Aeroengines,often regarded as the heart of aircraft,are crucial for flight safety and performance.Comprehensive performance evaluation of aeroengines supports Prognostics and Health Management(PHM)and aeroengine digit...Aeroengines,often regarded as the heart of aircraft,are crucial for flight safety and performance.Comprehensive performance evaluation of aeroengines supports Prognostics and Health Management(PHM)and aeroengine digital engineering.Due to their highly integrated nature,aeroengines present challenges in performance evaluation because their test-run data are high-dimensional,large-scale,and exhibit strong nonlinear correlations among test indicators.To solve this problem,this study proposes a unified framework of the comprehensive performance evaluation of aeroengines to assess performance objectively and globally.Specifically,the network model and the dynamics model of aeroengine performance are constructed driven by test-run data,which can explain the patterns of system state changes and the internal relationship,and depict the system accurately.Based on that,three perturbations in the model are used to simulate three fault modes of aeroengines.Moreover,the comprehensive performance evaluation indexes of aeroengines are proposed to evaluate the performance dynamically from two dimensions,the coupling performance and the activity performance.Thirteen test-run qualified and four test-run failed aeroengines are used to validate and establish the qualified ranges.The results demonstrate that the comprehensive evaluation indexes can distinguish test-run qualified and test-run failed aeroengines.By changing the dynamic parameters,the comprehensive performance under any thrust and inlet guide vanes(IGV)angle can be estimated,broadening the test-run scenarios beyond a few typical states.This novel approach offers significant advancements for the comprehensive performance evaluation and management of aeroengines,paving the way for future PHM and aeroengine digital engineering developments.展开更多
The social progress index(SPI)measures social and environmental performance beyond traditional economic indicators,providing transparent and actionable insights into the true condition of societies.This study investig...The social progress index(SPI)measures social and environmental performance beyond traditional economic indicators,providing transparent and actionable insights into the true condition of societies.This study investigates the interdependencies among SPI components and their impact on country-level sustainability performance.Using a Bayesian Belief Network(BBN)approach,the analysis explores the interdependencies among 12 SPI components(including advanced education,basic education,environmental quality,freedom and choice,health,housing,inclusive society,information and communications,nutrition and medical care,rights and voice,safety,and water and sanitation)and their collective influence on sustainability performance.Data from the Sustainable Development Report and SPI datasets,covering 162 countries(including Australia,China,United Arab Emirates,United Kingdom,United States,and so on),were used to assess the relative importance of each SPI component.The key findings indicate that advanced education,inclusive society,and freedom and choice make substantial contributions to high sustainability performance,whereas deficiencies in nutrition and medical care,water and sanitation,and freedom and choice are associated with poor sustainability performance.The results reveal that sustainability performance is shaped by a network of interlinked SPI components,with education and inclusion emerging as key levers for progress.The study emphasizes that targeted improvements in specific SPI components can significantly enhance a country’s overall sustainability performance.Rather than visualizing countries’progress through composite indicator-based heat maps,this study explores the interdependencies among SPI components and their role in sustainability performance at the global level.The study underscores the importance of a multidimensional policy approach that addresses social and environmental factors to enhance sustainability.The findings contribute to a deeper understanding of how SPI components interact and shape sustainable development.展开更多
Al-Si alloy,a high temperature phase change material,has great potential in thermal management due to its advantages of high heat storage density and thermal conductivity.Microencapsulation of Al-Si alloy is one of th...Al-Si alloy,a high temperature phase change material,has great potential in thermal management due to its advantages of high heat storage density and thermal conductivity.Microencapsulation of Al-Si alloy is one of the effective techniques to solve high temperature leakage and corrosion.In this paper,commercial Al-10Si alloy micro powders were encapsulated with flexible ceramic shells whose total thickness is below 1μm by hydrothermal treatment and heat treatment in N_(2) atmosphere.The compositions and microstructures were characterized by XRD,SEM and TEM.The shell was composed of AlN fibers network structure embedded withα-Al_(2)O_(3)/AlN which prevented the alloy from leaking and oxidizing,as well as had excellent thermal stability.The latent heat of microcapsules was 351.8 J g^(-1)for absorption and 372.7 J g^(-1)for exothermic.The microcapsules showed near zero thermal performance loss with latent heat storage(LHS)/release(LHR)was 353.2/403.7 J g^(-1)after 3000 cycles.Compared with the published Al-Si alloy microcapsules,both high heat storage density and super thermal cycle stability were achieved,showing promising development prospects in high temperature thermal management.展开更多
