The key to solve increasingly severe electromagnetic(EM)pollution is to explore sustainable,easily prepared,and cost-effective EM wave absorption materials with exceptional absorption capability.Herein,instead of anch...The key to solve increasingly severe electromagnetic(EM)pollution is to explore sustainable,easily prepared,and cost-effective EM wave absorption materials with exceptional absorption capability.Herein,instead of anchoring on carbon materials in single layer,MoS_(2) flower-like microspheres were stacked on the surface of pomelo peels-derived porous carbon nanosheets(C)to fabricate MoS_(2)@C nanocomposites by a facile solvothermal process.EM wave absorption performances of MoS_(2)@C nanocomposites in X-band were systematically investigated,indicating the minimum reflection loss(RLmin)of-62.3 dB(thickness of 2.88 mm)and effective absorption bandwidth(EAB)almost covering the whole X-band(thickness of 2.63 mm)with the filler loading of only 20 wt.%.Superior EM wave absorption performances of MoS_(2)@C nanocomposites could be attributed to the excellent impedance matching characteristic and dielectric loss capacity(conduction loss and polarization loss).This study revealed that the as-prepared MoS_(2)@C nanocomposites would be a novel prospective candidate for the sustainable EM absorbents with superior EM wave absorption performances.展开更多
Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed un...Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed under different icing conditions.Due to the complexity of the icing process,the rapid assessment of ice shape remains an important challenge.In this paper,an efficient prediction model of aircraft icing is established based on the deep belief network(DBN)and the stacked auto-encoder(SAE),which are all deep neural networks.The detailed network structures are designed and then the networks are trained according to the samples obtained by the icing numerical computation.After that the model is applied on the ice shape evaluation of NACA0012 airfoil.The results show that the model can accurately capture the nonlinear behavior of aircraft icing and thus make an excellent ice shape prediction.The model provides an important tool for aircraft icing analysis.展开更多
Stacked（insect and herbicide resistant） transgenic rice T1c-19 with cry1C＊/bar genes, its receptor rice Minghui 63（herein MH63） and a local two-line hybrid indica rice Fengliangyou Xiang 1（used as a control） we...Stacked（insect and herbicide resistant） transgenic rice T1c-19 with cry1C＊/bar genes, its receptor rice Minghui 63（herein MH63） and a local two-line hybrid indica rice Fengliangyou Xiang 1（used as a control） were compared for agronomic performance under field conditions without the relevant selection pressures. Agronomic traits（plant height, tiller number, and aboveground dry biomass）, reproductive ability（pollen viability, panicle length, and filled grain number of main panicles, seed set, and grain yield）, and weediness characteristics（seed shattering, seed overwintering ability, and volunteer seedling recruitment） were used to assess the potential weediness without selection pressure of stacked transgene rice T1c-19. In wet direct-seeded and transplanted rice fields, T1c-19 and its receptor MH63 performed similarly regarding vegetative growth and reproductive ability, but both of them were significantly inferior to the control. T1c-19 did not display weed characteristics; it had weak overwintering ability, low seed shattering and failed to establish volunteers. Exogenous insect and herbicide resistance genes did not confer competitive advantage to transgenic rice T1c-19 grown in the field without the relevant selection pressures.展开更多
In aircraft assembly, interlayer burr formation in dry drilling of stacked metal materials is a common problem. Traditional manual deburring operation seriously affects the assembly qual- ity and assembly efficiency, ...In aircraft assembly, interlayer burr formation in dry drilling of stacked metal materials is a common problem. Traditional manual deburring operation seriously affects the assembly qual- ity and assembly efficiency, is time-consuming and costly, and is not conducive to aircraft automatic assembly based on industrial robot. In this paper, the formation of drilling exit burr and the influ- ence of interlayer gap on interlayer burr formation were studied, and the mechanism of interlayer gap formation in drilling stacked aluminum alloy plates was investigated, a simplified mathematical model of interlayer gap based on the theory of plates and shells and finite element method was established. The relationship between interlayer gap and interlayer burr, as well as the effect of feed rate and pressing force on interlayer burr height and interlayer gap was discussed. The result shows that theoretical interlayer gap has a positive correlation with interlayer burr height and preloading nressing force is an effective method to control interlaver burr formation.展开更多
