Nanoelectromechanical systems(NEMS)based on atomically-thin tungsten diselenide(WSe_(2)),benefiting from the excellent material properties and the mechanical degree of freedom,offer an ideal platform for studying and ...Nanoelectromechanical systems(NEMS)based on atomically-thin tungsten diselenide(WSe_(2)),benefiting from the excellent material properties and the mechanical degree of freedom,offer an ideal platform for studying and exploiting dynamic strain engineering and cross-scale vibration coupling in two-dimensional(2D)crystals.However,such opportunity has remained largely unexplored for WSe_(2)NEMS,impeding exploration of exquisite physical processes and realization of novel device functions.Here,we demonstrate dynamic coupling between atomic lattice vibration and nanomechanical resonances in few-layer WSe_(2)NEMS.Using a custom-built setup capable of simultaneously detecting Raman and motional signals,we accomplish cross-scale mode coupling between the THz crystal phonon and MHz structural vibration,achieving GHz frequency tuning in the atomic lattice modes with a dynamic gauge factor of 61.9,the best among all 2D crystals reported to date.Our findings show that such 2D NEMS offer great promises for exploring cross-scale physics in atomically-thin semiconductors.展开更多
As an emerging research field,inductively coupled wireless power transfer(ICWPT) technology has attracted wide spread attention recently.In this paper,the maximum power transfer performances of four basic topologies l...As an emerging research field,inductively coupled wireless power transfer(ICWPT) technology has attracted wide spread attention recently.In this paper,the maximum power transfer performances of four basic topologies labeled as SS,SP,PS and PP are investigated.By modeling the equivalent circuits of these topologies in high frequency(HF),the primary resonance compensation capacitances for maximum power transfer capability are deduced.It is found that these capacitances fluctuate with load resistance change,which is disadvantageous to SP,PS and PP topologies and an obstacle to their practical applications as well.To solve this problem,a phase controlled inductor circuit is proposed.By adjusting the triggering angle,the real-time dynamic tuning control can be achieved to guarantee maximum power transfer.Finally,simulations and experiments show that the proposed method is of great effectiveness and reliability to solve the issue of resonance compensation capacitance fluctuation with load change and to guarantee the flexible applications of all topologies.展开更多
Aim To analyze the mathematical error model of a dynamically tuned gyro (DTG) strapdown northfinder in detail, guide the process of design, manufacture and adjustment of northfinder. Methods Each error source of thi...Aim To analyze the mathematical error model of a dynamically tuned gyro (DTG) strapdown northfinder in detail, guide the process of design, manufacture and adjustment of northfinder. Methods Each error source of this type of northfinder was determined, and the influence of each source on northfinding result was formulated. Results and Conclusion Under the guidance of the analysis, select relevant method for each source which has different effect on result to reduce northfinding error, a type of northfinder meeting the practical requirements of user was developed.展开更多
Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector ...Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.展开更多
The threat to information security from electromagnetic pollution has sparked widespread interest in the development of microwave absorption materials(MAMs).Although considerable progress has been made in high-perform...The threat to information security from electromagnetic pollution has sparked widespread interest in the development of microwave absorption materials(MAMs).Although considerable progress has been made in high-performance MAMs,little attention was paid to their absorption frequency regulation to respond to variable input frequencies and their stability and durability to cope with complex environments.Here,a highly compressible polyimide-packaging carbon nanocoils/carbon foam(PI@CNCs/CF)fabricated by a facile vacuum impregnation method is reported to be used as a dynamically frequency-tunable and environmentally stable microwave absorber.PI@CNCs/CF exhibits good structural stability and mechanical properties,which allows precise absorption frequency tuning by simply changing its compression ratio.For the first time,the tunable effective absorption bandwidth can cover the whole test frequency band(2−18 GHz)with the broadest effective absorption bandwidth of 10.8 GHz and the minimum reflection loss of−60.5 dB.Moreover,PI@CNCs/CF possesses excellent heat insulation,infrared stealth,self-cleaning,flame retardant,and acid-alkali corrosion resistance,which endows it high reliability even under various harsh environments and repeated compression testing.The frequency-tunable mechanism is elucidated by combining experiment and simulation results,possibly guiding in designing dynamically frequency-tunable MAMs with good environmental stability in the future.展开更多
