Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the ...Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.展开更多
Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL...Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.展开更多
The battery technology progress has been a contradictory process in which performance improvement and hidden risks coexist.Now the battery is still a“black box”,thus requiring a deep understanding of its internal st...The battery technology progress has been a contradictory process in which performance improvement and hidden risks coexist.Now the battery is still a“black box”,thus requiring a deep understanding of its internal state.The battery should“sense its internal physical/chemical conditions”,which puts strict requirements on embedded sensing parts.This paper summarizes the application of advanced optical fiber sensors in lithium-ion batteries and energy storage technologies that may be mass deployed,focuses on the insights of advanced optical fiber sensors into the processes of one-dimensional nano-micro-level battery material structural phase transition,electrolyte degradation,electrode-electrolyte interface dynamics to three-dimensional macro-safety evolution.The paper contributes to understanding how to use optical fiber sensors to achieve“real”and“embedded”monitoring.Through the inherent advantages of the advanced optical fiber sensor,it helps clarify the battery internal state and reaction mechanism,aiding in the establishment of more detailed models.These advancements can promote the development of smart batteries,with significant importance lying in essentially promoting the improvement of system consistency.Furthermore,with the help of smart batteries in the future,the importance of consistency can be weakened or even eliminated.The application of advanced optical fiber sensors helps comprehensively improve the battery quality,reliability,and life.展开更多
Four typical theories on the formation of thermal tears:strength,liquid film,intergranular bridging,and solidifica-tion shrinkage compensation theories.From these theories,a number of criteria have been derived for pr...Four typical theories on the formation of thermal tears:strength,liquid film,intergranular bridging,and solidifica-tion shrinkage compensation theories.From these theories,a number of criteria have been derived for predicting the formation of thermal cracks,such as the stress-based Niyama,Clyne,and RDG(Rapaz-Dreiser-Grimaud)criteria.In this paper,a mathematical model of horizontal centrifugal casting was established,and numerical simulation analysis was conducted for the centrifugal casting process of cylindrical Al-Cu alloy castings to investigate the effect of the centrifugal casting process conditions on the microstructure and hot tearing sensitivity of alloy castings by using the modified RDG hot tearing criterion.Results show that increasing the centrifugal rotation and pouring speeds can refine the microstructure of the alloy but increasing the pouring and mold preheating temperatures can lead to an increase in grain size.The grain size gradually transitions from fine grain on the outer layer to coarse grain on the inner layer.Meanwhile,combined with the modified RDG hot tearing criterion,the overall distribution of the castings’hot tearing sensitivity was analyzed.The analysis results indicate that the porosity in the middle region of the casting was large,and hot tearing defects were prone to occur.The hot tearing tendency on the inner side of the casting was greater than that on the outer side.The effects of centrifugal rotation speed,pouring temperature,and preheating temperature on the thermal sensitivity of Al-Cu alloy castings are summarized in this paper.This study revealed that the tendency of alloy hot cracking decreases with the increase of the centrifugal speed,and the maximum porosity of castings decreases first and then increases with the pouring temperature.As the preheating temperature increases,the overall maximum porosity of castings shows a decreasing trend.展开更多
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation under Grant No.2022M720419。
文摘Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.
文摘Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.
基金the National Natural Science Foundation of China(No.52307245[Y.D.Li],No.U21A20170[X.He],22279070[L.Wang],and 52206263[Y.Song])the China Postdoctoral Science Foundation(No.2022M721820[Y.D.Li])the Ministry of Science and Technology of China(No.2019YFA0705703[L.Wang])。
文摘The battery technology progress has been a contradictory process in which performance improvement and hidden risks coexist.Now the battery is still a“black box”,thus requiring a deep understanding of its internal state.The battery should“sense its internal physical/chemical conditions”,which puts strict requirements on embedded sensing parts.This paper summarizes the application of advanced optical fiber sensors in lithium-ion batteries and energy storage technologies that may be mass deployed,focuses on the insights of advanced optical fiber sensors into the processes of one-dimensional nano-micro-level battery material structural phase transition,electrolyte degradation,electrode-electrolyte interface dynamics to three-dimensional macro-safety evolution.The paper contributes to understanding how to use optical fiber sensors to achieve“real”and“embedded”monitoring.Through the inherent advantages of the advanced optical fiber sensor,it helps clarify the battery internal state and reaction mechanism,aiding in the establishment of more detailed models.These advancements can promote the development of smart batteries,with significant importance lying in essentially promoting the improvement of system consistency.Furthermore,with the help of smart batteries in the future,the importance of consistency can be weakened or even eliminated.The application of advanced optical fiber sensors helps comprehensively improve the battery quality,reliability,and life.
文摘Four typical theories on the formation of thermal tears:strength,liquid film,intergranular bridging,and solidifica-tion shrinkage compensation theories.From these theories,a number of criteria have been derived for predicting the formation of thermal cracks,such as the stress-based Niyama,Clyne,and RDG(Rapaz-Dreiser-Grimaud)criteria.In this paper,a mathematical model of horizontal centrifugal casting was established,and numerical simulation analysis was conducted for the centrifugal casting process of cylindrical Al-Cu alloy castings to investigate the effect of the centrifugal casting process conditions on the microstructure and hot tearing sensitivity of alloy castings by using the modified RDG hot tearing criterion.Results show that increasing the centrifugal rotation and pouring speeds can refine the microstructure of the alloy but increasing the pouring and mold preheating temperatures can lead to an increase in grain size.The grain size gradually transitions from fine grain on the outer layer to coarse grain on the inner layer.Meanwhile,combined with the modified RDG hot tearing criterion,the overall distribution of the castings’hot tearing sensitivity was analyzed.The analysis results indicate that the porosity in the middle region of the casting was large,and hot tearing defects were prone to occur.The hot tearing tendency on the inner side of the casting was greater than that on the outer side.The effects of centrifugal rotation speed,pouring temperature,and preheating temperature on the thermal sensitivity of Al-Cu alloy castings are summarized in this paper.This study revealed that the tendency of alloy hot cracking decreases with the increase of the centrifugal speed,and the maximum porosity of castings decreases first and then increases with the pouring temperature.As the preheating temperature increases,the overall maximum porosity of castings shows a decreasing trend.
