The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment innovation.Insulating material,as the core of electrical power equipm...The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment innovation.Insulating material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and opportunities.The goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first discussed.The engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are investigated.Insulating materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being discussed.In the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material level.It is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality rules.The authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.展开更多
Understanding the net primary productivity(NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the glo...Understanding the net primary productivity(NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the globe. Then, we used the Carnegie-Ames-Stanford Approach(CASA) model to estimate global grassland NPP and explore the spatio-temporal variations of grassland NPP in response to climate change from 1982 to 2008. Results showed that the largest area of grassland distribution during the study period was in Asia(1737.23 × 104 km^2), while the grassland area in Europe was relatively small(202.83 × 10~4 km^2). Temporally, the total NPP increased with fluctuations from 1982 to 2008, with an annual increase rate of 0.03 Pg C/yr. The total NPP experienced a significant increasing trend from 1982 to 1995, while a decreasing trend was observed from 1996 to 2008. Spatially, the grassland NPP in South America and Africa were higher than the other regions, largely as a result of these regions are under warm and wet climatic conditions. The highest mean NPP was recorded for savannas(560.10 g C/(m^2·yr)), whereas the lowest was observed in open shrublands with an average NPP of 162.53 g C/(m^2·yr). The relationship between grassland NPP and annual mean temperature and annual precipitation(AMT, AP, respectively) varies with changes in AP, which indicates that, grassland NPP is more sensitive to precipitation than temperature.展开更多
Flexible electrohydrodynamic(EHD)pumps have been developed and applied in many fields due to no transmission structure,no wear,easy manipulation,and no noise.Physical simulation is often used to predict the output per...Flexible electrohydrodynamic(EHD)pumps have been developed and applied in many fields due to no transmission structure,no wear,easy manipulation,and no noise.Physical simulation is often used to predict the output performance of flexible EHD pumps.However,this method neglects fluid–solid interaction and energy loss caused by flexible materials,which are both difficult to calculate when the flexible pumps deform.Therefore,this study proposes a flexible pump output performance prediction using machine learning algorithms.We used three different types of machine learning:random forest regression,ridge regression,and neural network to predict the critical parameters(pressure,flow rate,and power)of the flexible EHD pump.Voltage,angle,gap,overlap,and channel height are selected as five input data of the neural network.In addition,we optimized essential parameters in the three networks.Finally,we adopt the best predictive model and evaluate the significance of five input parameters to the output performance of the flexible EHD pumps.Among the three methods,the MLP model has exceptionally high accuracy in predicting pressure and flow.Our work is beneficial for the design process of fluid sources in flexible soft actuators and soft hydraulic sources in microfluidic chips.展开更多
Monitoring the operational state of sensors promptly and the accurate diagnosis of faults are essential. This paper proposes an improved fault diagnosis scheme for sensors, which includes both fault detection and faul...Monitoring the operational state of sensors promptly and the accurate diagnosis of faults are essential. This paper proposes an improved fault diagnosis scheme for sensors, which includes both fault detection and fault identification. Firstly, trend analysis combined with least squares support vector machine（TA-LSSVM） method is proposed and implemented to detect faults. Secondly, an improved error correcting output coding-support vector machine（ECOC-SVM） based fault identification method is proposed to distinguish different sensor failure modes. To demonstrate the effectiveness of the proposed scheme, experiments are conducted with an MTi-series sensor, and some comparisons are made with other fault identification methods. The experimental results demonstrate that the proposed fault diagnosis scheme offers an essential improvement with detection real-time property and better identification accuracy.展开更多
The emergence of electronic devices has brought earth-shaking changes to people's life.However,an extemal power source may become indispensable to the electronie devices due to the limited capacity of batteries.As...The emergence of electronic devices has brought earth-shaking changes to people's life.However,an extemal power source may become indispensable to the electronie devices due to the limited capacity of batteries.As one of the possible solutions for the extermal power sources,the triboelectric nanogenerator(TENG)provides a novel idea to the increasing mumber of personal electronic devices.TENG is a new type of energy ollector,which has become a hot spot in the field of nanotechnology.It is widely used at the acquisition and conversion of mechan-ical enegy to electrice energy through the principle of electrostatic induction.On this basis,the TENG could be integrated with the energy stonage system into a self-powered system,W hich can supply power to the electronic devices and make them work continuously.In this review,TENG's basic structure as well as its working process and working mode are firstly discussed.The integration method of TENGs with enegy storage systems and the related research status are then introduced in detail.At the end of this paper,we put forward some problems and discuss the prospect in the future.展开更多
