Quadruped robots with body joints exhibit enhanced mobility,however,in outdoor environments,the energy that the robot can carry is limited,necessitating optimization of energy consumption to accomplish more tasks with...Quadruped robots with body joints exhibit enhanced mobility,however,in outdoor environments,the energy that the robot can carry is limited,necessitating optimization of energy consumption to accomplish more tasks within these constraints.Inspired by quadruped animals,this paper proposes an energy-saving strategy for a body joint quadruped robot based on Central Pattern Generator(CPG)with multi-sensor fusion bio-reflexes.First,an energy consumption model for the robot is established,and energy characteristic tests are conducted under different gait parameters.Based on these energy characteristics,optimal energy-efficient gait parameters are determined for various environmental conditions.Second,biological reflex mechanisms are studied,and a motion control model based on multi-sensor fusion biological reflexes is established using CPG as the foundation.By integrating the reflex model and gait parameters,real-time adaptive adjustments to the robot’s motion gait are achieved on complex terrains,reducing energy loss caused by terrain disturbances.Finally,a prototype of the body joint quadruped robot is built for experimental verification.Simulation and experimental results demonstrate that the proposed algorithm effectively reduces the robot’s Cost of Transport(COT)and significantly improves energy efficiency.The related research results can provide a useful reference for the research on energy efficiency of quadruped robots on complex terrain.展开更多
Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operati...Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.展开更多
With the intelligent upgrading of manufacturing equipment,achieving high-precision and efficient fault diagnosis is essential to enhance equipment stability and increase productivity.Online monitoring and fault diagno...With the intelligent upgrading of manufacturing equipment,achieving high-precision and efficient fault diagnosis is essential to enhance equipment stability and increase productivity.Online monitoring and fault diagnosis technology play a critical role in improving the stability of metal additive manufacturing equipment.However,the limited proportion of fault data during operation challenges the accuracy and efficiency of multi-classification models due to excessive redundant data.A multi-sensor and principal component analysis(PCA)and support vector machine(SVM)asymptotic classification(PCSV)for additive manufacturing fault diagnosis method is proposed,and it divides the fault diagnosis into two steps.In the first step,real-time data are evaluated using the T2 and Q statistical parameters of the PCAmodel to identify potential faults while filtering non-fault data,thereby reducing redundancy and enhancing real-time efficiency.In the second step,the identified fault data are input into the SVM model for precise multi-class classification of fault categories.The PCSV method advances the field by significantly improving diagnostic accuracy and efficiency,achieving an accuracy of 99%,a diagnosis time of 0.65 s,and a training time of 503 s.The experimental results demonstrate the sophistication of the PCSV method for high-precision and high-efficiency fault diagnosis of small fault samples.展开更多
Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joint...Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joints the P-wave first arrival data. This method adaptively adjusts the preference for “superior” arrays and leverages “inferior” arrays to escape local optima, thereby improving the location accuracy. The effectiveness and stability of this method were validated through synthetic tests, pencil-lead break (PLB) experiments, and mining engineering applications. Specifically, for synthetic tests with 1 μs Gaussian noise and 100 μs large noise in rock samples, the location error of the multi-sensor arrays jointed location method is only 0.30 cm, which improves location accuracy by 97.51% compared to that using a single sensor array. The average location error of PLB events on three surfaces of a rock sample is reduced by 48.95%, 26.40%, and 55.84%, respectively. For mine blast event tests, the average location error of the dual sensor arrays jointed method is 62.74 m, 54.32% and 14.29% lower than that using only sensor arrays 1 and 2, respectively. In summary, the proposed multi-sensor arrays jointed location method demonstrates good noise resistance, stability, and accuracy, providing a compelling new solution for MS location in relevant mining scenarios.展开更多
