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In this paper, we propose a novel and robust vision-and system-on-chip (SoC)-based system as a sensor to effectively detect falls of the elderly. The proposed method consists of five steps: initial light stability confirmation, gradient-difference-based foreground detection, dilationand multiframe-based foreground construction, false fall detection problem solving, and fall detection determination with a general-purpose input/output-based fall warning transmission. Real test videos have shown that our comprehensive experiments justify the low power, low hardware cost, and high detection accuracy merits of the proposed method when compared with related fall detection methods.

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Abstract:In this paper, we present Taguchi's and rolling modeling methods of artificial neural network (ANN) for very-short-term electric demand forecasting (VSTEDF) from the consumers' viewpoint. The rolling model is a metabolism technique that guarantees input data are always the most recent values. In ANN prediction, several factors that may influence the model should be well examined. Taguchi's method was employed to optimize the parameter settings for the ANN-based electric demand-value forecaster. Our experimental result shows that the optimal settings of ANN prediction model are 3 lagged load points, 0.1 for the momentum, 5 hidden neurons and 0.1 for the learning rate. The error of forecasting is as small as 3%. That is, comparison with the results of ordinary ANN and Grey prediction, the presented Taguchi-ANN-based forecaster gives more accurate prediction for VSTEDF.

Binarizing historically degraded as-built drawing (HDAD) maps is a new challenging job, especially in terms of removing the three artifacts, namely noise, the yellowing areas, and the folded lines, while preserving the foreground components well. In this paper, we first propose a semi-automatic labeling method to create the HDAD-pair dataset of which each HDAD-pair consists of one HDAD map and its binarized HDAD map. Based on the created training HDAD-pair dataset, we propose a convolutional neural network-based (CNN-based) binarization method to produce high-quality binarized HDAD maps. Based on the testing HDAD maps, the thorough experimental data demonstrated that in terms of the accuracy, PSNR (peak-signal-to-noise-ratio), and the perceptual effect of the binarized HDAD maps, our method substantially outperforms the nine existing binarization methods. In addition, with similar accuracy, the experimental results demonstrated the significant execution-time reduction merit of our method relative to the retrained version of the state-of-the-art CNN-based binarization methods.