A novel image fusion algorithm based on bandelet transform is proposed. Bandelet transform can take advantage of the geometrical regularity of image structure and represent sharp image transitions such as edges effici...A novel image fusion algorithm based on bandelet transform is proposed. Bandelet transform can take advantage of the geometrical regularity of image structure and represent sharp image transitions such as edges efficiently in image fusion. For reconstructing the fused image, the maximum rule is used to select source images＇ geometric flow and bandelet coefficients. Experimental results indicate that the bandelet-based fusion algorithm represents the edge and detailed information well and outperforms the wavelet-based and Laplacian pyramid-based fusion algorithms, especially when the abundant texture and edges are contained in the source images.展开更多
Aerosol ammonium(NH_(4)^(+)),mainly produced from the reactions of ammonia(NH_(3))with acids in the atmosphere,has significant impacts on air pollution,radiative forcing,and human health.Understanding the source and f...Aerosol ammonium(NH_(4)^(+)),mainly produced from the reactions of ammonia(NH_(3))with acids in the atmosphere,has significant impacts on air pollution,radiative forcing,and human health.Understanding the source and formation mechanism of NH_(4)^(+)can provide scientific insights into air quality improvements.However,the sources of NH_(3)in urban areas are not well understood,and few studies focus on NH_(3)/NH_(4)^(+)at different heights within the atmospheric boundary layer,which hinders a comprehensive understanding of aerosol NH_(4)^(+).In this study,we perform both field observation and modeling studies(the Community Multiscale Air Quality,CMAQ)to investigate regional NH_(3)emission sources and vertically resolved NH_(4)^(+)formation mechanisms during the winter in Beijing.Both stable nitrogen isotope analyses and CMAQ model suggest that combustion-related NH_(3)emissions,including fossil fuel sources,NH_(3)slip,and biomass burning,are important sources of aerosol NH_(4)^(+)with more than 60%contribution occurring on heavily polluted days.In contrast,volatilization-related NH_(3)sources(livestock breeding,N-fertilizer application,and human waste)are dominant on clean days.Combustion-related NH_(3)is mostly local from Beijing,and biomass burning is likely an important NH_(3)source(~15%–20%)that was previously overlooked.More effective control strategies such as the two-product(e.g.,reducing both SO_(2)and NH_(3))control policy should be considered to improve air quality.展开更多
Surface flashover of spacers is a key factor limiting the application of HVDC GIS/GIL,while the charge accumula-tion on the surface of the spacer could have a potential adverse effect on the surface flashover voltage....Surface flashover of spacers is a key factor limiting the application of HVDC GIS/GIL,while the charge accumula-tion on the surface of the spacer could have a potential adverse effect on the surface flashover voltage.This paper discusses the laws regarding distribution patterns of surface charges and the related mechanisms.The field-dependent property is discussed in detail to comprehensively illustrate the charge transport mecha-nism and explain the research differences regarding different surface charge patterns obtained by previous researchers.In addition,the main surface charge control methods for epoxy resin are summarized and discussed.The potential research directions of charge control methods and key points in manufacturing of spacers used in HVDC GIS/GIL are also explored.展开更多
A flexible,multi-site tactile and thermal sensor(MTTS)based on polyvinylidene fluoride(resolution 50×50)is reported.It can be used to implement spatial mapping caused by tactile and thermal events and record the ...A flexible,multi-site tactile and thermal sensor(MTTS)based on polyvinylidene fluoride(resolution 50×50)is reported.It can be used to implement spatial mapping caused by tactile and thermal events and record the two-dimensional motion trajectory of a tracked target object.The output voltage and current signal are recorded as amapping by sensing the external pressure and thermal radiation stimulus,and the response distribution is dynamically observed on the three-dimensional interface.Through the mapping relationship between the established piezoelectric and pyroelectric signals,the piezoelectric component and the pyroelectric component are effectively extracted from the composite signals.The MTTS has a good sensitivity for tactile and thermal detection,and the electrodes have good synchronism.In addition,the signal interference is less than 9.5%and decreases as the pressure decreases after the distance between adjacent sites exceeds 200μm.The integration of MTTS and signal processing units has potential applications in human-machine interaction systems,health status detection and smart assistive devices.展开更多
In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion ...In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion module is studied.Furthermore,a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking algorithm.For the problem of object association failure caused by UAV movement,image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking algorithm.The simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform,and effectively solve the problem of association failure caused by UAV movement.展开更多