Fused silica(SiO_(2)glass),a key amorphous component of Earth’s silicate minerals,undergoes coordination and phase transformations under high pressure.Although extensive studies have been conducted,discrepancies betw...Fused silica(SiO_(2)glass),a key amorphous component of Earth’s silicate minerals,undergoes coordination and phase transformations under high pressure.Although extensive studies have been conducted,discrepancies between theoretical and experimental studies remain,particularly regarding strain rate effects during compression.Here,we examine strain rate influences on the shock-induced amorphous–amorphous phase transitions in fused silica by measuring its Hugoniot equation of state and longitudinal sound velocity(CL)up to 7 GPa at strain rates of 10^(6)–10^(7)s^(-1)using a one-stage light-gas gun.A discontinuity in the relationship between shock velocity(US)and particle velocity(UP)and a significant softening in C_(L)of fused silica were observed near~5 GPa under shock loading.Our results indicate that high strain rates restrict Si–O–Si rotation in fused silica,modifying their bonds and increasing silicon coordination.The transition pressure by shock compression is significantly higher than that under static high-pressure conditions(2–3 GPa),which agrees with some recent theoretical predictions with high compression rates,reflecting the greater pressure needed to overcome energy barriers with the strain rate increase.These findings offer insights into strain rate-dependent phase transitions in fused silica and other silicate minerals(e.g.,quartz,olivine,and forsterite),bridging gaps between theoretical simulations and experiments.展开更多
Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhan...Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving.展开更多
The use of additive manufacturing techniques in the development of unconventional materials can help reduce the environmental impact of traditional construction materials.In this paper,the properties of a 3D-printed b...The use of additive manufacturing techniques in the development of unconventional materials can help reduce the environmental impact of traditional construction materials.In this paper,the properties of a 3D-printed biocomposite were evaluated.Biofilaments obtained by mixing pulverized bamboo fibers with polylactic acid(PLA)resin were extruded during the manufacturing process.To assess the effect of incorporating plant fibers,an analysis was conducted on the morphology,elemental chemical composition,crystallinity index,principal functional groups,thermal stability,surface roughness,microhardness,density,tensile strength,elastic modulus,and strain percentage of reinforced samples.The results were comparedwith those obtained from the characterization of standard PLAfilaments(unreinforced).The fused deposition modeling(FDM)technique was employed to print biocomposite specimens.Additionally,the influence of the printing parameters(infill density,build orientation,and layer thickness)on the physical,tribological,andmechanical properties of the biocomposites was analyzed.These results were compared with those obtained for specimens printed with pure PLA.The findings indicate that incorporating 10%vegetable filler into PLA filaments enhanced the strength and stiffness of the biocomposite under axial loads.Finally,the strength of the biocomposite subjected to axial loads was compared with the standardized values for wood-plastic composites,demonstrating the feasibility of its use for non-structural purposes in civil construction.展开更多
Transparent sand is a special material to realize visualization of concealed work in geotechnical engineering. To investigate the dynamic characteristics of transparent sand, a series of undrained cyclic simple shear ...Transparent sand is a special material to realize visualization of concealed work in geotechnical engineering. To investigate the dynamic characteristics of transparent sand, a series of undrained cyclic simple shear tests were conducted on the saturated transparent sand composed of fused quartz and refractive index-matched oil mixture. The results reveal that an increase in the initial shear stress ratio significantly affects the shape of the hysteresis loop, particularly resulting in more pronounced asymmetrical accumulation. Factors such as lower relative density, higher cyclic stress ratios and higher initial shear stress ratio have been shown to accelerate cyclic deformation, cyclic pore water pressure and stiffness degradation. The cyclic liquefaction resistance curves decrease as the initial shear stress ratio increases or as relative density decreases. Booker model and power law function model were applied to predict the pore water pressure for transparent sand. Both models yielded excellent fits for their respective condition, indicating a similar dynamic liquefaction pattern to that of natural sands. Finally, transparent sand displays similar dynamic characteristics in terms of cyclic liquefaction resistance and Kα correction factor. These comparisons indicate that transparent sand can serve as an effective means to mimic many natural sands in dynamic model tests.展开更多
Intrusion detection(ID)is a cyber security practice that encompasses the process of monitoring network activities to identify unauthorized or malicious actions.This includes problems like the difficulties of existing ...Intrusion detection(ID)is a cyber security practice that encompasses the process of monitoring network activities to identify unauthorized or malicious actions.This includes problems like the difficulties of existing intrusion detection models to identify emerging attacks,generating many false alarms,and their inability and difficulty to adapt themselves with time when it comes to threats,hence to overcome all those existing challenges in this research develop a Prairie Araneida optimization based fused Convolutional Neural Network model(PAO-CNN)for intrusion detection.The fused CNN(Convolutional Neural Netowrk)is a remarkable development since it combines statistical features that are extracted from the processed data and provide enhanced capabilities for the model to capture complicated patterns existing in intrusion datasets.The