In-situ growing carbon nanotubes （CNTs） directly on carbon fibers （CFs） always lead to a degraded tensile strength of CFs and then a poor fiber-dominated mechanical property of carbon/carbon composites （C/ Cs）. ...In-situ growing carbon nanotubes （CNTs） directly on carbon fibers （CFs） always lead to a degraded tensile strength of CFs and then a poor fiber-dominated mechanical property of carbon/carbon composites （C/ Cs）. To solve this issue, here, a novel carbon fiber-based multiscale reinforcement is reported. To synthesize it, carbon fibers （CFs） have been first grafted by graphene oxide （GO）, and then carbon nanotubes （CNTs） have been in-situ grown on GO-grafted CFs by catalytic chemical vapor deposition. Characterizations on this novel reinforcement show that GO grafting cannot only nondestructively improve the surface chemical activity of CFs but also protect CFs against the high-temperature corrosion of metal catalyst during CNT growth, which maintains their tensile properties. Tensile property tests for unidirectional C/Cs with different preforms show that this novel reinforcement can endow C/C with improved tensile properties, 32% and 87% higher than that of pure C/C and C/C only doped with in-situ grown CNTs. This work would open up a possibility to fabricate multiscale C/Cs with excellent global performance.展开更多
Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined compo...Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.展开更多
To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination predi...To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model.Specifically,the characteristics of load components are analyzed for different seasons,and the corresponding models are established.First,the improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)method is employed to decompose the system load for all four seasons,and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component.Second,the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy.Finally,different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm,with a prediction resolution of 15 min,and the predicted values are accumulated to obtain the final results.According to the experimental findings,the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods.The results demonstrate that the proposedmethod can effectively address the load power variation induced by seasonal differences in different regions.展开更多
In response to the demand for cooling solutions in data centers with the burgeoning growth of the information era,it is imperative to explore a high-performance and energy-saving thermal management system.In this pape...In response to the demand for cooling solutions in data centers with the burgeoning growth of the information era,it is imperative to explore a high-performance and energy-saving thermal management system.In this paper,a copper-water pump-assisted loop heat pipe based on a top-down superhydrophilic multiscale composite wick-channel structure is investigated to optimize the stability and operation range in the loop heat pipe(LHP).Aided by theoretical pressure analysis,it has been demonstrated that this composite structure enhances the durability and maintenance of the large capillary pressure head.The comparison has analyzed the effects of heat leakage on the compensation chamber and the phase change in channels by establishing system thermal resistance networks.The results show that the pump-assisted loop heat pipe(P-A LHP)exhibits lower baseplate temperature fluctuation within 0.5℃and a larger operation range of more than 400 W below the baseplate temperature of 85℃.In addition,the P-A LHP elevates heat transfer capacity to 430 W by increasing the mass flow rate,and the minimum thermal resistance of 0.130℃/W is achieved compared with the LHP minimum thermal resistance of 0.217℃/W.Finally,the maximum P-A LHP coefficient of performance is 22.7 under the small mass flow rate,which is larger than most registered active cooling systems.展开更多
Though coherence, a classical method to describe the linear correlation between two time series, has wideranging applications, from economics to neuroscience, it fails to illustrate the inherently multi-time scalesbas...Though coherence, a classical method to describe the linear correlation between two time series, has wideranging applications, from economics to neuroscience, it fails to illustrate the inherently multi-time scalesbased correlations. In this paper, we proposed a multiscale-like coherence model, defined as composite multiscalecoherence (CMSC) by combining the kth coarse-grain processing with the coherence. We made a comparison withthe multiscale coherence (MSC) with coarse-grain process in numerical data to compare the sensitivity profiles tothe coupling strength, data length and white Gaussian noise. After that, we applied the proposed model to explorethe functional corticomuscular coupling (FCMC) by analyzing the correlation between the EEG and EMG signals.Simulation results reflected that the CMSC method were sensitive to the coupling strength, data length and thewhite Gaussian noise, and presented more stability along the time scale compared to the MSC method. Ourapplication of CMSC methods on the EEG and EMG signals indicated that the FCMC was of multi-time scalecharacteristics and higher coherence mainly consisted in the alpha and beta bands at about scale 10, thoughsignificant area showed a gradual decline with the scale increasing. Further comparison indicated that bothmodels are equally effective to describe the multiscale characteristics of the FCMC at lower time scales, whilesome differences emerge at the high time scales. Both simulation and experimental data demonstrate the effectiveness of the proposed multiscale-like model to describe the multiscale correlation between two time series. Thisstudy extends the relative researches on the FCMC to the multi-time scale.展开更多
