction up to 19%. Therefore, longer PRPD duration is recommended to withstand the effects of noise contamination.

In an attempt to solve the lengthy training times of neural networks, we proposed Parallel Circuits (PCs), a biologically inspired architecture. Previous work has shown that this approach fails to maintain generalization performance in spite of achieving sharp speed gains. To address this issue, and motivated by the way Dropout prevents node co-adaption, in this paper, we suggest an improvement by extending Dropout to the PC architecture. The paper provides multiple insights into this combination, including a variety of fusion approaches. Experiments show promising results in which improved error rates are achieved in most cases, whilst maintaining the speed advantage of the PC approach.