This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eli...This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.展开更多
This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance system...This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.展开更多
This article investigates the approaching control for fixed-wing Unmanned Aerial Vehi-cle(UAV)aerial recovery in the presence of pre-specified performance requirements,complex air-flows,maneuvering flight of transport...This article investigates the approaching control for fixed-wing Unmanned Aerial Vehi-cle(UAV)aerial recovery in the presence of pre-specified performance requirements,complex air-flows,maneuvering flight of transport aircraft,and different initial deviations.First,a novelcontrol-oriented Six-Degree-Of-Freedom(6-DOF)UAV model considering airflow disturbancesis established for better consistency with the actual UAV system.Then,to achieve satisfactory per-formance in the approaching process,a Flexible Appointed-time Prescribed Performance Control(FAPPC)algorithm,with the features of user-specified time convergence,no overshoot,indepen-dence from the initial value,and singularity-free,is proposed.Specifically,to solve the singularityissue encountered by the existing PPC methods in dealing with sudden disturbances,an adaptiveadjustment signal is introduced in FAPPC to perceive the threat of increasing error and relax thepreset boundaries appropriately.Moreover,minimum learning parameter-based neural networkestimators are developed to approximate unknown lumped disturbances at a low computationalcost.Finally,the stability of the closed system is analyzed via Lyapunov synthesis,and the effective-ness and advantages of the proposed control scheme are demonstrated via simulation andHardware-In-the-Loop(HIL)experimental validation.展开更多
Predicting player performance in sports is a critical challenge with significant implications for team success,fan engagement,and financial outcomes.Although,inMajor League Baseball(MLB),statistical methodologies such...Predicting player performance in sports is a critical challenge with significant implications for team success,fan engagement,and financial outcomes.Although,inMajor League Baseball(MLB),statistical methodologies such as sabermetrics have been widely used,the dynamic nature of sports makes accurate performance prediction a difficult task.Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions.This study addresses this challenge by employing the temporal fusion transformer(TFT),an advanced and cutting-edge deep learning model for complex data,to predict pitchers’earned run average(ERA),a key metric in baseball performance analysis.The performance of the TFT model is evaluated against recurrent neural network-based approaches and existing projection systems.In experimental results,the TFT based model consistently outperformed its counterparts,demonstrating superior accuracy in pitcher performance prediction.By leveraging the advanced capabilities of TFT,this study contributes to more precise player evaluations and improves strategic planning in baseball.展开更多
The determination of source-side extracted heating parameters is of great significance to the economic operation of cogeneration systems.This paper investigated the coupling performance of a cogeneration heating and p...The determination of source-side extracted heating parameters is of great significance to the economic operation of cogeneration systems.This paper investigated the coupling performance of a cogeneration heating and power system multidimensionally based on the operating characteristics of the cogeneration units,the hydraulic and thermodynamic characteristics of the heating network,and the energy loads.Taking a steam network supported by a gas-steam combined cycle cogeneration system as the research case,the interaction effect among the source-side prime movers,the heating networks,and the terminal demand thermal parameters were investigated based on the designed values,the plant testing data,and the validated simulation.The operating maps of the gas-steam combined cycle cogeneration units were obtained using THERMOFLEX,and the minimum source-side steam parameters of the steam network were solved using an inverse solution procedure based on the hydro-thermodynamic coupling model.The cogeneration operating maps indicate that the available operating domain considerably narrows with the rise of the extraction steam pressure and flow rate.The heating network inverse solution demonstrates that the source-side steam pressure and temperature can be optimized from the originally designed 1.11 MPa and 238.8°C to 1.074 MPa and 191.15°C,respectively.Under the operating strategy with the minimum source-side heating parameters,the power peak regulation depth remarkably increases to 18.30%whereas the comprehensive thermal efficiency decreases.The operation under the minimum source-side heating steam parameters can be superior to the originally designed one in the economy at a higher price of the heating steam.At a fuel price of$0.38/kg and the power to fuel price of 0.18 kg/(kW·h),the critical price ratio of heating steam to fuel is 119.1 kg/t.The influence of the power-fuel price ratio on the economic deviation appears relatively weak.展开更多