This paper proposes a latch that can mitigate SEUs via an error detection circuit.The error detection circuit is hardened by a C-element and a stacked PMOS.In the hold state,a particle strikes the latch or the error d...This paper proposes a latch that can mitigate SEUs via an error detection circuit.The error detection circuit is hardened by a C-element and a stacked PMOS.In the hold state,a particle strikes the latch or the error detection circuit may cause a fault logic state of the circuit.The error detection circuit can detect the upset node in the latch and the fault output will be corrected.The upset node in the error detection circuit can be corrected by the C-element.The power dissipation and propagation delay of the proposed latch are analyzed by HSPICE simulations.The proposed latch consumes about 77.5%less energy and 33.1%less propagation delay than the triple modular redundancy（TMR）latch.Simulation results demonstrate that the proposed latch can mitigate SEU effectively.展开更多
Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep...Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.展开更多
This paper proposes a novel fault diagnosis method by fusing the information from multi-sensor signals to improve the reliability of the conventional vibration-based wind turbine drivetrain gearbox fault diagnosis met...This paper proposes a novel fault diagnosis method by fusing the information from multi-sensor signals to improve the reliability of the conventional vibration-based wind turbine drivetrain gearbox fault diagnosis methods.The method fully extracts fault features for variable speed,insufficient samples,and strong noise scenarios that may occur in the actual operation of a wind turbine planetary gearbox.First,multiple sensor signals are added to the diagnostic model,and multiple stacked denoising auto-encoders are designed and improved to extract the fault information.Then,a cycle reservoir with regular jumps is introduced to fuse multidimensional fault information and output diagnostic results in response to the insufficient ability to process fused information by the conventional Softmax classifier.In addition,the competitive swarm optimizer algorithm is introduced to address the challenge of obtaining the optimal combination of parameters in the network.Finally,the validation results show that the proposed method can increase fault diagnostic accuracy and improve robustness.展开更多
Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models(BEMs).The approach can be used to investigate patterns and processes ...Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models(BEMs).The approach can be used to investigate patterns and processes of species richness.If data limitations on individual species distributions are inevitable,but how do they affect inferences of patterns and processes of species richness?We investigate the influence of different data sources on estimated species richness gradients in China.We fitted BEMs using species distributions data for 334 bird species obtained from(1)global range maps,(2)regional checklists,(3)museum records and surveys,and(4)citizen science data using presence-only(Mahalanobis distance),presence-background(MAXENT),and presence–absence(GAM and BRT)BEMs.Individual species predictions were stacked to generate species richness gradients.Here,we show that different data sources and BEMs can generate spatially varying gradients of species richness.The environmental predictors that best explained species distributions also differed between data sources.Models using citizen-based data had the highest accuracy,whereas those using range data had the lowest accuracy.Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty.When multiple data sets exist for the same region and taxa,we advise that explicit treatments of uncertainty,such as sensitivity analyses of the input data,should be conducted during the process of modeling.展开更多
In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciat...In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.展开更多
This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols w...This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout.The model,called Stacked Generalization for Channel Estimation(SGCE),aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput.The SGCE model incorporates six machine learning methods:random forest(RF),gradient boosting machine(GB),light gradient boosting machine(LGBM),support vector regression(SVR),extremely randomized tree(ERT),and extreme gradient boosting(XGB).By generating meta-data from five models(RF,GB,LGBM,SVR,and ERT),we ensure accurate channel coefficient predictions using the XGB model.To validate themodeling performance,we employ the leave-one-out cross-validation(LOOCV)approach,where each observation serves as the validation set while the remaining observations act as the training set.SGCE performances’results demonstrate higher mean andmedian accuracy compared to the separatedmodel.SGCE achieves an average accuracy of 98.4%,precision of 98.1%,and the highest F1-score of 98.5%,accurately predicting channel coefficients.Furthermore,our proposedmethod outperforms prior traditional and intelligent techniques in terms of throughput and bit error rate.SGCE’s superior performance highlights its efficacy in optimizing channel estimation.It can effectively predict channel coefficients and contribute to enhancing the overall efficiency of radio mobile systems.Through extensive experimentation and evaluation,we demonstrate that SGCE improved performance in channel estimation,surpassing previous techniques.Accordingly,SGCE’s capabilities have significant implications for optimizing channel estimation in modern communication systems.展开更多