When learning the structure of a Bayesian network,the search space expands significantly as the network size and the number of nodes increase,leading to a noticeable decrease in algorithm efficiency.Traditional constr...When learning the structure of a Bayesian network,the search space expands significantly as the network size and the number of nodes increase,leading to a noticeable decrease in algorithm efficiency.Traditional constraint-based methods typically rely on the results of conditional independence tests.However,excessive reliance on these test results can lead to a series of problems,including increased computational complexity and inaccurate results,especially when dealing with large-scale networks where performance bottlenecks are particularly evident.To overcome these challenges,we propose a Markov blanket discovery algorithm based on constrained local neighborhoods for constructing undirected independence graphs.This method uses the Markov blanket discovery algorithm to refine the constraints in the initial search space,sets an appropriate constraint radius,thereby reducing the initial computational cost of the algorithm and effectively narrowing the initial solution range.Specifically,the method first determines the local neighborhood space to limit the search range,thereby reducing the number of possible graph structures that need to be considered.This process not only improves the accuracy of the search space constraints but also significantly reduces the number of conditional independence tests.By performing conditional independence tests within the local neighborhood of each node,the method avoids comprehensive tests across the entire network,greatly reducing computational complexity.At the same time,the setting of the constraint radius further improves computational efficiency while ensuring accuracy.Compared to other algorithms,this method can quickly and efficiently construct undirected independence graphs while maintaining high accuracy.Experimental simulation results show that,this method has significant advantages in obtaining the structure of undirected independence graphs,not only maintaining an accuracy of over 96%but also reducing the number of conditional independence tests by at least 50%.This sig展开更多
Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking systems.However,the complex background,the strong nois...Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking systems.However,the complex background,the strong noise,and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets.A multi-channel based on attention network is proposed in this paper,aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model,high complexity and poor detection performance of deep learning algorithms.First,given the difficulty in extracting the features of infrared multiscale and small dim targets,the multiple channels are designed based on dilated convolution to capture multiscale target features.Second,the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target features.In addition,the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion block.Finally,it is verified that,compared with other state-of-the-art methods based on the datasets SIRST and MDFA,the proposed algorithm further improves the detection effect,and the model size and computational complexity are smaller.展开更多
Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory.In this paper,we propose a lear...Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory.In this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations.We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation.This tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual domains.Furthermore,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation.Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures.Comprehensive validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.展开更多
Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from ...Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from the perspectives of network update strategy,initialization method,and parameter selection.This paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic algorithms.Simulations show that compared with existing SOM networks,the improved SOM network proposed in this paper improves the convergence rate and algorithm accuracy.Compared with iterated local search and heuristic algorithms,the improved SOM net-work algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP.展开更多
This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines...This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.展开更多
The Coronavirus Disease(COVID-19)pandemic has exposed the vulnerabilities of medical services across the globe,especially in underdeveloped nations.In the aftermath of the COVID-19 outbreak,a strong demand exists for ...The Coronavirus Disease(COVID-19)pandemic has exposed the vulnerabilities of medical services across the globe,especially in underdeveloped nations.In the aftermath of the COVID-19 outbreak,a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods.Medical imaging has become a crucial component in the disease diagnosis process,whereas X-rays and Computed Tomography(CT)scan imaging are employed in a deep network to diagnose the diseases.In general,four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks,such as network training,feature extraction,model performance testing and optimal feature selection.The current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion(CFPADLDF)approach for detecting and classifying COVID-19.The presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical images.Initially,the proposed CFPA-DLDF technique employs the Gabor Filtering(GF)approach to pre-process the input images.In addition,a weighted voting-based ensemble model is employed for feature extraction,in which both VGG-19 and the MixNet models are included.Finally,the CFPA with Recurrent Neural Network(RNN)model is utilized for classification,showing the work’s novelty.A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model,and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches.展开更多