A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direc...A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements.Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format,probabilistic shaping,roll-off factor,baud rate,optical signal-to-noise ratio,and chromatic dispersion.The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios.Furthermore,the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network.Compared with the designed VGG-based CNN,the MobileNet-based MTL does not need to train all the classes,and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy,indicating great potential in various monitoring scenarios.展开更多
The network on chip（NoC）is used as a solution for the communication problems in a complex system on chip（SoC）design.To further enhance performances,the NoC architectures,a high level modeling and an evaluation met...The network on chip（NoC）is used as a solution for the communication problems in a complex system on chip（SoC）design.To further enhance performances,the NoC architectures,a high level modeling and an evaluation method based on OPNET are proposed to analyze their performances on different injection rates and traffic patterns.Simulation results for general NoC in terms of the average latency and the throughput are analyzed and used as a guideline to make appropriate choices for a given application.Finally,a MPEG4 decoder is mapped on different NoC architectures.Results prove the effectiveness of the evaluation method.展开更多
An analytical approach to evaluate the performance of the 3G/ad hoc integrated network is presented. A channel model capturing both path loss and shadowing is applied to the analysis so as to characterize power fallof...An analytical approach to evaluate the performance of the 3G/ad hoc integrated network is presented. A channel model capturing both path loss and shadowing is applied to the analysis so as to characterize power falloff vs. distance. The 3G/ad hoc integrated network scenario model is introduced briefly. Based on this model, several performances of the 3G/ ad hoc integrated network in terms of outage probability, call dropping probability and new call blocking probability are evaluated. The corresponding performance formulae are deduced in accordance with the analytical models. Meanwhile, the formula of the 3G/ad hoc integrated network capacity is deduced on the basis of the formula of the outage probability. It is observed from extensive simulation and numerical analysis that the 3G/ad hoc integrated network remarkably outperforms the 3G network with regards to the network performance. This derived evaluation approach can be applied into planning and optimization of the 3G/ad hoc network.展开更多
The Open Flow implementations(SDNs) have been deployed increasingly on varieties of networks in research institutions as well as commercial institutions. To develop an Open Flow implementation, it is required to under...The Open Flow implementations(SDNs) have been deployed increasingly on varieties of networks in research institutions as well as commercial institutions. To develop an Open Flow implementation, it is required to understand the performance of the network. A few benchmark tools(e.g., Cbench and OFlops) can be used to measure the network performance, while these tools take considerable time to simulate traffic behaviors and generate the required results,therefore extending the development time. In this paper, we present an analytical model, which is based on stochastic network calculus theory, for evaluating the performance of switch to controller.The previous studies show that stochastic network calculus can provide realistic emulation of real network traffic behaviors. Our model is evaluated by using both simulation tool and realistic testbed.The results show the stochastic network calculus based analysis model can realistically measure the network performance of the end-to-end properties between controller and switch.展开更多
A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, ro...A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, roadhead ers are m echanical m iners w h ich have b een extensively u se d in tu n n elin g , m ining an d civil indu stries. Perform ance pred ictio n is an im p o rta n t issue for successful ro a d h e a d e r application andgenerally deals w ith m achine selection, p ro d u ctio n rate an d b it consu m p tio n . The m ain aim o f thisresearch is to investigate th e c u ttin g p erfo rm an ce （in stan tan eo u s c u ttin g rates （ICRs）） o f m ed iu m -d u tyro ad h ead ers by using artificial neural n etw o rk （ANN） approach. T here are d ifferent categories forANNs, b u t based o n train in g alg o rith m th e re are tw o m ain k in d s： supervised and u n su p erv ised . Them u lti-lay er p ercep tro n （MLP） an d K ohonen self-organizing feature m ap （KSOFM） are th e m o st w idelyused neu ral netw o rk s for supervised an d u n su p erv ised ones, respectively. For gaining this goal, ad atab ase w as prim arily provided from ro ad h