adoption of a fused architecture represents an integrated way towards intrusion detection where the model can significantly interpret various features to achieve higher accuracy.On top of this,the Prairie Araneida stage which is based on coyote behavior and social spider colonies respectively plays a role in enabling to handling of intricate optimization landscapes.The dual contribution of a fused CNN and novel optimization strategies strengthens the research’s goal to design an effective intrusion detection system that can evolve with new cyber threats.When the Training percentage(TP)is set to 90,the model’s performance can be assessed using metrics like Accuracy,Sensitivity,and Specificity.In this particular dataset,these metrics reach approximately 97.78%,96.25%,and 96.15%,respectively,which are crucial values.Additionally,when using a k-fold value of 6,the model achieves metrics of 97.04%Accuracy,97.37%Sensitivity,and 96.48%Specificity.展开更多
Acrylonitrile–butadiene–styrene(ABS)is the main material used in fused deposition modeling(FDM),which has good toughness and strength,but the single ABS material has poor heat resistance,which tends to cause warping...Acrylonitrile–butadiene–styrene(ABS)is the main material used in fused deposition modeling(FDM),which has good toughness and strength,but the single ABS material has poor heat resistance,which tends to cause warping and deformation during the printing process.Polycarbonate(PC)exhibits good performance in heat resistance,allowing it to maintain stable performance at higher temperatures.In this work,PC was used as a blending modifier to prepare five kinds of ABS/PC composite filaments,and the mechanical and thermal properties of the ABS/PC(Acrylonitrile-butadiene-styrene/Polycarbonate)composite filaments were studied and analyzed.Results showed that the glass transition temperature(Tg)of the blend increased continuously as the PC content increased.When the mass fraction of ABS/PC was 50/50,the glass transition temperature of the blend increased by 21.21%,and the tensile strength of the composites reached 40.23 MPa,which was an increase of 36.47% compared with that of pure ABS.However,the impact strength of the composites decreased with the addition of PC.Moreover,with the increase in the mass fraction of PC,the printing accuracy error increased from 0.02 to 0.18 mm.The results of this paper will promote the improvement of ABS printing performance and enrich the available materials for FDM.展开更多
Fluorinated fused azobenzene boron(FBAz)is a novel electron-deficient building block for polymer electron acceptors in all-polymer solar cells(all-PSC).The B←N bridging units impart a fixed configuration and low-lyin...Fluorinated fused azobenzene boron(FBAz)is a novel electron-deficient building block for polymer electron acceptors in all-polymer solar cells(all-PSC).The B←N bridging units impart a fixed configuration and low-lying LUMO/HOMO energy.Three polymer acceptor materials(P2f,P3f and P5f)with different fluorine substitution positions by copolymerizing FBAz with indacenodithiophene(IDT),are synthesized and investigated to study the influence of fluorinated forms on the all-polymer solar cell performance.The FBAz units are synthesized in just three steps,facilitating the straightforward production of polymer acceptors P2f,P3f,and P5f.These acceptors exhibit strong light absorption in the visible to near-infrared range of 500-1000nm and possess suitable LUMO/HOMO energy levels of-3.99/-5.66 eV which are very complementary to that(E_(LUMO/HOMO)=-3.59/-5.20 eV)of the widely-used polymer donor poly[(ethylhexylthiophenyl)-benzodithiophene-(ethylhexyl)-thienothiophene](PTB7-Th).All-polymer solar cells(all-PSCs)with PTB7-Th as electron donor and P3f as electron acceptor exhibits highest power conversion efficiencies(PCE)2.70%.When PC_(61)BM is added as the third component,the device efficiency can reach 5.36%.These preliminary results indicate that FBAz is a promising strong electron acceptor for the development of n-type polymer semiconductors,especially in organic photovoltaics(OPVs).To the best of our knowledge,this is the first example demonstrating the unique photovoltaic properties of the N=N double bond as an acceptor material.展开更多
We demonstrate a new polarization smoothing(PS)approach utilizing residual stress birefringence in fused silica to create a spatially random polarization control plate(SRPCP),thereby improving target illumination unif...We demonstrate a new polarization smoothing(PS)approach utilizing residual stress birefringence in fused silica to create a spatially random polarization control plate(SRPCP),thereby improving target illumination uniformity in inertial confinement fusion(ICF)laser systems.The fundamental operating mechanism and key fabrication techniques for the SRPCP are systematically developed and experimentally validated.The SRPCP converts a linearly polarized 3ω incident laser beam into an output beam with a spatially randomized polarization distribution.When combined with a continuous phase plate,the SRPCP effectively suppresses high-intensity speckles at all spatial frequencies in the focal spot.The proposed PS technique is specifically designed for high-fluence large-aperture laser systems,enabling novel polarization control regimes in laser-driven ICF.展开更多
基金supported by the National Natural Science Foundation of China(Grant no.52075488)the Natural Science Foundation of Zhejiang Province(LY20E050023).
