ABSTRACT
Bread and soup are two of the most important foods in daily life, thus dough fermentation and nutrient soup elaboration are more and more popular, but there is a lack of relevant low-cost and high-reliable household appliances on the market. Therefore, this paper proposes automatic control methods for dough fermentation and nutrient soup elaboration based on a special microwave oven. Fermentation theory, run-up microwave fermentation principle, microwave extraction principle, NTC temperature probe design and scalable fuzzy control algorithm are described in detail. Besides, the experimental platform is set up with a temperature chamber, an optical fiber thermometer and a power meter. Experimental results demonstrate that the relationship between the heating time and flour's mass is linear. For different ambient temperature tests, the volume ratios of the fermented dough to unfermented dough of different cases range from 2.2 to 2.62, and the inside of the dough after fermentation is fluffy, with small and dense cavities. Meanwhile, there is no acid taste and skin dryness, and the power consumption of microwave fermentation is less than half of that induced by grill, convection or steam fermentation. The detection error of the NTC temperature probe with microwave shielded is 0.48 °C, and the control error of the closed loop system is less than 0.5 °C. The temperature-rise slope of water is lower than that of ingredient, and the water's temperature is about 1 °C less than that of the ingredient. The soup after microwave elaboration is amber and clear, the ingredients are intact, the water loss is less than 50 g, and the total power consumption is 684 Wh. In short, microwave-based control methods for dough fermentation and nutrient soup elaboration are effective.
ABSTRACT
The ratio of the elderly to the total population around the world is larger than 10%, and about 30% of the elderly are injured by falls each year. Accidental falls, especially bathroom falls, account for a large proportion. Therefore, fall events detection of the elderly is of great importance. In this article, a non-contact fall detector based on a Micro-electromechanical Systems Pyroelectric Infrared (MEMS PIR) sensor and a thermopile IR array sensor is designed to detect bathroom falls. Besides, image processing algorithms with a low pass filter and double boundary scans are put forward in detail. Then, the statistical features of the area, center, duration and temperature are extracted. Finally, a 3-layer BP neural network is adopted to identify the fall events. Taking into account the key factors of ambient temperature, objective, illumination, fall speed, fall state, fall area and fall scene, 640 tests were performed in total, and 5-fold cross validation is adopted. Experimental results demonstrate that the averages of the precision, recall, detection accuracy and F1-Score are measured to be 94.45%, 90.94%, 92.81% and 92.66%, respectively, which indicates that the novel detection method is feasible. Thereby, this IOT detector can be extensively used for household bathroom fall detection and is low-cost and privacy-security guaranteed.