An analytical method,called the symplectic mathematical method,is proposed to study the wave propagation in a spring-mass chain with gradient arranged local resonators and nonlinear ground springs.Combined with the li...An analytical method,called the symplectic mathematical method,is proposed to study the wave propagation in a spring-mass chain with gradient arranged local resonators and nonlinear ground springs.Combined with the linearized perturbation approach,the symplectic transform matrix for a unit cell of the weakly nonlinear graded metamaterial is derived,which only relies on the state vector.The results of the dispersion relation obtained with the symplectic mathematical method agree well with those achieved by the Bloch theory.It is shown that wider and lower frequency bandgaps are formed when the hardening nonlinearity and incident wave intensity increase.Subsequently,the displacement response and transmission performance of nonlinear graded metamaterials with finite length are studied.The dual tunable effects of nonlinearity and gradation on the wave propagation are explored under different excitation frequencies.For small excitation frequencies,the gradient parameter plays a dominant role compared with the nonlinearity.The reason is that the gradient tuning aims at the gradient arrangement of local resonators,which is limited by the critical value of the local resonator mass.In contrast,for larger excitation frequencies,the hardening nonlinearity is dominant and will contribute to the formation of a new bandgap.展开更多
Robotics has aroused huge attention since the 1950s.Irrespective of the uniqueness that industrial applications exhibit,conventional rigid robots have displayed noticeable limitations,particularly in safe cooperation ...Robotics has aroused huge attention since the 1950s.Irrespective of the uniqueness that industrial applications exhibit,conventional rigid robots have displayed noticeable limitations,particularly in safe cooperation as well as with environmental adaption.Accordingly,scientists have shifted their focus on soft robotics to apply this type of robots more effectively in unstructured environments.For decades,they have been committed to exploring sub-fields of soft robotics(e.g.,cutting-edge techniques in design and fabrication,accurate modeling,as well as advanced control algorithms).Although scientists have made many different efforts,they share the common goal of enhancing applicability.The presented paper aims to brief the progress of soft robotic research for readers interested in this field,and clarify how an appropriate control algorithm can be produced for soft robots with specific morphologies.This paper,instead of enumerating existing modeling or control methods of a certain soft robot prototype,interprets for the relationship between morphology and morphology-dependent motion strategy,attempts to delve into the common issues in a particular class of soft robots,and elucidates a generic solution to enhance their performance.展开更多
It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on gree...It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction,air quality improvement,and improved health.In the context of carbon peak,carbon neutrality,and clean air policies,this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 indicators.The 18 indicators cover the following five aspects:air pollution and associated weather-climate conditions,progress in structural transition,sources,inks,and mitigation pathway of atmospheric composition,health impacts and benefits of coordinated control,and synergetic governance system and practices.By tracking the progress in each indicator,this perspective presents the major accomplishment of coordinated control,identifies the emerging challenges toward the synergetic governance,and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.展开更多
Convolution Neural Networks(CNN)can quickly diagnose COVID-19 patients by analyzing computed tomography(CT)images of the lung,thereby effectively preventing the spread of COVID-19.However,the existing CNN-based COVID-...Convolution Neural Networks(CNN)can quickly diagnose COVID-19 patients by analyzing computed tomography(CT)images of the lung,thereby effectively preventing the spread of COVID-19.However,the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population.Which reduces the model’s classification sensitivity,resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people.To solve the problem,this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image classification,combining softmax loss to design a jointly supervised metric loss function COVID Triplet-Center Loss(COVID-TCL).Triplet loss can increase inter-class discreteness,and center loss can improve intra-class compactness.Therefore,COVID-TCL can help the CNN-based model to extract more discriminative features and strengthen the diagnostic capacity of COVID-19 patients in the early stage and incubation period.Meanwhile,we use the extreme gradient boosting(XGBoost)as a classifier to design a COVID-19 images classification model of CNN-XGBoost architecture,to further improve the CNN-based model’s classification effect and operation efficiency.The experiment shows that the classification accuracy of the model proposed in this paper is 97.41%,and the sensitivity is 97.61%,which is higher than the other 7 reference models.The COVID-TCL can effectively improve the classification sensitivity of the CNN-based model,the CNN-XGBoost architecture can further improve the CNN-based model’s classification effect.展开更多
文摘The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment innovation.Insulating material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and opportunities.The goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first discussed.The engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are investigated.Insulating materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being discussed.In the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material level.It is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality rules.The authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.