文摘Quadruped robots with body joints exhibit enhanced mobility,however,in outdoor environments,the energy that the robot can carry is limited,necessitating optimization of energy consumption to accomplish more tasks within these constraints.Inspired by quadruped animals,this paper proposes an energy-saving strategy for a body joint quadruped robot based on Central Pattern Generator(CPG)with multi-sensor fusion bio-reflexes.First,an energy consumption model for the robot is established,and energy characteristic tests are conducted under different gait parameters.Based on these energy characteristics,optimal energy-efficient gait parameters are determined for various environmental conditions.Second,biological reflex mechanisms are studied,and a motion control model based on multi-sensor fusion biological reflexes is established using CPG as the foundation.By integrating the reflex model and gait parameters,real-time adaptive adjustments to the robot’s motion gait are achieved on complex terrains,reducing energy loss caused by terrain disturbances.Finally,a prototype of the body joint quadruped robot is built for experimental verification.Simulation and experimental results demonstrate that the proposed algorithm effectively reduces the robot’s Cost of Transport(COT)and significantly improves energy efficiency.The related research results can provide a useful reference for the research on energy efficiency of quadruped robots on complex terrain.
基金supported by the China Three Gorges Corporation(No.NBZZ202300860)the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.
基金supported in part by the National Key R&D Program of China Grant 2022YFB4602200.
文摘With the intelligent upgrading of manufacturing equipment,achieving high-precision and efficient fault diagnosis is essential to enhance equipment stability and increase productivity.Online monitoring and fault diagnosis technology play a critical role in improving the stability of metal additive manufacturing equipment.However,the limited proportion of fault data during operation challenges the accuracy and efficiency of multi-classification models due to excessive redundant data.A multi-sensor and principal component analysis(PCA)and support vector machine(SVM)asymptotic classification(PCSV)for additive manufacturing fault diagnosis method is proposed,and it divides the fault diagnosis into two steps.In the first step,real-time data are evaluated using the T2 and Q statistical parameters of the PCAmodel to identify potential faults while filtering non-fault data,thereby reducing redundancy and enhancing real-time efficiency.In the second step,the identified fault data are input into the SVM model for precise multi-class classification of fault categories.The PCSV method advances the field by significantly improving diagnostic accuracy and efficiency,achieving an accuracy of 99%,a diagnosis time of 0.65 s,and a training time of 503 s.The experimental results demonstrate the sophistication of the PCSV method for high-precision and high-efficiency fault diagnosis of small fault samples.
基金Project(SICGM2023301) supported by the State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology,ChinaProject(SMDPC202202) supported by the Key Laboratory of Mining Disaster Prevention and Control,ChinaProject(U21A2030) supported by the National Natural Science Foundation of China。
文摘Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joints the P-wave first arrival data. This method adaptively adjusts the preference for “superior” arrays and leverages “inferior” arrays to escape local optima, thereby improving the location accuracy. The effectiveness and stability of this method were validated through synthetic tests, pencil-lead break (PLB) experiments, and mining engineering applications. Specifically, for synthetic tests with 1 μs Gaussian noise and 100 μs large noise in rock samples, the location error of the multi-sensor arrays jointed location method is only 0.30 cm, which improves location accuracy by 97.51% compared to that using a single sensor array. The average location error of PLB events on three surfaces of a rock sample is reduced by 48.95%, 26.40%, and 55.84%, respectively. For mine blast event tests, the average location error of the dual sensor arrays jointed method is 62.74 m, 54.32% and 14.29% lower than that using only sensor arrays 1 and 2, respectively. In summary, the proposed multi-sensor arrays jointed location method demonstrates good noise resistance, stability, and accuracy, providing a compelling new solution for MS location in relevant mining scenarios.
基金supported by the National Natural Science Foundation of China(Grant Nos.42422201,12175211,and 12350710177)the Sichuan Science and Technology Program(Grant No.2023NSFSC1910).