e a d e rs＇ p erfo rm an ce an d geom echanical characteristics o frock form ations in tu n n els and d rift galleries in Tabas coal m ine, th e larg est an d th e only fullymech an ized coal m ine in Iran. T hen th e datab ase w as analyzed in o rd e r to yield th e m ost im p o rtan tfactor for ICR by using relatively im p o rta n t factor in w hich G arson eq u atio n w as utilized. The MLPn etw o rk w as train ed by 3 in p u t p ara m e te rs including rock m ass pro p erties, rock quality d esignation（RQD）, in tact rock p ro p erties such as uniaxial com pressive stre n g th （UCS） an d Brazilian ten sile stren g th（BTS）, and o n e o u tp u t p a ra m e te r （ICR）. In o rd e r to have m ore v alidation o n MLP o u tp u ts, KSOFM visualizationw as applied. The m ean square e rro r （MSE） an d regression coefficient （R ） o f MLP w e re found tobe 5.49 an d 0.97, respectively. M oreover, KSOFM n etw o rk has a m ap size o f 8 x 5 and final qu an tizatio nan d topographic erro rs w e re 0.383 an d 0.032, respectively. The results show th a t MLP neural n etw orkshave a strong capability to p red ict an d ev alu ate th e perfo rm an ce o f m ed iu m -d u ty ro ad h ead ers in coalm easu re rocks. Furtherm ore, it is concluded th a t KSOFM neural n etw o rk is an efficient w ay for u n d e rstand in g system beh av io r an d know ledge extraction. Finally, it is indicated th a t UCS has m ore influenceo n ICR b y applying th e b e st train ed MLP n etw o rk w eig h ts in G arson eq u atio n w h ich is also confirm ed byKSOFM.展开更多
A K-tier uplink heterogeneous cellular network is modelled and analysed by accounting for both truncated channel inversion power control and biased user association. Each user has a maximum transmit power constraint a...A K-tier uplink heterogeneous cellular network is modelled and analysed by accounting for both truncated channel inversion power control and biased user association. Each user has a maximum transmit power constraint and transmits data when it has sufficient transmit power to perform channel inversion. With biased user association, each user is associated with a base station(BS) that provides the maximum received power weighted by a bias factor, but not their nearest BS. Stochastic geometry is used to evaluate the performances of the proposed system model in terms of the outage probability and ergodic rate for each tier as functions of the biased and power control parameters. Simulations validate our analytical derivations. Numerical results show that there exists a trade-off introduced by the power cut-off threshold and the maximum user transmit power constraint. When the maximum user transmit power becomes a binding constraint, the overall performance is independent of BS densities. In addition, we have shown that it is beneficial for the outage and rate performances by optimizing different network parameters such as the power cut-off threshold as well as the biased factors.展开更多
Water-using operations in the process industry have demands for water usually both on water quality and temperature, and the existing mathematical models of heat exchange networks cannot guarantee the energy performan...Water-using operations in the process industry have demands for water usually both on water quality and temperature, and the existing mathematical models of heat exchange networks cannot guarantee the energy performance of a water network optimal. In this paper, the effects of non-isothermal merging on energy performance of water allocation networks are analyzed, which include utility consumption, total heat exchange load, and number of heat exchange matches. Three principles are proposed to express the effects of non-isothermal merging on energy performance of water allocation networks. A rule of non-isothermal merging without increasing utility consumption is deduced. And an approach to improve energy performance of water allocation network is presented. A case study is given to demonstrate the method.展开更多
Palmprint recognition and palm vein recognition are two emerging biometrics technologies.In the past two decades,many traditional methods have been proposed for palmprint recognition and palm vein recognition,and have...Palmprint recognition and palm vein recognition are two emerging biometrics technologies.In the past two decades,many traditional methods have been proposed for palmprint recognition and palm vein recognition,and have achieved impressive results.However,the research on deep learningbased palmprint recognition and palm vein recognition is still very preliminary.In this paper,in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition indepth,we conduct performance evaluation of seventeen representative and classic convolutional neural networks(CNNs)on one 3D palmprint database,five 2D palmprint databases and two palm vein databases.A lot of experiments have been carried out in the conditions of different network structures,different learning rates,and different numbers of network layers.We have also conducted experiments on both separate data mode and mixed data mode.Experimental results show that these classic CNNs can achieve promising recognition results,and the recognition performance of recently proposed CNNs is better.Particularly,among classic CNNs,one of the recently proposed classic CNNs,i.e.,EfficientNet achieves the best recognition accuracy.However,the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.展开更多