基金Under the auspices of Asia Pacific Network for Global Change Research(APN)Global Change Fund Project(No.ARCP2015-03CMY-Li)+2 种基金National Natural Science Foundation of China(No.41271361,41501575)National Key Research and Development Project(No.2018YFD0800201)Key Project of Chinese National Programs for Fundamental Research and Development(No.2010CB950702)
文摘Understanding the net primary productivity(NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the globe. Then, we used the Carnegie-Ames-Stanford Approach(CASA) model to estimate global grassland NPP and explore the spatio-temporal variations of grassland NPP in response to climate change from 1982 to 2008. Results showed that the largest area of grassland distribution during the study period was in Asia(1737.23 × 104 km^2), while the grassland area in Europe was relatively small(202.83 × 10~4 km^2). Temporally, the total NPP increased with fluctuations from 1982 to 2008, with an annual increase rate of 0.03 Pg C/yr. The total NPP experienced a significant increasing trend from 1982 to 1995, while a decreasing trend was observed from 1996 to 2008. Spatially, the grassland NPP in South America and Africa were higher than the other regions, largely as a result of these regions are under warm and wet climatic conditions. The highest mean NPP was recorded for savannas(560.10 g C/(m^2·yr)), whereas the lowest was observed in open shrublands with an average NPP of 162.53 g C/(m^2·yr). The relationship between grassland NPP and annual mean temperature and annual precipitation(AMT, AP, respectively) varies with changes in AP, which indicates that, grassland NPP is more sensitive to precipitation than temperature.
基金supported by Grant-in-Aid for Early-Career Scientists from the Japan Society for the Promotion of Science(23K13290),Japan.
文摘Flexible electrohydrodynamic(EHD)pumps have been developed and applied in many fields due to no transmission structure,no wear,easy manipulation,and no noise.Physical simulation is often used to predict the output performance of flexible EHD pumps.However,this method neglects fluid–solid interaction and energy loss caused by flexible materials,which are both difficult to calculate when the flexible pumps deform.Therefore,this study proposes a flexible pump output performance prediction using machine learning algorithms.We used three different types of machine learning:random forest regression,ridge regression,and neural network to predict the critical parameters(pressure,flow rate,and power)of the flexible EHD pump.Voltage,angle,gap,overlap,and channel height are selected as five input data of the neural network.In addition,we optimized essential parameters in the three networks.Finally,we adopt the best predictive model and evaluate the significance of five input parameters to the output performance of the flexible EHD pumps.Among the three methods,the MLP model has exceptionally high accuracy in predicting pressure and flow.Our work is beneficial for the design process of fluid sources in flexible soft actuators and soft hydraulic sources in microfluidic chips.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61304254,61321002 and 61120106010the Key Exploration Project under Grant No.7131253
文摘Monitoring the operational state of sensors promptly and the accurate diagnosis of faults are essential. This paper proposes an improved fault diagnosis scheme for sensors, which includes both fault detection and fault identification. Firstly, trend analysis combined with least squares support vector machine（TA-LSSVM） method is proposed and implemented to detect faults. Secondly, an improved error correcting output coding-support vector machine（ECOC-SVM） based fault identification method is proposed to distinguish different sensor failure modes. To demonstrate the effectiveness of the proposed scheme, experiments are conducted with an MTi-series sensor, and some comparisons are made with other fault identification methods. The experimental results demonstrate that the proposed fault diagnosis scheme offers an essential improvement with detection real-time property and better identification accuracy.
基金This work was supported by the Project of Shandong Province Higher Educational Science and Technology Program(No.J18KA316)Liaoning Science and Technology Plan(No.20180550573)+1 种基金the Shandong Science and Technology Development Plan(No.2019GGX104019)Guangdong Basic and Applied Basic Research Foundation(No.2019A1515110706).
文摘The emergence of electronic devices has brought earth-shaking changes to people's life.However,an extemal power source may become indispensable to the electronie devices due to the limited capacity of batteries.As one of the possible solutions for the extermal power sources,the triboelectric nanogenerator(TENG)provides a novel idea to the increasing mumber of personal electronic devices.TENG is a new type of energy ollector,which has become a hot spot in the field of nanotechnology.It is widely used at the acquisition and conversion of mechan-ical enegy to electrice energy through the principle of electrostatic induction.On this basis,the TENG could be integrated with the energy stonage system into a self-powered system,W hich can supply power to the electronic devices and make them work continuously.In this review,TENG's basic structure as well as its working process and working mode are firstly discussed.The integration method of TENGs with enegy storage systems and the related research status are then introduced in detail.At the end of this paper,we put forward some problems and discuss the prospect in the future.
基金This work was supported by the Navigation Science Foundation (No.05F07001)the National Natural Science Foundation of China (No.60472081).
文摘A novel image fusion algorithm based on bandelet transform is proposed. Bandelet transform can take advantage of the geometrical regularity of image structure and represent sharp image transitions such as edges efficiently in image fusion. For reconstructing the fused image, the maximum rule is used to select source images＇ geometric flow and bandelet coefficients. Experimental results indicate that the bandelet-based fusion algorithm represents the edge and detailed information well and outperforms the wavelet-based and Laplacian pyramid-based fusion algorithms, especially when the abundant texture and edges are contained in the source images.