High luminous efficiency and high color rendering index(CRI) are both the foremost factors for white organic lightemitting diodes(WOLEDs) to serve as next generation solid-state lighting sources. In this paper, we sho...High luminous efficiency and high color rendering index(CRI) are both the foremost factors for white organic lightemitting diodes(WOLEDs) to serve as next generation solid-state lighting sources. In this paper, we show that both luminous efficiency and CRI can be improved by adjusting the green/red spectra of WOLEDs. With green emission spectra matching with the human photopic curve, the WOLEDs exhibit higher luminous efficiency and higher CRI. Theoretical calculation shows that by tuning the white emission spectra to maximally match with the human photopic curve, the luminous efficiency can be improved by 41.8% without altering the color coordinates, the color correlated temperature(CCT) and the external quantum efficiency(EQE) of the WOLEDs.展开更多
To address the issues of small target miss detection,false positives in complex scenarios,and insufficient real-time performance in maglev train foreign object intrusion detection,this paper proposes a multi-module fu...To address the issues of small target miss detection,false positives in complex scenarios,and insufficient real-time performance in maglev train foreign object intrusion detection,this paper proposes a multi-module fusion improvement algorithm,YOLO11-FADA(Fusion of Augmented Features and Dynamic Attention),based on YOLO11.The model achieves collaborative optimization through three key modules:The Local Feature Augmentation Module(LFAM)enhances small target features and mitigates feature loss during down-sampling through multi-scale feature parallel extraction and attention fusion.The Dynamically Tuned Self-Attention(DTSA)module introduces learnable parameters to adjust attentionweights dynamically,and,in combinationwith convolution,expands the receptive field to suppress complex background interference.TheWeighted Convolution 2D(wConv2D)module optimizes convolution kernel weights using symmetric density functions and sparsification,reducing the parameter count by 30% while retaining core feature extraction capabilities.YOLO11-FADA achieves a mAP@0.5 of 0.907 on a custom maglev train foreign object dataset,improving by 3.0% over the baseline YOLO11 model.The model’s computational complexity is 7.3 GFLOPs,with a detection speed of 118.6 FPS,striking a balance between detection accuracy and real-time performance,thereby offering an efficient solution for rail transit safety monitoring.展开更多
Precisely forecasting the performance of Deep Learning(DL)models,particularly in critical areas such as Uniform Resource Locator(URL)-based threat detection,aids in improving systems developed for difficult tasks.In c...Precisely forecasting the performance of Deep Learning(DL)models,particularly in critical areas such as Uniform Resource Locator(URL)-based threat detection,aids in improving systems developed for difficult tasks.In cybersecurity,recognizing harmful URLs is vital to lowering risks associated with phishing,malware,and other online-based attacks.Since it directly affects the model’s capacity to differentiate between benign and harmful URLs,finding the optimum mix of hyperparameters in DL models is a significant difficulty.Two commonly used architectures for sequential and spatial data processing,Long Short-Term Memory(LSTM)/Gated Recurrent Unit(GRU)and Convolutional Neural Network(CNN)/Long Short-Term Memory(LSTM)models are targeted in this study to have higher predictive capacity by modifying crucial hyperparameters such as learning rate,batch size,and dropout rate using cloud capability.Research finds the best settings for the models by testing 50 dropout rates(between 0.1 and 0.5)with different learning rates and batch sizes.Performances were measured in the form of accuracy,precision,recall,F1-score,and errors such as Mean Absolute Error(MAE),Mean Squared Error(MSE),Root Mean Squared Error(RMSE)and Mean Absolute Percent Error(MAPE).In our results,CNN/LSTM performed better often than LSTM/GRU,with up to 10%better F1-score and much lower MAPE when the learning rate was 0.001 and the dropout rate was 0.2.These results show the value of fine-tuning hyperparameters to increase model performance and reduce errors.Higher on many of the parameters,CNN/LSTM architecture became obvious as the more trustworthy one.It also discussed the importance of DL in enhancing URL attack detection mechanisms to provide increased accuracy and precision for real-world cybersecurity.展开更多
基金support from the National Science Fund for Distinguished Young Scholars(Grant No.T2325007)Opening Foundation of Hubei Key Laboratory of MicroNanoelectronic Materials and Devices(Grant No.K202307)+3 种基金National Natural Science Foundation of China(Grant Nos.62450003,62404029,62401104,U21A20459,62250073,61774029)Opening Foundation of State key laboratory of precision measuring technology and instruments(Tianjin University)(Grant No.Pilab2411)China Postdoctoral Science Foundation(Grant Nos.GZB20230107,GZB20240109)Natural Science Foundation of Sichuan Province(Grant Nos.2024NSFSC1408,2024NSFSC1430).
