A c c e p t e d M a n u s c r i p t
Highlights: ZnCl 2 aqueous solution (>29.6 wt.%) is a non-derivatizing solvent for starch Starch can be dissolved completely and regenerated easily in ZnCl 2 solution The suggested dissolution mechanism was the formation of a "zinc-starch complex", not degradation by H + .
Highlights (for review)Page 2 of 31 A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t 2
Abstract: 14It is important to obtain starch-based homogeneous systems for starch modification. 15Regarding this, an important key point is to find cheap, low-cost and low-toxicity 16 solvents to allow complete dissolution of starch and its easy regeneration. This study 17 reveals that a ZnCl 2 aqueous solution is a good non-derivatizing solvent for starch at 18 50 °C, and can completely dissolve starch granules.
Hydrogen is considered to be a hazardous substance. Hydrogen sensors can be used to detect the concentration of hydrogen and provide an ideal monitoring means for the safe use of hydrogen energy. Hydrogen sensors need to be highly reliable, so fault identification and diagnosis for gas sensors are of vital practical significance. However, traditional machine learning methods for fault diagnosis are based on features extracted by experts, prior knowledge requirements and the sensitivity of system changes. In this study, a new convolutional neural network (CNN) using the random forest (RF) classifier is proposed for hydrogen sensor fault diagnosis. First, the 1-D time-domain data of fault signals are converted into 2-D gray matrix images; this process does not require noise suppression and no signal information is lost. Secondly, the features of the gray matrix images are automatically extracted by using a CNN, which does not rely on expert experience. Dropout and zero-padding are used to optimize the structure of the CNN and reduce overfitting. Random forest, which is robust and has strong generalization ability, is introduced for the classification of gas sensor signal modes, in order to obtain the final diagnostic results. Finally, we design and implement a prototype hydrogen sensor array for experimental verification. The accuracy of fault diagnosis in hydrogen sensors is 100% under noisy environment with the proposed method, which is superior of CNN without RF and other methods. The results show that the proposed CNN with RF method provides a good solution for hydrogen sensor fault diagnosis. INDEX TERMS Fault diagnosis, hydrogen sensor, convolutional neural network, random forest, feature extraction.
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