In this study,the effects of stacked nanosheets and the surrounding interphase zone on the resistance of the contact region between nanosheets and the tunneling conductivity of samples are evaluated with developed equ...In this study,the effects of stacked nanosheets and the surrounding interphase zone on the resistance of the contact region between nanosheets and the tunneling conductivity of samples are evaluated with developed equations superior to those previously reported.The contact resistance and nanocomposite conductivity are modeled by several influencing factors,including stack properties,interphase depth,tunneling size,and contact diameter.The developed model's accuracy is verified through numerous experimental measurements.To further validate the models and establish correlations between parameters,the effects of all the variables on contact resistance and nanocomposite conductivity are analyzed.Notably,the contact resistance is primarily dependent on the polymer tunnel resistivity,contact area,and tunneling size.The dimensions of the graphene nanosheets significantly influence the conductivity,which ranges from 0 S/m to90 S/m.An increased number of nanosheets in stacks and a larger gap between them enhance the nanocomposite's conductivity.Furthermore,the thicker interphase and smaller tunneling size can lead to higher sample conductivity due to their optimistic effects on the percolation threshold and network efficacy.展开更多
The combined prefabricated steel-hybrid stacked girder structure is very common in modern bridge design.An actual bridge engineering design project is taken as an example in this paper to analyze the application strat...The combined prefabricated steel-hybrid stacked girder structure is very common in modern bridge design.An actual bridge engineering design project is taken as an example in this paper to analyze the application strategy of this structure,encompassing overall design strategy,structural design strategy,and structural calculation strategy.The aim is to offer insights that can enhance the quality of bridge design.展开更多
基金supported by the PhD Start-up Fund of Science and Technology Department of Liaoning Province(No.2022-BS-306)the General Cultivation Scientific Research Project of Bohai University(No.0522xn058)the PhD Research Startup Foundation of Bohai University(No.0521bs021).
文摘The key to solve increasingly severe electromagnetic(EM)pollution is to explore sustainable,easily prepared,and cost-effective EM wave absorption materials with exceptional absorption capability.Herein,instead of anchoring on carbon materials in single layer,MoS_(2) flower-like microspheres were stacked on the surface of pomelo peels-derived porous carbon nanosheets(C)to fabricate MoS_(2)@C nanocomposites by a facile solvothermal process.EM wave absorption performances of MoS_(2)@C nanocomposites in X-band were systematically investigated,indicating the minimum reflection loss(RLmin)of-62.3 dB(thickness of 2.88 mm)and effective absorption bandwidth(EAB)almost covering the whole X-band(thickness of 2.63 mm)with the filler loading of only 20 wt.%.Superior EM wave absorption performances of MoS_(2)@C nanocomposites could be attributed to the excellent impedance matching characteristic and dielectric loss capacity(conduction loss and polarization loss).This study revealed that the as-prepared MoS_(2)@C nanocomposites would be a novel prospective candidate for the sustainable EM absorbents with superior EM wave absorption performances.
基金supported in part by the National Natural Science Foundation of China(No.51606213)the National Major Science and Technology Projects(No.J2019-III-0010-0054)。
文摘Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed under different icing conditions.Due to the complexity of the icing process,the rapid assessment of ice shape remains an important challenge.In this paper,an efficient prediction model of aircraft icing is established based on the deep belief network(DBN)and the stacked auto-encoder(SAE),which are all deep neural networks.The detailed network structures are designed and then the networks are trained according to the samples obtained by the icing numerical computation.After that the model is applied on the ice shape evaluation of NACA0012 airfoil.The results show that the model can accurately capture the nonlinear behavior of aircraft icing and thus make an excellent ice shape prediction.The model provides an important tool for aircraft icing analysis.
