Microstructure and gas-sensing properties of thick film sensor using nanophase SnO 2 powder

Abstract The object of the present work apply the nanophase SnO 2 powder synthesized by inert gas condensation method (IGC) to thick film hydrogen sensor and to study the effect of microstructural changes of sensors on the sensing properties. SnO 2 gas sensors were printed using nanophase SnO 2 powder without any doping element by a silk screen printing method and subsequently sintered. Among the thick film sensors manufactured at various temperatures, the highest sensitivity was obtained in the sensor sintered at 600 • C, which exhibited its maximum sensitivity of 96% at 250 • C against the hydrogen concentration of 2500 ppm. The results were discussed in viewpoint of the pore structure and the crystallinity of the sensors

COVID-19 outbreak prediction using Seq2Seq + Attention and Word2Vec keyword time series data

As of 2022, COVID-19, first reported in Wuhan, China, in November 2019, has become a worldwide epidemic, causing numerous infections and casualties and enormous social and economic damage. To mitigate its impact, various COVID-19 prediction studies have emerged, most of them using mathematical models and artificial intelligence for prediction. However, the problem with these models is that their prediction accuracy is considerably reduced when the duration of the COVID-19 outbreak is short. In this paper, we propose a new prediction method combining Word2Vec and the existing long short-term memory and Seq2Seq + Attention model. We compare the prediction error of the existing and proposed models with the COVID-19 prediction results reported from five US states: California, Texas, Florida, New York, and Illinois. The results of the experiment show that the proposed model combining Word2Vec and the existing long short-term memory and Seq2Seq + Attention achieves better prediction results and lower errors than the existing long short-term memory and Seq2Seq + Attention models. In experiments, the Pearson correlation coefficient increased by 0.05 to 0.21 and the RMSE decreased by 0.03 to 0.08 compared to the existing method

COVID-19 outbreak prediction using Seq2Seq + Attention and Word2Vec keyword time series data.

As of 2022, COVID-19, first reported in Wuhan, China, in November 2019, has become a worldwide epidemic, causing numerous infections and casualties and enormous social and economic damage. To mitigate its impact, various COVID-19 prediction studies have emerged, most of them using mathematical models and artificial intelligence for prediction. However, the problem with these models is that their prediction accuracy is considerably reduced when the duration of the COVID-19 outbreak is short. In this paper, we propose a new prediction method combining Word2Vec and the existing long short-term memory and Seq2Seq + Attention model. We compare the prediction error of the existing and proposed models with the COVID-19 prediction results reported from five US states: California, Texas, Florida, New York, and Illinois. The results of the experiment show that the proposed model combining Word2Vec and the existing long short-term memory and Seq2Seq + Attention achieves better prediction results and lower errors than the existing long short-term memory and Seq2Seq + Attention models. In experiments, the Pearson correlation coefficient increased by 0.05 to 0.21 and the RMSE decreased by 0.03 to 0.08 compared to the existing method

Week 3 prediction using Seq2Seq model.

As of 2022, COVID-19, first reported in Wuhan, China, in November 2019, has become a worldwide epidemic, causing numerous infections and casualties and enormous social and economic damage. To mitigate its impact, various COVID-19 prediction studies have emerged, most of them using mathematical models and artificial intelligence for prediction. However, the problem with these models is that their prediction accuracy is considerably reduced when the duration of the COVID-19 outbreak is short. In this paper, we propose a new prediction method combining Word2Vec and the existing long short-term memory and Seq2Seq + Attention model. We compare the prediction error of the existing and proposed models with the COVID-19 prediction results reported from five US states: California, Texas, Florida, New York, and Illinois. The results of the experiment show that the proposed model combining Word2Vec and the existing long short-term memory and Seq2Seq + Attention achieves better prediction results and lower errors than the existing long short-term memory and Seq2Seq + Attention models. In experiments, the Pearson correlation coefficient increased by 0.05 to 0.21 and the RMSE decreased by 0.03 to 0.08 compared to the existing method.</div

Seq2Seq model structure.

As of 2022, COVID-19, first reported in Wuhan, China, in November 2019, has become a worldwide epidemic, causing numerous infections and casualties and enormous social and economic damage. To mitigate its impact, various COVID-19 prediction studies have emerged, most of them using mathematical models and artificial intelligence for prediction. However, the problem with these models is that their prediction accuracy is considerably reduced when the duration of the COVID-19 outbreak is short. In this paper, we propose a new prediction method combining Word2Vec and the existing long short-term memory and Seq2Seq + Attention model. We compare the prediction error of the existing and proposed models with the COVID-19 prediction results reported from five US states: California, Texas, Florida, New York, and Illinois. The results of the experiment show that the proposed model combining Word2Vec and the existing long short-term memory and Seq2Seq + Attention achieves better prediction results and lower errors than the existing long short-term memory and Seq2Seq + Attention models. In experiments, the Pearson correlation coefficient increased by 0.05 to 0.21 and the RMSE decreased by 0.03 to 0.08 compared to the existing method.</div

Week 2 prediction using Seq2Seq model.

As of 2022, COVID-19, first reported in Wuhan, China, in November 2019, has become a worldwide epidemic, causing numerous infections and casualties and enormous social and economic damage. To mitigate its impact, various COVID-19 prediction studies have emerged, most of them using mathematical models and artificial intelligence for prediction. However, the problem with these models is that their prediction accuracy is considerably reduced when the duration of the COVID-19 outbreak is short. In this paper, we propose a new prediction method combining Word2Vec and the existing long short-term memory and Seq2Seq + Attention model. We compare the prediction error of the existing and proposed models with the COVID-19 prediction results reported from five US states: California, Texas, Florida, New York, and Illinois. The results of the experiment show that the proposed model combining Word2Vec and the existing long short-term memory and Seq2Seq + Attention achieves better prediction results and lower errors than the existing long short-term memory and Seq2Seq + Attention models. In experiments, the Pearson correlation coefficient increased by 0.05 to 0.21 and the RMSE decreased by 0.03 to 0.08 compared to the existing method.</div

Extraction methodology of time series data for training the model.

Extraction methodology of time series data for training the model.</p

Week 1 prediction using LSTM model.

As of 2022, COVID-19, first reported in Wuhan, China, in November 2019, has become a worldwide epidemic, causing numerous infections and casualties and enormous social and economic damage. To mitigate its impact, various COVID-19 prediction studies have emerged, most of them using mathematical models and artificial intelligence for prediction. However, the problem with these models is that their prediction accuracy is considerably reduced when the duration of the COVID-19 outbreak is short. In this paper, we propose a new prediction method combining Word2Vec and the existing long short-term memory and Seq2Seq + Attention model. We compare the prediction error of the existing and proposed models with the COVID-19 prediction results reported from five US states: California, Texas, Florida, New York, and Illinois. The results of the experiment show that the proposed model combining Word2Vec and the existing long short-term memory and Seq2Seq + Attention achieves better prediction results and lower errors than the existing long short-term memory and Seq2Seq + Attention models. In experiments, the Pearson correlation coefficient increased by 0.05 to 0.21 and the RMSE decreased by 0.03 to 0.08 compared to the existing method.</div