This paper studies the problems of H-infinity performance optimization and controller design for continuous-time NCSs with both sensor-to-controller and controller-to-actuator communication constraints （limited commu...This paper studies the problems of H-infinity performance optimization and controller design for continuous-time NCSs with both sensor-to-controller and controller-to-actuator communication constraints （limited communication channels）. By taking the derivative character of network-induced delay into full consideration and defining new Lyapunov functions, linear matrix inequalities （LMIs）-based H-infinity performance optimization and controller design are presented for NCSs with limited communication channels. If there do not exist any constraints on the communication channels, the proposed design methods are also applicable. The merit of the proposed methods lies in their Jess conservativeness, which is achieved by avoiding the utilization of bounding inequalities for cross products of vectors. The simulation results illustrate the merit and effectiveness of the proposed H-infinity controller design for NCSs with limited communication channels.展开更多
With rapid development of unmanned aerial vehicles(UAVs), more and more UAVs access satellite networks for data transmission. To improve the spectral efficiency, non-orthogonal multiple access(NOMA) is adopted to inte...With rapid development of unmanned aerial vehicles(UAVs), more and more UAVs access satellite networks for data transmission. To improve the spectral efficiency, non-orthogonal multiple access(NOMA) is adopted to integrate UAVs into the satellite network, where multiple satellites cooperatively serve the UAVs and mobile terminal using the Ku-band and above. Taking into account the rain fading and the fading correlation, the outage performance is first analytically obtained for fixed power allocation and then efficiently calculated by the proposed power allocation algorithm to guarantee the user fairness. Simulation results verify the outage performance analysis and show the performance improvement of the proposed power allocation scheme.展开更多
In High-Speed Railways(HSRs),the Train Control and Management System(TCMS)plays a crucial role.However,as the demand for train networks grows,the limitations of traditional wired connections have become apparent.This ...In High-Speed Railways(HSRs),the Train Control and Management System(TCMS)plays a crucial role.However,as the demand for train networks grows,the limitations of traditional wired connections have become apparent.This paper designs and implements a Wireless Train Communication Network(WTCN)to enhance the existing train network infrastructure.To address the challenges that wireless communication technology faces in the unique environment of high-speed rail,this study first analyzes various onboard environments and simulates several typical scenarios in the laboratory.Integrating the specific application scenarios and service characteristics of the high-speed train control network,we conduct measurements and validations of WiFi performance,exploring the specific impacts of different factors on throughput and delay.展开更多
基金supported by the National Natural Science Foundation of China(72231008,72171193,and 72071153)the Science and Technology Innovation Group Program of Shaanxi Province(2024RS-CXTD-28)the Open Fund of Intelligent Control Laboratory(ICL-2023-0304).
文摘Aeroengines,often regarded as the heart of aircraft,are crucial for flight safety and performance.Comprehensive performance evaluation of aeroengines supports Prognostics and Health Management(PHM)and aeroengine digital engineering.Due to their highly integrated nature,aeroengines present challenges in performance evaluation because their test-run data are high-dimensional,large-scale,and exhibit strong nonlinear correlations among test indicators.To solve this problem,this study proposes a unified framework of the comprehensive performance evaluation of aeroengines to assess performance objectively and globally.Specifically,the network model and the dynamics model of aeroengine performance are constructed driven by test-run data,which can explain the patterns of system state changes and the internal relationship,and depict the system accurately.Based on that,three perturbations in the model are used to simulate three fault modes of aeroengines.Moreover,the comprehensive performance evaluation indexes of aeroengines are proposed to evaluate the performance dynamically from two dimensions,the coupling performance and the activity performance.Thirteen test-run qualified and four test-run failed aeroengines are used to validate and establish the qualified ranges.The results demonstrate that the comprehensive evaluation indexes can distinguish test-run qualified and test-run failed aeroengines.By changing the dynamic parameters,the comprehensive performance under any thrust and inlet guide vanes(IGV)angle can be estimated,broadening the test-run scenarios beyond a few typical states.This novel approach offers significant advancements for the comprehensive performance evaluation and management of aeroengines,paving the way for future PHM and aeroengine digital engineering developments.