基金supported by the China Transgenic Organism Research and Commercialization Project (2016ZX08011-001)the National Natural Science Fund Project (31270579)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education, China (20130097130006)the 111 Project of China (B07030)
文摘Stacked（insect and herbicide resistant） transgenic rice T1c-19 with cry1C＊/bar genes, its receptor rice Minghui 63（herein MH63） and a local two-line hybrid indica rice Fengliangyou Xiang 1（used as a control） were compared for agronomic performance under field conditions without the relevant selection pressures. Agronomic traits（plant height, tiller number, and aboveground dry biomass）, reproductive ability（pollen viability, panicle length, and filled grain number of main panicles, seed set, and grain yield）, and weediness characteristics（seed shattering, seed overwintering ability, and volunteer seedling recruitment） were used to assess the potential weediness without selection pressure of stacked transgene rice T1c-19. In wet direct-seeded and transplanted rice fields, T1c-19 and its receptor MH63 performed similarly regarding vegetative growth and reproductive ability, but both of them were significantly inferior to the control. T1c-19 did not display weed characteristics; it had weak overwintering ability, low seed shattering and failed to establish volunteers. Exogenous insect and herbicide resistance genes did not confer competitive advantage to transgenic rice T1c-19 grown in the field without the relevant selection pressures.
基金the financial support of the Aeronautical Science Foundation of China(Nos.2013ZE52067,2014ZE52057)
文摘In aircraft assembly, interlayer burr formation in dry drilling of stacked metal materials is a common problem. Traditional manual deburring operation seriously affects the assembly qual- ity and assembly efficiency, is time-consuming and costly, and is not conducive to aircraft automatic assembly based on industrial robot. In this paper, the formation of drilling exit burr and the influ- ence of interlayer gap on interlayer burr formation were studied, and the mechanism of interlayer gap formation in drilling stacked aluminum alloy plates was investigated, a simplified mathematical model of interlayer gap based on the theory of plates and shells and finite element method was established. The relationship between interlayer gap and interlayer burr, as well as the effect of feed rate and pressing force on interlayer burr height and interlayer gap was discussed. The result shows that theoretical interlayer gap has a positive correlation with interlayer burr height and preloading nressing force is an effective method to control interlaver burr formation.
基金Project supported by the National Natural Science Foundation of China(Nos.61404001,61306046)the Anhui Province University Natural Science Research Major Project(No.KJ2014ZD12)+1 种基金the Huainan Science and Technology Program(No.2013A4011)the National Natural Science Foundation of China(No.61371025)
文摘This paper proposes a latch that can mitigate SEUs via an error detection circuit.The error detection circuit is hardened by a C-element and a stacked PMOS.In the hold state,a particle strikes the latch or the error detection circuit may cause a fault logic state of the circuit.The error detection circuit can detect the upset node in the latch and the fault output will be corrected.The upset node in the error detection circuit can be corrected by the C-element.The power dissipation and propagation delay of the proposed latch are analyzed by HSPICE simulations.The proposed latch consumes about 77.5%less energy and 33.1%less propagation delay than the triple modular redundancy（TMR）latch.Simulation results demonstrate that the proposed latch can mitigate SEU effectively.
基金supported by the National Natural Science Foundation of China(Grant Nos.51979253,51879245)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(Grant No.CUGCJ1821).
文摘Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.
基金supported by the Shanghai Rising-Star Program(No.21QC1400200)the Natural Science Foundation of Shanghai(No.21ZR1425400)the National Natural Science Foundation of China(No.52377111).
文摘This paper proposes a novel fault diagnosis method by fusing the information from multi-sensor signals to improve the reliability of the conventional vibration-based wind turbine drivetrain gearbox fault diagnosis methods.The method fully extracts fault features for variable speed,insufficient samples,and strong noise scenarios that may occur in the actual operation of a wind turbine planetary gearbox.First,multiple sensor signals are added to the diagnostic model,and multiple stacked denoising auto-encoders are designed and improved to extract the fault information.Then,a cycle reservoir with regular jumps is introduced to fuse multidimensional fault information and output diagnostic results in response to the insufficient ability to process fused information by the conventional Softmax classifier.In addition,the competitive swarm optimizer algorithm is introduced to address the challenge of obtaining the optimal combination of parameters in the network.Finally,the validation results show that the proposed method can increase fault diagnostic accuracy and improve robustness.
基金supported by GuangDong Basic and Applied Basic Research Foundation(2021A1515110215)Guangdong Academy of Sciences(2022GDASZH-2022010105)+2 种基金the National Science Foundation of China(42101239)the Guangzhou Basic Research Program(2022GZQN31)the Guangzhou Basic and Applied Research Project(202201010296).