基金supported by the National Natural Science Foundation of China(42130513,41905110,and 41961130384)the Royal Society Newton Advanced Fellowship,United Kingdom(NAFR1191220)the Research Grants Council of the Hong Kong Special Administrative Region,China(T24/504/17 and A-Poly U502/16)。
文摘Aerosol ammonium(NH_(4)^(+)),mainly produced from the reactions of ammonia(NH_(3))with acids in the atmosphere,has significant impacts on air pollution,radiative forcing,and human health.Understanding the source and formation mechanism of NH_(4)^(+)can provide scientific insights into air quality improvements.However,the sources of NH_(3)in urban areas are not well understood,and few studies focus on NH_(3)/NH_(4)^(+)at different heights within the atmospheric boundary layer,which hinders a comprehensive understanding of aerosol NH_(4)^(+).In this study,we perform both field observation and modeling studies(the Community Multiscale Air Quality,CMAQ)to investigate regional NH_(3)emission sources and vertically resolved NH_(4)^(+)formation mechanisms during the winter in Beijing.Both stable nitrogen isotope analyses and CMAQ model suggest that combustion-related NH_(3)emissions,including fossil fuel sources,NH_(3)slip,and biomass burning,are important sources of aerosol NH_(4)^(+)with more than 60%contribution occurring on heavily polluted days.In contrast,volatilization-related NH_(3)sources(livestock breeding,N-fertilizer application,and human waste)are dominant on clean days.Combustion-related NH_(3)is mostly local from Beijing,and biomass burning is likely an important NH_(3)source(~15%–20%)that was previously overlooked.More effective control strategies such as the two-product(e.g.,reducing both SO_(2)and NH_(3))control policy should be considered to improve air quality.
基金This study is financially supported by the National Basic Research Program(2017YFB0903800)and the Electric Power Research Institute,Yunnan Electric Power Grid Co.Ltd,Kunming,Yunnan 650217,China(YNKJXM20160146).
文摘Surface flashover of spacers is a key factor limiting the application of HVDC GIS/GIL,while the charge accumula-tion on the surface of the spacer could have a potential adverse effect on the surface flashover voltage.This paper discusses the laws regarding distribution patterns of surface charges and the related mechanisms.The field-dependent property is discussed in detail to comprehensively illustrate the charge transport mecha-nism and explain the research differences regarding different surface charge patterns obtained by previous researchers.In addition,the main surface charge control methods for epoxy resin are summarized and discussed.The potential research directions of charge control methods and key points in manufacturing of spacers used in HVDC GIS/GIL are also explored.
基金supported by the Shandong Science and Technology Development Plan(No.GG201809230040)the National Natural Science Foundation of China(Grant Nos.61573202 and 11847135).
文摘A flexible,multi-site tactile and thermal sensor(MTTS)based on polyvinylidene fluoride(resolution 50×50)is reported.It can be used to implement spatial mapping caused by tactile and thermal events and record the two-dimensional motion trajectory of a tracked target object.The output voltage and current signal are recorded as amapping by sensing the external pressure and thermal radiation stimulus,and the response distribution is dynamically observed on the three-dimensional interface.Through the mapping relationship between the established piezoelectric and pyroelectric signals,the piezoelectric component and the pyroelectric component are effectively extracted from the composite signals.The MTTS has a good sensitivity for tactile and thermal detection,and the electrodes have good synchronism.In addition,the signal interference is less than 9.5%and decreases as the pressure decreases after the distance between adjacent sites exceeds 200μm.The integration of MTTS and signal processing units has potential applications in human-machine interaction systems,health status detection and smart assistive devices.
基金the National Natural Science Foundation of China (No.61627810)the National Science and Technology Major Program of China (No.2018YFB1305003)the National Defense Science and Technology Outstanding Youth Science Foundation (No.2017-JCJQ-ZQ-031)。
文摘In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion module is studied.Furthermore,a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking algorithm.For the problem of object association failure caused by UAV movement,image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking algorithm.The simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform,and effectively solve the problem of association failure caused by UAV movement.
基金This work is supported by the National Natural Science Foundation of China(62262016,61961160706,62231010)14th Five-Year Plan Civil Aerospace Technology Preliminary Research Project(D040405)the National Key Laboratory Foundation 2022-JCJQ-LB-006(Grant No.6142411212201).