With the framework of exterior product,we investigate the relationship between composite multiscale entropy[CMSE]and refractive index and absorption coefficient by reanalyzing six concentrations of bovine serum albumi...With the framework of exterior product,we investigate the relationship between composite multiscale entropy[CMSE]and refractive index and absorption coefficient by reanalyzing six concentrations of bovine serum albumin aqueous solutions from the published work.Two bivectors are constructed by CMSE and its square by the refractive index and absorption coefficient under vectorization.The desirable linear behaviors can be captured,not only between the defined two bivectors in normalized magnitudes,but also between the normalized magnitude of bivectors pertinent to CMSE and the magnitude of a single vector on the refractive index or absorption coefficient,with the processing of optimum selection.Besides that,the relationship between the coefficients of two bivectors is also considered.The results reveal that plenty of sound linear behaviors can be found and also suggest the scale of 15,16 and frequency of 0.2,0.21 THz are prominent for those linear behaviors.This work provides a new insight into the correlation between terahertz[THz]time and frequency domain information.展开更多
Three-dimensional(3D)porous piezoresistive sensors are widely used because of their simple fabrication and convenient signal acquisition.However,because of the dependence on organic skeleton materials and the complexi...Three-dimensional(3D)porous piezoresistive sensors are widely used because of their simple fabrication and convenient signal acquisition.However,because of the dependence on organic skeleton materials and the complexity of conductive coating preparation,the electrical and mechanical properties of 3D wearable piezoresistive sensors have gradually failed to accommodate many emerging fields.Here,a new flexible 3D piezoresistive sensor(NF3PS)with high sensitivity and a wide measurement range is proposed,which comprises a natural porous loofah as a flexible framework and carbon fiber/carbon nanotube(CF/CNT)multiscale composite as a conductive coating.Composed of cellulose and lignin,the irregular,porous loofah has excellent mechanical strength,elasticity,and toughness,ensuring a repeated compression/recovery behavior of the NF3PS.In addition,compared with the single-size carbon coating,the coupling of multiscale CF/CNT composite coating improves sensitivities over a range of pressures.The NF3PS demonstrates a sensitivity of 6.94 kPa^(-1) with good linearity in the pressure range of 0–11.2 kPa and maintains a sensitivity of 0.28 kPa^(-1) in an ultrawide measurement range of 11.2–84.6 kPa.Considering flexibility,robustness,and wide-ranging linear resistance variation,the feasibility of the NF3PS in human activity monitoring,mechanical control,and smart homes is verified.This work provides a novel strategy for a new generation of 3D flexible pressure sensors for improving sensitivity and measurement range and demonstrates attractive applications in wearable sensors.展开更多
基金supported by the National Natural Science Foundation of China (Nos.51432008,51502242,U1435202,and 51202194)the Research Fund for the Doctoral Program of Higher Education of China (No.20126102110013)the Key Grant Project of Chinese Ministry of Education (No.313047)
文摘In-situ growing carbon nanotubes （CNTs） directly on carbon fibers （CFs） always lead to a degraded tensile strength of CFs and then a poor fiber-dominated mechanical property of carbon/carbon composites （C/ Cs）. To solve this issue, here, a novel carbon fiber-based multiscale reinforcement is reported. To synthesize it, carbon fibers （CFs） have been first grafted by graphene oxide （GO）, and then carbon nanotubes （CNTs） have been in-situ grown on GO-grafted CFs by catalytic chemical vapor deposition. Characterizations on this novel reinforcement show that GO grafting cannot only nondestructively improve the surface chemical activity of CFs but also protect CFs against the high-temperature corrosion of metal catalyst during CNT growth, which maintains their tensile properties. Tensile property tests for unidirectional C/Cs with different preforms show that this novel reinforcement can endow C/C with improved tensile properties, 32% and 87% higher than that of pure C/C and C/C only doped with in-situ grown CNTs. This work would open up a possibility to fabricate multiscale C/Cs with excellent global performance.
基金Projects(City U 11201315,T32-101/15-R)supported by the Research Grants Council of the Hong Kong Special Administrative Region,China
文摘Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.
文摘To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model.Specifically,the characteristics of load components are analyzed for different seasons,and the corresponding models are established.First,the improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)method is employed to decompose the system load for all four seasons,and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component.Second,the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy.Finally,different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm,with a prediction resolution of 15 min,and the predicted values are accumulated to obtain the final results.According to the experimental findings,the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods.The results demonstrate that the proposedmethod can effectively address the load power variation induced by seasonal differences in different regions.
基金supported by the National Natural Science Foundation of China Project(Grant No.52006218).
文摘In response to the demand for cooling solutions in data centers with the burgeoning growth of the information era,it is imperative to explore a high-performance and energy-saving thermal management system.In this paper,a copper-water pump-assisted loop heat pipe based on a top-down superhydrophilic multiscale composite wick-channel structure is investigated to optimize the stability and operation range in the loop heat pipe(LHP).Aided by theoretical pressure analysis,it has been demonstrated that this composite structure enhances the durability and maintenance of the large capillary pressure head.The comparison has analyzed the effects of heat leakage on the compensation chamber and the phase change in channels by establishing system thermal resistance networks.The results show that the pump-assisted loop heat pipe(P-A LHP)exhibits lower baseplate temperature fluctuation within 0.5℃and a larger operation range of more than 400 W below the baseplate temperature of 85℃.In addition,the P-A LHP elevates heat transfer capacity to 430 W by increasing the mass flow rate,and the minimum thermal resistance of 0.130℃/W is achieved compared with the LHP minimum thermal resistance of 0.217℃/W.Finally,the maximum P-A LHP coefficient of performance is 22.7 under the small mass flow rate,which is larger than most registered active cooling systems.