文摘Nanoelectromechanical systems(NEMS)based on atomically-thin tungsten diselenide(WSe_(2)),benefiting from the excellent material properties and the mechanical degree of freedom,offer an ideal platform for studying and exploiting dynamic strain engineering and cross-scale vibration coupling in two-dimensional(2D)crystals.However,such opportunity has remained largely unexplored for WSe_(2)NEMS,impeding exploration of exquisite physical processes and realization of novel device functions.Here,we demonstrate dynamic coupling between atomic lattice vibration and nanomechanical resonances in few-layer WSe_(2)NEMS.Using a custom-built setup capable of simultaneously detecting Raman and motional signals,we accomplish cross-scale mode coupling between the THz crystal phonon and MHz structural vibration,achieving GHz frequency tuning in the atomic lattice modes with a dynamic gauge factor of 61.9,the best among all 2D crystals reported to date.Our findings show that such 2D NEMS offer great promises for exploring cross-scale physics in atomically-thin semiconductors.
基金supported by the National High-Tech Research & Development Program of China ("863" Program) (Grant No. 2012AA050210)the National Natural Science Foundation of China (Grant No. 51177011)+1 种基金the Research Innovation Program for College Graduates of Jiangsu Province (Grant No. CXZZ11_0150)Scholarship Award for Excellent Doctoral Student granted by Ministry of Education of China
文摘As an emerging research field,inductively coupled wireless power transfer(ICWPT) technology has attracted wide spread attention recently.In this paper,the maximum power transfer performances of four basic topologies labeled as SS,SP,PS and PP are investigated.By modeling the equivalent circuits of these topologies in high frequency(HF),the primary resonance compensation capacitances for maximum power transfer capability are deduced.It is found that these capacitances fluctuate with load resistance change,which is disadvantageous to SP,PS and PP topologies and an obstacle to their practical applications as well.To solve this problem,a phase controlled inductor circuit is proposed.By adjusting the triggering angle,the real-time dynamic tuning control can be achieved to guarantee maximum power transfer.Finally,simulations and experiments show that the proposed method is of great effectiveness and reliability to solve the issue of resonance compensation capacitance fluctuation with load change and to guarantee the flexible applications of all topologies.
文摘Aim To analyze the mathematical error model of a dynamically tuned gyro (DTG) strapdown northfinder in detail, guide the process of design, manufacture and adjustment of northfinder. Methods Each error source of this type of northfinder was determined, and the influence of each source on northfinding result was formulated. Results and Conclusion Under the guidance of the analysis, select relevant method for each source which has different effect on result to reduce northfinding error, a type of northfinder meeting the practical requirements of user was developed.
文摘Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.
基金supported by the National Natural Science Foundation of China(grants 22278101,22068010,and 52365044)the Natural Science Foundation of Hainan Province(grants 120RC454 and 519QN176)the Innovation Project for Scientific and Technological Talents in Hainan Province(grant KJRC2023C08)。
文摘The threat to information security from electromagnetic pollution has sparked widespread interest in the development of microwave absorption materials(MAMs).Although considerable progress has been made in high-performance MAMs,little attention was paid to their absorption frequency regulation to respond to variable input frequencies and their stability and durability to cope with complex environments.Here,a highly compressible polyimide-packaging carbon nanocoils/carbon foam(PI@CNCs/CF)fabricated by a facile vacuum impregnation method is reported to be used as a dynamically frequency-tunable and environmentally stable microwave absorber.PI@CNCs/CF exhibits good structural stability and mechanical properties,which allows precise absorption frequency tuning by simply changing its compression ratio.For the first time,the tunable effective absorption bandwidth can cover the whole test frequency band(2−18 GHz)with the broadest effective absorption bandwidth of 10.8 GHz and the minimum reflection loss of−60.5 dB.Moreover,PI@CNCs/CF possesses excellent heat insulation,infrared stealth,self-cleaning,flame retardant,and acid-alkali corrosion resistance,which endows it high reliability even under various harsh environments and repeated compression testing.The frequency-tunable mechanism is elucidated by combining experiment and simulation results,possibly guiding in designing dynamically frequency-tunable MAMs with good environmental stability in the future.
