High-temperature oxidation is a common failure in high-temperature environments,which widely occur in aircraft engines and aerospace thrusters;as a result,the development of anti-high-temperature oxidation materials h...High-temperature oxidation is a common failure in high-temperature environments,which widely occur in aircraft engines and aerospace thrusters;as a result,the development of anti-high-temperature oxidation materials has been pursued.Ni-based alloys are a common high-temperature material;however,they are too expensive.High-entropy alloys are alternatives for the anti-oxidation property at high temperatures because of their special structure and properties.The recent achievements of high-temperature oxidation are reviewed in this paper.The high-temperature oxidation environment,temperature,phase structure,alloy elements,and preparation methods of high-entropy alloys are summarized.The reason why high-entropy alloys have anti-oxidation ability at high temperatures is illuminated.Current research,material selection,and application prospects of high-temperature oxidation are introduced.展开更多
Because of computational complexity,the deep neural network(DNN)in embedded devices is usually trained on high-performance computers or graphic processing units(GPUs),and only the inference phase is implemented in emb...Because of computational complexity,the deep neural network(DNN)in embedded devices is usually trained on high-performance computers or graphic processing units(GPUs),and only the inference phase is implemented in embedded devices.Data processed by embedded devices,such as smartphones and wearables,are usually personalized,so the DNN model trained on public data sets may have poor accuracy when inferring the personalized data.As a result,retraining DNN with personalized data collected locally in embedded devices is necessary.Nevertheless,retraining needs labeled data sets,while the data collected locally are unlabeled,then how to retrain DNN with unlabeled data is a problem to be solved.This paper proves the necessity of retraining DNN model with personalized data collected in embedded devices after trained with public data sets.It also proposes a label generation method by which a fake label is generated for each unlabeled training case according to users’feedback,thus retraining can be performed with unlabeled data collected in embedded devices.The experimental results show that our fake label generation method has both good training effects and wide applicability.The advanced neural networks can be trained with unlabeled data from embedded devices and the individualized accuracy of the DNN model can be gradually improved along with personal using.展开更多
基金This work was financially supported by the National Natural Science Foundation of China(No.52071014)the Fundamental Research Funds for the Central Universities(No.FRF-GF-19-033BZ)the National Key Research and Development Program of China(No.2020YFB0704501).
文摘High-temperature oxidation is a common failure in high-temperature environments,which widely occur in aircraft engines and aerospace thrusters;as a result,the development of anti-high-temperature oxidation materials has been pursued.Ni-based alloys are a common high-temperature material;however,they are too expensive.High-entropy alloys are alternatives for the anti-oxidation property at high temperatures because of their special structure and properties.The recent achievements of high-temperature oxidation are reviewed in this paper.The high-temperature oxidation environment,temperature,phase structure,alloy elements,and preparation methods of high-entropy alloys are summarized.The reason why high-entropy alloys have anti-oxidation ability at high temperatures is illuminated.Current research,material selection,and application prospects of high-temperature oxidation are introduced.
基金supported by the National Natural Science Foundation of China under Grants No.61534002,No.61761136015,No.61701095.
文摘Because of computational complexity,the deep neural network(DNN)in embedded devices is usually trained on high-performance computers or graphic processing units(GPUs),and only the inference phase is implemented in embedded devices.Data processed by embedded devices,such as smartphones and wearables,are usually personalized,so the DNN model trained on public data sets may have poor accuracy when inferring the personalized data.As a result,retraining DNN with personalized data collected locally in embedded devices is necessary.Nevertheless,retraining needs labeled data sets,while the data collected locally are unlabeled,then how to retrain DNN with unlabeled data is a problem to be solved.This paper proves the necessity of retraining DNN model with personalized data collected in embedded devices after trained with public data sets.It also proposes a label generation method by which a fake label is generated for each unlabeled training case according to users’feedback,thus retraining can be performed with unlabeled data collected in embedded devices.The experimental results show that our fake label generation method has both good training effects and wide applicability.The advanced neural networks can be trained with unlabeled data from embedded devices and the individualized accuracy of the DNN model can be gradually improved along with personal using.
