Abstract: A gas sensing device includes one or more chemo-resistive gas sensors; one or more heat sources, wherein the gas sensors are heated according to one or more first temperature profiles during the recovery phases and according to one or more second temperature profiles during the sense phases; a preprocessing processor for generating preprocessed signal samples; a feature extraction processor for extracting one or more feature values from the received preprocessed signal samples; and a gas concentration processor for creating a sensing result, wherein the gas concentration processor includes a classification processor for outputting a class decision value, wherein the classification processor is configured for outputting a confidence value, wherein the classification processor includes a first trained model based algorithm processor, wherein the gas concentration processor comprises a quantification processor for creating an estimation value, and wherein the quantification processor comprises a second trained m

Abstract: A gas sensing device includes chemo-resistive gas sensors, wherein each of the gas sensors generates signals corresponding to concentrations of gases in a mixture of gases; a heating arrangement for heating gas sensors according to a periodic temperature profile; a preprocessing processor for receiving the signals from each of the gas sensors and for preprocessing the received signals to generate a preprocessed signal sample for each of the gas sensors for each period of the periodic temperature profile; a feature extraction processor configured for receiving the preprocessed signal samples and for extracting for each of the periods a set of feature values from the preprocessed signal samples received for the respective period; and a gas concentration processor for receiving sets of feature values.

Abstract: A gas sensing device includes one or more chemo-resistive gas sensors; one or more heat sources; a preprocessing processor; a feature extraction processor; a discriminative embedding network processor for receiving sets of feature values and for creating for each of the sets of feature values a set of embedded feature values; a classification processor for receiving the sets of embedded feature values and for creating a classification value for each set of the embedded feature values, wherein the classification value indicates a class of a mixture of gases; and a quantification processor for receiving the sets of embedded feature values and the classification values, wherein the quantification processor is creates, for each of the gases, a sensing result for each of the sets of embedded feature values.

Abstract: The present disclosure discloses a modified hydroxypropyl methyl cellulose for an enhanced ceramic tile adhesive, which is prepared from the following raw materials by mass percent: 54%-94% of hydroxypropyl methyl cellulose, 5%-40% of starch ether, 0.5%-3% of dispersing agent and 0.5%-3% of rheological agent, wherein the hydroxypropyl methyl cellulose is prepared from cellulose powder, granular caustic soda, liquid caustic soda, chloromethane and propylene oxide. A preparation method includes: (1) weighing the raw materials; (2) mixing the cellulose powder, the granular caustic soda, the liquid caustic soda, the chloromethane and the propylene oxide, carrying out etherification reaction, and then sequentially carrying out neutralization, washing, centrifugation, drying and crushing to obtain the hydroxypropyl methyl cellulose; and (3) mixing and stirring the hydroxypropyl methyl cellulose, the starch ether, the dispersing agent and the rheological agent to obtain the modified hydroxypropyl methyl cellulose.

Abstract: The present disclosure discloses a modified hydroxyethyl methyl cellulose for an enhanced ceramic tile adhesive, which is prepared from the following raw materials by mass percent: 54%-94% of hydroxyethyl methyl cellulose, 5%-40% of starch ether, 0.5%-3% of dispersing agent and 0.5%-3% of rheological agent, wherein the hydroxyethyl methyl cellulose is prepared from cellulose powder, granular caustic soda, liquid caustic soda, chloromethane and ethylene oxide. A preparation method includes: (1) weighing the raw materials; (2) mixing the cellulose powder, the granular caustic soda, the liquid caustic soda, the chloromethane and the ethylene oxide, carrying out etherification reaction, and then sequentially carrying out neutralization, washing, centrifugation, drying and crushing to obtain the hydroxyethyl methyl cellulose; and (3) mixing and stirring the hydroxyethyl methyl cellulose, the starch ether, the dispersing agent and the rheological agent to obtain the modified hydroxyethyl methyl cellulose.

Abstract: A gas sensing device includes one or more chemo-resistive gas sensors; one or more heating elements for heating each of the gas sensors; a preprocessing block for filtering signal samples in order to generate filtered signal samples for each of the gas sensors; an information extraction block for generating representations for the filtered signal samples for each of the gas sensors based on dynamic characteristics of the received filtered signal samples of the respective gas sensor; and a decision making block for receiving the representations, wherein the decision making block includes a trained model based algorithm stage having an input layer and an output layer, wherein the decision making block includes trained models, wherein the decision making block creates sensing results based on output values of the output layer of the algorithm stage, and wherein the output values are created by using the trained models.

Abstract: A gas sensing device includes one or more chemo-resistive gas sensors; one or more heat sources, wherein the gas sensors are heated according to one or more first temperature profiles during the recovery phases and according to one or more second temperature profiles during the sense phases; a preprocessing processor for generating preprocessed signal samples; a feature extraction processor for extracting one or more feature values from the received preprocessed signal samples; and a gas concentration processor for creating a sensing result, wherein the gas concentration processor includes a classification processor for outputting a class decision value, wherein the classification processor is configured for outputting a confidence value, wherein the classification processor includes a first trained model based algorithm processor, wherein the gas concentration processor comprises a quantification processor for creating an estimation value, and wherein the quantification processor comprises a second trained m

Abstract: A gas sensing device includes one or more chemo-resistive gas sensors; one or more heating elements for heating each of the gas sensors; a preprocessing block for filtering signal samples in order to generate filtered signal samples for each of the gas sensors; an information extraction block for generating representations for the filtered signal samples for each of the gas sensors based on dynamic characteristics of the received filtered signal samples of the respective gas sensor; and a decision making block for receiving the representations, wherein the decision making block includes a trained model based algorithm stage having an input layer and an output layer, wherein the decision making block includes trained models, wherein the decision making block creates sensing results based on output values of the output layer of the algorithm stage, and wherein the output values are created by using the trained models.