文摘Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models(BEMs).The approach can be used to investigate patterns and processes of species richness.If data limitations on individual species distributions are inevitable,but how do they affect inferences of patterns and processes of species richness?We investigate the influence of different data sources on estimated species richness gradients in China.We fitted BEMs using species distributions data for 334 bird species obtained from(1)global range maps,(2)regional checklists,(3)museum records and surveys,and(4)citizen science data using presence-only(Mahalanobis distance),presence-background(MAXENT),and presence–absence(GAM and BRT)BEMs.Individual species predictions were stacked to generate species richness gradients.Here,we show that different data sources and BEMs can generate spatially varying gradients of species richness.The environmental predictors that best explained species distributions also differed between data sources.Models using citizen-based data had the highest accuracy,whereas those using range data had the lowest accuracy.Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty.When multiple data sets exist for the same region and taxa,we advise that explicit treatments of uncertainty,such as sensitivity analyses of the input data,should be conducted during the process of modeling.
基金supported in part by the Gansu Province Higher Education Institutions Industrial Support Program:Security Situational Awareness with Artificial Intelligence and Blockchain Technology.Project Number(2020C-29).
文摘In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.
基金This research project was funded by the Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,grant No(43-PRFA-P-58).
文摘This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout.The model,called Stacked Generalization for Channel Estimation(SGCE),aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput.The SGCE model incorporates six machine learning methods:random forest(RF),gradient boosting machine(GB),light gradient boosting machine(LGBM),support vector regression(SVR),extremely randomized tree(ERT),and extreme gradient boosting(XGB).By generating meta-data from five models(RF,GB,LGBM,SVR,and ERT),we ensure accurate channel coefficient predictions using the XGB model.To validate themodeling performance,we employ the leave-one-out cross-validation(LOOCV)approach,where each observation serves as the validation set while the remaining observations act as the training set.SGCE performances’results demonstrate higher mean andmedian accuracy compared to the separatedmodel.SGCE achieves an average accuracy of 98.4%,precision of 98.1%,and the highest F1-score of 98.5%,accurately predicting channel coefficients.Furthermore,our proposedmethod outperforms prior traditional and intelligent techniques in terms of throughput and bit error rate.SGCE’s superior performance highlights its efficacy in optimizing channel estimation.It can effectively predict channel coefficients and contribute to enhancing the overall efficiency of radio mobile systems.Through extensive experimentation and evaluation,we demonstrate that SGCE improved performance in channel estimation,surpassing previous techniques.Accordingly,SGCE’s capabilities have significant implications for optimizing channel estimation in modern communication systems.
基金the Basic Science Research Program through the National Research Foundation(NRF)of Korea funded by the Ministry of Education,Science,and Technology(No.2022R1A2C1004437)the Ministry of Science and ICT(MSIT)of Korea Government(No.2022M3J7A1062940)。
文摘In this study,the effects of stacked nanosheets and the surrounding interphase zone on the resistance of the contact region between nanosheets and the tunneling conductivity of samples are evaluated with developed equations superior to those previously reported.The contact resistance and nanocomposite conductivity are modeled by several influencing factors,including stack properties,interphase depth,tunneling size,and contact diameter.The developed model's accuracy is verified through numerous experimental measurements.To further validate the models and establish correlations between parameters,the effects of all the variables on contact resistance and nanocomposite conductivity are analyzed.Notably,the contact resistance is primarily dependent on the polymer tunnel resistivity,contact area,and tunneling size.The dimensions of the graphene nanosheets significantly influence the conductivity,which ranges from 0 S/m to90 S/m.An increased number of nanosheets in stacks and a larger gap between them enhance the nanocomposite's conductivity.Furthermore,the thicker interphase and smaller tunneling size can lead to higher sample conductivity due to their optimistic effects on the percolation threshold and network efficacy.
文摘The combined prefabricated steel-hybrid stacked girder structure is very common in modern bridge design.An actual bridge engineering design project is taken as an example in this paper to analyze the application strategy of this structure,encompassing overall design strategy,structural design strategy,and structural calculation strategy.The aim is to offer insights that can enhance the quality of bridge design.
