Abstract-In order to improve the performance of fall detection system for the elderly based on triaxial acceleration sensor, and accurately to judge the fall direction of human body, a method was put forward based on self-organizing map neural network (SOM) and the information of triaxial acceleration sensor to cluster and analyze the human motion. To verify the recognition results of the SOM method, 130 samples of 13 common action including fall were participated in the SOM network testing. The results show that the sensitivity, specificity and accuracy of the new system were 90%, 96.7%, 94.6%, respectively. These results were better than those of the method of threshold value.
The automatic radioscopic inspection of industrial parts usually uses reference based methods. These methods select, as benchmark for comparison, image data from good parts to detect the anomalies of parts under inspection. However, parts can vary within the specification during the production process, which makes comparison of older reference image sets with current images of parts difficult and increases the probability of false rejections. To counter this variability, the reference image sets have to be updated. This paper proposes an adaptive reference image set selection procedure to be used in the assisted defect recognition (ADR) system in turbine blade inspection. The procedure first selects an initial reference image set using an approach called ADR Model Optimizer and then uses positive rate in a sliding-time window to determine the need to update the reference image set. Whenever there is a need, the ADR Model Optimizer is retrained with new data consisting of the old reference image sets augmented with false rejected images to generate a new reference image set. The experimental result demonstrates that the proposed procedure can adaptively select a reference image set, leading to an inspection process with a high true positive rate and a low false positive rate.
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