文摘Fused silica(SiO_(2)glass),a key amorphous component of Earth’s silicate minerals,undergoes coordination and phase transformations under high pressure.Although extensive studies have been conducted,discrepancies between theoretical and experimental studies remain,particularly regarding strain rate effects during compression.Here,we examine strain rate influences on the shock-induced amorphous–amorphous phase transitions in fused silica by measuring its Hugoniot equation of state and longitudinal sound velocity(CL)up to 7 GPa at strain rates of 10^(6)–10^(7)s^(-1)using a one-stage light-gas gun.A discontinuity in the relationship between shock velocity(US)and particle velocity(UP)and a significant softening in C_(L)of fused silica were observed near~5 GPa under shock loading.Our results indicate that high strain rates restrict Si–O–Si rotation in fused silica,modifying their bonds and increasing silicon coordination.The transition pressure by shock compression is significantly higher than that under static high-pressure conditions(2–3 GPa),which agrees with some recent theoretical predictions with high compression rates,reflecting the greater pressure needed to overcome energy barriers with the strain rate increase.These findings offer insights into strain rate-dependent phase transitions in fused silica and other silicate minerals(e.g.,quartz,olivine,and forsterite),bridging gaps between theoretical simulations and experiments.
文摘Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving.
基金a derivative product of the project INV-ING-3788 financed by the Vicerectory of Research of the Universidad Militar Nueva Granada,validity 2023.
文摘The use of additive manufacturing techniques in the development of unconventional materials can help reduce the environmental impact of traditional construction materials.In this paper,the properties of a 3D-printed biocomposite were evaluated.Biofilaments obtained by mixing pulverized bamboo fibers with polylactic acid(PLA)resin were extruded during the manufacturing process.To assess the effect of incorporating plant fibers,an analysis was conducted on the morphology,elemental chemical composition,crystallinity index,principal functional groups,thermal stability,surface roughness,microhardness,density,tensile strength,elastic modulus,and strain percentage of reinforced samples.The results were comparedwith those obtained from the characterization of standard PLAfilaments(unreinforced).The fused deposition modeling(FDM)technique was employed to print biocomposite specimens.Additionally,the influence of the printing parameters(infill density,build orientation,and layer thickness)on the physical,tribological,andmechanical properties of the biocomposites was analyzed.These results were compared with those obtained for specimens printed with pure PLA.The findings indicate that incorporating 10%vegetable filler into PLA filaments enhanced the strength and stiffness of the biocomposite under axial loads.Finally,the strength of the biocomposite subjected to axial loads was compared with the standardized values for wood-plastic composites,demonstrating the feasibility of its use for non-structural purposes in civil construction.
基金Project(U2268213) supported by the National Natural Science Foundation of ChinaProject(2024YFHZ0121) supported by the Sichuan Science and Technology Program,China。
文摘Transparent sand is a special material to realize visualization of concealed work in geotechnical engineering. To investigate the dynamic characteristics of transparent sand, a series of undrained cyclic simple shear tests were conducted on the saturated transparent sand composed of fused quartz and refractive index-matched oil mixture. The results reveal that an increase in the initial shear stress ratio significantly affects the shape of the hysteresis loop, particularly resulting in more pronounced asymmetrical accumulation. Factors such as lower relative density, higher cyclic stress ratios and higher initial shear stress ratio have been shown to accelerate cyclic deformation, cyclic pore water pressure and stiffness degradation. The cyclic liquefaction resistance curves decrease as the initial shear stress ratio increases or as relative density decreases. Booker model and power law function model were applied to predict the pore water pressure for transparent sand. Both models yielded excellent fits for their respective condition, indicating a similar dynamic liquefaction pattern to that of natural sands. Finally, transparent sand displays similar dynamic characteristics in terms of cyclic liquefaction resistance and Kα correction factor. These comparisons indicate that transparent sand can serve as an effective means to mimic many natural sands in dynamic model tests.
文摘Intrusion detection(ID)is a cyber security practice that encompasses the process of monitoring network activities to identify unauthorized or malicious actions.This includes problems like the difficulties of existing intrusion detection models to identify emerging attacks,generating many false alarms,and their inability and difficulty to adapt themselves with time when it comes to threats,hence to overcome all those existing challenges in this research develop a Prairie Araneida optimization based fused Convolutional Neural Network model(PAO-CNN)for intrusion detection.The fused CNN(Convolutional Neural Netowrk)is a remarkable development since it combines statistical features that are extracted from the processed data and provide enhanced capabilities for the model to capture complicated patterns existing in intrusion datasets.The adoption of a fused architecture represents an integrated way towards intrusion detection where the model can significantly interpret various features to achieve higher accuracy.On top of this,the Prairie Araneida stage which is based on coyote behavior and social spider colonies respectively plays a role in enabling to handling of intricate optimization landscapes.The dual contribution of a fused CNN and novel optimization strategies strengthens the research’s goal to design an effective intrusion detection system that can evolve with new cyber threats.When the Training percentage(TP)is set to 90,the model’s performance can be assessed using metrics like Accuracy,Sensitivity,and Specificity.In this particular dataset,these metrics reach approximately 97.78%,96.25%,and 96.15%,respectively,which are crucial values.Additionally,when using a k-fold value of 6,the model achieves metrics of 97.04%Accuracy,97.37%Sensitivity,and 96.48%Specificity.