文摘When learning the structure of a Bayesian network,the search space expands significantly as the network size and the number of nodes increase,leading to a noticeable decrease in algorithm efficiency.Traditional constraint-based methods typically rely on the results of conditional independence tests.However,excessive reliance on these test results can lead to a series of problems,including increased computational complexity and inaccurate results,especially when dealing with large-scale networks where performance bottlenecks are particularly evident.To overcome these challenges,we propose a Markov blanket discovery algorithm based on constrained local neighborhoods for constructing undirected independence graphs.This method uses the Markov blanket discovery algorithm to refine the constraints in the initial search space,sets an appropriate constraint radius,thereby reducing the initial computational cost of the algorithm and effectively narrowing the initial solution range.Specifically,the method first determines the local neighborhood space to limit the search range,thereby reducing the number of possible graph structures that need to be considered.This process not only improves the accuracy of the search space constraints but also significantly reduces the number of conditional independence tests.By performing conditional independence tests within the local neighborhood of each node,the method avoids comprehensive tests across the entire network,greatly reducing computational complexity.At the same time,the setting of the constraint radius further improves computational efficiency while ensuring accuracy.Compared to other algorithms,this method can quickly and efficiently construct undirected independence graphs while maintaining high accuracy.Experimental simulation results show that,this method has significant advantages in obtaining the structure of undirected independence graphs,not only maintaining an accuracy of over 96%but also reducing the number of conditional independence tests by at least 50%.This sig
基金the Industry-University-Research Cooperation Fund Project of the Eighth Research Institute of China Aerospace Science and Technology Corporation (No.USCAST2021-5)the Major Scientific Instrument Research of National Natural Science Foundation of China (No.61627810)+1 种基金the National Science and Technology Major Program of China (No.2018YFB1305003)the National Defense Science and Technology Outstanding Youth Science Foundation (No.2017-JCJQ-ZQ-031)。
文摘Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking systems.However,the complex background,the strong noise,and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets.A multi-channel based on attention network is proposed in this paper,aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model,high complexity and poor detection performance of deep learning algorithms.First,given the difficulty in extracting the features of infrared multiscale and small dim targets,the multiple channels are designed based on dilated convolution to capture multiscale target features.Second,the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target features.In addition,the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion block.Finally,it is verified that,compared with other state-of-the-art methods based on the datasets SIRST and MDFA,the proposed algorithm further improves the detection effect,and the model size and computational complexity are smaller.
基金supported in part by the National Natural Science Foundation of China (62225309,62073222,U21A20480,62361166632)。
文摘Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory.In this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations.We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation.This tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual domains.Furthermore,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation.Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures.Comprehensive validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.
基金the National Natural Science Foundation of China (No.61627810)the National Science and Technology Major Program of China (No.2018YFB1305003)the National Defense Science and Technology Outstanding Youth Science Foundation (No.2017-JCJQ-ZQ-031)。
文摘Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from the perspectives of network update strategy,initialization method,and parameter selection.This paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic algorithms.Simulations show that compared with existing SOM networks,the improved SOM network proposed in this paper improves the convergence rate and algorithm accuracy.Compared with iterated local search and heuristic algorithms,the improved SOM net-work algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP.
基金supported in part by the National Natural Science Foundation of China(61933007, U21A2019, 62273005, 62273088, 62303301)the Program of Shanghai Academic/Technology Research Leader of China (20XD1420100)+2 种基金the Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105)the Natural Science Foundation of Anhui Province of China (2108085MA07)the Alexander von Humboldt Foundation of Germany。
文摘This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.
文摘The Coronavirus Disease(COVID-19)pandemic has exposed the vulnerabilities of medical services across the globe,especially in underdeveloped nations.In the aftermath of the COVID-19 outbreak,a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods.Medical imaging has become a crucial component in the disease diagnosis process,whereas X-rays and Computed Tomography(CT)scan imaging are employed in a deep network to diagnose the diseases.In general,four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks,such as network training,feature extraction,model performance testing and optimal feature selection.The current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion(CFPADLDF)approach for detecting and classifying COVID-19.The presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical images.Initially,the proposed CFPA-DLDF technique employs the Gabor Filtering(GF)approach to pre-process the input images.In addition,a weighted voting-based ensemble model is employed for feature extraction,in which both VGG-19 and the MixNet models are included.Finally,the CFPA with Recurrent Neural Network(RNN)model is utilized for classification,showing the work’s novelty.A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model,and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches.