基金funded by National Natural Science Foundation of China grant(U20A20192 and 62076216)Hebei Natural Science Foundation(F2022203002,F2021203033 and G2020203012)+2 种基金Cultivation Project for Basic Research and Innovation of Yanshan University(2021LGZD010)the Funding Program for Innovative Ability Training of graduate students of Hebei Provincial Department of Education under Grant CXZZSS2022123Hebei Innovation Capability Improvement Plan Project(22567619H).
文摘Though coherence, a classical method to describe the linear correlation between two time series, has wideranging applications, from economics to neuroscience, it fails to illustrate the inherently multi-time scalesbased correlations. In this paper, we proposed a multiscale-like coherence model, defined as composite multiscalecoherence (CMSC) by combining the kth coarse-grain processing with the coherence. We made a comparison withthe multiscale coherence (MSC) with coarse-grain process in numerical data to compare the sensitivity profiles tothe coupling strength, data length and white Gaussian noise. After that, we applied the proposed model to explorethe functional corticomuscular coupling (FCMC) by analyzing the correlation between the EEG and EMG signals.Simulation results reflected that the CMSC method were sensitive to the coupling strength, data length and thewhite Gaussian noise, and presented more stability along the time scale compared to the MSC method. Ourapplication of CMSC methods on the EEG and EMG signals indicated that the FCMC was of multi-time scalecharacteristics and higher coherence mainly consisted in the alpha and beta bands at about scale 10, thoughsignificant area showed a gradual decline with the scale increasing. Further comparison indicated that bothmodels are equally effective to describe the multiscale characteristics of the FCMC at lower time scales, whilesome differences emerge at the high time scales. Both simulation and experimental data demonstrate the effectiveness of the proposed multiscale-like model to describe the multiscale correlation between two time series. Thisstudy extends the relative researches on the FCMC to the multi-time scale.
基金supported by the National Natural Science Foundation of China(Nos.U1837202 and 11804209)Joint Funds of the Equipment Pre-research and Aerospace Science and Technology(No.6141B061006)。
文摘With the framework of exterior product,we investigate the relationship between composite multiscale entropy[CMSE]and refractive index and absorption coefficient by reanalyzing six concentrations of bovine serum albumin aqueous solutions from the published work.Two bivectors are constructed by CMSE and its square by the refractive index and absorption coefficient under vectorization.The desirable linear behaviors can be captured,not only between the defined two bivectors in normalized magnitudes,but also between the normalized magnitude of bivectors pertinent to CMSE and the magnitude of a single vector on the refractive index or absorption coefficient,with the processing of optimum selection.Besides that,the relationship between the coefficients of two bivectors is also considered.The results reveal that plenty of sound linear behaviors can be found and also suggest the scale of 15,16 and frequency of 0.2,0.21 THz are prominent for those linear behaviors.This work provides a new insight into the correlation between terahertz[THz]time and frequency domain information.
基金supported by the National Natural Science Foundation of China(Grant No.52175554)the Natural Science Foundation of Hebei Province(Grant No.F2021409007)+2 种基金the Hebei Province Foundation for the Returned Overseas Chinese Scholars(Grant No.C20220103)the School Research Fund Project(Grant Nos.ZDYY-2021-01,YKY-2022-33)。
文摘Three-dimensional(3D)porous piezoresistive sensors are widely used because of their simple fabrication and convenient signal acquisition.However,because of the dependence on organic skeleton materials and the complexity of conductive coating preparation,the electrical and mechanical properties of 3D wearable piezoresistive sensors have gradually failed to accommodate many emerging fields.Here,a new flexible 3D piezoresistive sensor(NF3PS)with high sensitivity and a wide measurement range is proposed,which comprises a natural porous loofah as a flexible framework and carbon fiber/carbon nanotube(CF/CNT)multiscale composite as a conductive coating.Composed of cellulose and lignin,the irregular,porous loofah has excellent mechanical strength,elasticity,and toughness,ensuring a repeated compression/recovery behavior of the NF3PS.In addition,compared with the single-size carbon coating,the coupling of multiscale CF/CNT composite coating improves sensitivities over a range of pressures.The NF3PS demonstrates a sensitivity of 6.94 kPa^(-1) with good linearity in the pressure range of 0–11.2 kPa and maintains a sensitivity of 0.28 kPa^(-1) in an ultrawide measurement range of 11.2–84.6 kPa.Considering flexibility,robustness,and wide-ranging linear resistance variation,the feasibility of the NF3PS in human activity monitoring,mechanical control,and smart homes is verified.This work provides a novel strategy for a new generation of 3D flexible pressure sensors for improving sensitivity and measurement range and demonstrates attractive applications in wearable sensors.