基金the Natural Science Foundation of Shandong Province(grant no.ZR2020KF024)Yantai City Science and Technology Plan Project(grant no.2022ZDCX016)+1 种基金the University Research Project of Shandong Province(grant no.J17KB007)Natural Science Foundation of Shandong Jiaotong University(grant no.Z201937).
文摘Acrylonitrile–butadiene–styrene(ABS)is the main material used in fused deposition modeling(FDM),which has good toughness and strength,but the single ABS material has poor heat resistance,which tends to cause warping and deformation during the printing process.Polycarbonate(PC)exhibits good performance in heat resistance,allowing it to maintain stable performance at higher temperatures.In this work,PC was used as a blending modifier to prepare five kinds of ABS/PC composite filaments,and the mechanical and thermal properties of the ABS/PC(Acrylonitrile-butadiene-styrene/Polycarbonate)composite filaments were studied and analyzed.Results showed that the glass transition temperature(Tg)of the blend increased continuously as the PC content increased.When the mass fraction of ABS/PC was 50/50,the glass transition temperature of the blend increased by 21.21%,and the tensile strength of the composites reached 40.23 MPa,which was an increase of 36.47% compared with that of pure ABS.However,the impact strength of the composites decreased with the addition of PC.Moreover,with the increase in the mass fraction of PC,the printing accuracy error increased from 0.02 to 0.18 mm.The results of this paper will promote the improvement of ABS printing performance and enrich the available materials for FDM.
基金supported by the National Natural Science Foundation of China(No.22375123)the Shuguang Program of Shanghai Education Development Foundation,the Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering(No.2022SXTD012)。
文摘Fluorinated fused azobenzene boron(FBAz)is a novel electron-deficient building block for polymer electron acceptors in all-polymer solar cells(all-PSC).The B←N bridging units impart a fixed configuration and low-lying LUMO/HOMO energy.Three polymer acceptor materials(P2f,P3f and P5f)with different fluorine substitution positions by copolymerizing FBAz with indacenodithiophene(IDT),are synthesized and investigated to study the influence of fluorinated forms on the all-polymer solar cell performance.The FBAz units are synthesized in just three steps,facilitating the straightforward production of polymer acceptors P2f,P3f,and P5f.These acceptors exhibit strong light absorption in the visible to near-infrared range of 500-1000nm and possess suitable LUMO/HOMO energy levels of-3.99/-5.66 eV which are very complementary to that(E_(LUMO/HOMO)=-3.59/-5.20 eV)of the widely-used polymer donor poly[(ethylhexylthiophenyl)-benzodithiophene-(ethylhexyl)-thienothiophene](PTB7-Th).All-polymer solar cells(all-PSCs)with PTB7-Th as electron donor and P3f as electron acceptor exhibits highest power conversion efficiencies(PCE)2.70%.When PC_(61)BM is added as the third component,the device efficiency can reach 5.36%.These preliminary results indicate that FBAz is a promising strong electron acceptor for the development of n-type polymer semiconductors,especially in organic photovoltaics(OPVs).To the best of our knowledge,this is the first example demonstrating the unique photovoltaic properties of the N=N double bond as an acceptor material.
基金supported by the National Natural Science Foundation of China(Grant No.62275235).
文摘We demonstrate a new polarization smoothing(PS)approach utilizing residual stress birefringence in fused silica to create a spatially random polarization control plate(SRPCP),thereby improving target illumination uniformity in inertial confinement fusion(ICF)laser systems.The fundamental operating mechanism and key fabrication techniques for the SRPCP are systematically developed and experimentally validated.The SRPCP converts a linearly polarized 3ω incident laser beam into an output beam with a spatially randomized polarization distribution.When combined with a continuous phase plate,the SRPCP effectively suppresses high-intensity speckles at all spatial frequencies in the focal spot.The proposed PS technique is specifically designed for high-fluence large-aperture laser systems,enabling novel polarization control regimes in laser-driven ICF.