基金supported by the National Natural Science Foundation of China(No.61405089)the Innovation of Science and Technology Committee of Shenzhen(No.JCYJ20140417105742713)
文摘High luminous efficiency and high color rendering index(CRI) are both the foremost factors for white organic lightemitting diodes(WOLEDs) to serve as next generation solid-state lighting sources. In this paper, we show that both luminous efficiency and CRI can be improved by adjusting the green/red spectra of WOLEDs. With green emission spectra matching with the human photopic curve, the WOLEDs exhibit higher luminous efficiency and higher CRI. Theoretical calculation shows that by tuning the white emission spectra to maximally match with the human photopic curve, the luminous efficiency can be improved by 41.8% without altering the color coordinates, the color correlated temperature(CCT) and the external quantum efficiency(EQE) of the WOLEDs.
文摘To address the issues of small target miss detection,false positives in complex scenarios,and insufficient real-time performance in maglev train foreign object intrusion detection,this paper proposes a multi-module fusion improvement algorithm,YOLO11-FADA(Fusion of Augmented Features and Dynamic Attention),based on YOLO11.The model achieves collaborative optimization through three key modules:The Local Feature Augmentation Module(LFAM)enhances small target features and mitigates feature loss during down-sampling through multi-scale feature parallel extraction and attention fusion.The Dynamically Tuned Self-Attention(DTSA)module introduces learnable parameters to adjust attentionweights dynamically,and,in combinationwith convolution,expands the receptive field to suppress complex background interference.TheWeighted Convolution 2D(wConv2D)module optimizes convolution kernel weights using symmetric density functions and sparsification,reducing the parameter count by 30% while retaining core feature extraction capabilities.YOLO11-FADA achieves a mAP@0.5 of 0.907 on a custom maglev train foreign object dataset,improving by 3.0% over the baseline YOLO11 model.The model’s computational complexity is 7.3 GFLOPs,with a detection speed of 118.6 FPS,striking a balance between detection accuracy and real-time performance,thereby offering an efficient solution for rail transit safety monitoring.
文摘Precisely forecasting the performance of Deep Learning(DL)models,particularly in critical areas such as Uniform Resource Locator(URL)-based threat detection,aids in improving systems developed for difficult tasks.In cybersecurity,recognizing harmful URLs is vital to lowering risks associated with phishing,malware,and other online-based attacks.Since it directly affects the model’s capacity to differentiate between benign and harmful URLs,finding the optimum mix of hyperparameters in DL models is a significant difficulty.Two commonly used architectures for sequential and spatial data processing,Long Short-Term Memory(LSTM)/Gated Recurrent Unit(GRU)and Convolutional Neural Network(CNN)/Long Short-Term Memory(LSTM)models are targeted in this study to have higher predictive capacity by modifying crucial hyperparameters such as learning rate,batch size,and dropout rate using cloud capability.Research finds the best settings for the models by testing 50 dropout rates(between 0.1 and 0.5)with different learning rates and batch sizes.Performances were measured in the form of accuracy,precision,recall,F1-score,and errors such as Mean Absolute Error(MAE),Mean Squared Error(MSE),Root Mean Squared Error(RMSE)and Mean Absolute Percent Error(MAPE).In our results,CNN/LSTM performed better often than LSTM/GRU,with up to 10%better F1-score and much lower MAPE when the learning rate was 0.001 and the dropout rate was 0.2.These results show the value of fine-tuning hyperparameters to increase model performance and reduce errors.Higher on many of the parameters,CNN/LSTM architecture became obvious as the more trustworthy one.It also discussed the importance of DL in enhancing URL attack detection mechanisms to provide increased accuracy and precision for real-world cybersecurity.