基金Project supported by the National Natural Science Foundation of China(Nos.12072266,12172297,11972287,and 12072262)the Open Foundation of the State Key Laboratory of Structural Analysis for Industrial Equipment of China(No.GZ22106)。
文摘An analytical method,called the symplectic mathematical method,is proposed to study the wave propagation in a spring-mass chain with gradient arranged local resonators and nonlinear ground springs.Combined with the linearized perturbation approach,the symplectic transform matrix for a unit cell of the weakly nonlinear graded metamaterial is derived,which only relies on the state vector.The results of the dispersion relation obtained with the symplectic mathematical method agree well with those achieved by the Bloch theory.It is shown that wider and lower frequency bandgaps are formed when the hardening nonlinearity and incident wave intensity increase.Subsequently,the displacement response and transmission performance of nonlinear graded metamaterials with finite length are studied.The dual tunable effects of nonlinearity and gradation on the wave propagation are explored under different excitation frequencies.For small excitation frequencies,the gradient parameter plays a dominant role compared with the nonlinearity.The reason is that the gradient tuning aims at the gradient arrangement of local resonators,which is limited by the critical value of the local resonator mass.In contrast,for larger excitation frequencies,the hardening nonlinearity is dominant and will contribute to the formation of a new bandgap.
文摘Robotics has aroused huge attention since the 1950s.Irrespective of the uniqueness that industrial applications exhibit,conventional rigid robots have displayed noticeable limitations,particularly in safe cooperation as well as with environmental adaption.Accordingly,scientists have shifted their focus on soft robotics to apply this type of robots more effectively in unstructured environments.For decades,they have been committed to exploring sub-fields of soft robotics(e.g.,cutting-edge techniques in design and fabrication,accurate modeling,as well as advanced control algorithms).Although scientists have made many different efforts,they share the common goal of enhancing applicability.The presented paper aims to brief the progress of soft robotic research for readers interested in this field,and clarify how an appropriate control algorithm can be produced for soft robots with specific morphologies.This paper,instead of enumerating existing modeling or control methods of a certain soft robot prototype,interprets for the relationship between morphology and morphology-dependent motion strategy,attempts to delve into the common issues in a particular class of soft robots,and elucidates a generic solution to enhance their performance.
基金This work was supported by the National Natural Science Foundation of China(41921005,42130708,and 72140003)and the Energy Foundation.
文摘It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction,air quality improvement,and improved health.In the context of carbon peak,carbon neutrality,and clean air policies,this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 indicators.The 18 indicators cover the following five aspects:air pollution and associated weather-climate conditions,progress in structural transition,sources,inks,and mitigation pathway of atmospheric composition,health impacts and benefits of coordinated control,and synergetic governance system and practices.By tracking the progress in each indicator,this perspective presents the major accomplishment of coordinated control,identifies the emerging challenges toward the synergetic governance,and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.
基金This work was supported,in part,by the Natural Science Foundation of Jiangsu Province under Grant Numbers BK20201136,BK20191401in part,by the National Nature Science Foundation of China under Grant Numbers 62272236,61502096,61304205,61773219,61502240in part,by the Public Welfare Fund Project of Zhejiang Province Grant Numbers LGG20E050001.
文摘Convolution Neural Networks(CNN)can quickly diagnose COVID-19 patients by analyzing computed tomography(CT)images of the lung,thereby effectively preventing the spread of COVID-19.However,the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population.Which reduces the model’s classification sensitivity,resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people.To solve the problem,this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image classification,combining softmax loss to design a jointly supervised metric loss function COVID Triplet-Center Loss(COVID-TCL).Triplet loss can increase inter-class discreteness,and center loss can improve intra-class compactness.Therefore,COVID-TCL can help the CNN-based model to extract more discriminative features and strengthen the diagnostic capacity of COVID-19 patients in the early stage and incubation period.Meanwhile,we use the extreme gradient boosting(XGBoost)as a classifier to design a COVID-19 images classification model of CNN-XGBoost architecture,to further improve the CNN-based model’s classification effect and operation efficiency.The experiment shows that the classification accuracy of the model proposed in this paper is 97.41%,and the sensitivity is 97.61%,which is higher than the other 7 reference models.The COVID-TCL can effectively improve the classification sensitivity of the CNN-based model,the CNN-XGBoost architecture can further improve the CNN-based model’s classification effect.