基金the resources and support provided by the American University of Sharjah to conduct this research
文摘The social progress index(SPI)measures social and environmental performance beyond traditional economic indicators,providing transparent and actionable insights into the true condition of societies.This study investigates the interdependencies among SPI components and their impact on country-level sustainability performance.Using a Bayesian Belief Network(BBN)approach,the analysis explores the interdependencies among 12 SPI components(including advanced education,basic education,environmental quality,freedom and choice,health,housing,inclusive society,information and communications,nutrition and medical care,rights and voice,safety,and water and sanitation)and their collective influence on sustainability performance.Data from the Sustainable Development Report and SPI datasets,covering 162 countries(including Australia,China,United Arab Emirates,United Kingdom,United States,and so on),were used to assess the relative importance of each SPI component.The key findings indicate that advanced education,inclusive society,and freedom and choice make substantial contributions to high sustainability performance,whereas deficiencies in nutrition and medical care,water and sanitation,and freedom and choice are associated with poor sustainability performance.The results reveal that sustainability performance is shaped by a network of interlinked SPI components,with education and inclusion emerging as key levers for progress.The study emphasizes that targeted improvements in specific SPI components can significantly enhance a country’s overall sustainability performance.Rather than visualizing countries’progress through composite indicator-based heat maps,this study explores the interdependencies among SPI components and their role in sustainability performance at the global level.The study underscores the importance of a multidimensional policy approach that addresses social and environmental factors to enhance sustainability.The findings contribute to a deeper understanding of how SPI components interact and shape sustainable development.
基金financial support from the National Natural Science Foundation of China(No.52072276)Hubei Important Project on Science and Technology(No.2022BECO20).
文摘Al-Si alloy,a high temperature phase change material,has great potential in thermal management due to its advantages of high heat storage density and thermal conductivity.Microencapsulation of Al-Si alloy is one of the effective techniques to solve high temperature leakage and corrosion.In this paper,commercial Al-10Si alloy micro powders were encapsulated with flexible ceramic shells whose total thickness is below 1μm by hydrothermal treatment and heat treatment in N_(2) atmosphere.The compositions and microstructures were characterized by XRD,SEM and TEM.The shell was composed of AlN fibers network structure embedded withα-Al_(2)O_(3)/AlN which prevented the alloy from leaking and oxidizing,as well as had excellent thermal stability.The latent heat of microcapsules was 351.8 J g^(-1)for absorption and 372.7 J g^(-1)for exothermic.The microcapsules showed near zero thermal performance loss with latent heat storage(LHS)/release(LHR)was 353.2/403.7 J g^(-1)after 3000 cycles.Compared with the published Al-Si alloy microcapsules,both high heat storage density and super thermal cycle stability were achieved,showing promising development prospects in high temperature thermal management.
基金the National Natural Science Foundation of China(62203356)Fundamental Research Funds for the Central Universities of China(31020210502002)。
文摘This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.
基金the National Natural Science Foundation of China(Grant No.12072090).
文摘This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.
基金funded by the National Natural Science Foundation of China(Nos.62173022,61673042)the Academic Excellence Foundation of Beihang University for Ph.D.Studentsthe Outstanding Research Project of Shen Yuan Honors College,Beihang University,China(No.230123104)。
文摘This article investigates the approaching control for fixed-wing Unmanned Aerial Vehi-cle(UAV)aerial recovery in the presence of pre-specified performance requirements,complex air-flows,maneuvering flight of transport aircraft,and different initial deviations.First,a novelcontrol-oriented Six-Degree-Of-Freedom(6-DOF)UAV model considering airflow disturbancesis established for better consistency with the actual UAV system.Then,to achieve satisfactory per-formance in the approaching process,a Flexible Appointed-time Prescribed Performance Control(FAPPC)algorithm,with the features of user-specified time convergence,no overshoot,indepen-dence from the initial value,and singularity-free,is proposed.Specifically,to solve the singularityissue encountered by the existing PPC methods in dealing with sudden disturbances,an adaptiveadjustment signal is introduced in FAPPC to perceive the threat of increasing error and relax thepreset boundaries appropriately.Moreover,minimum learning parameter-based neural networkestimators are developed to approximate unknown lumped disturbances at a low computationalcost.Finally,the stability of the closed system is analyzed via Lyapunov synthesis,and the effective-ness and advantages of the proposed control scheme are demonstrated via simulation andHardware-In-the-Loop(HIL)experimental validation.
基金supported by SKKU Global Research Platform Research Fund,Sungkyunkwan University,2024-2025.
文摘Predicting player performance in sports is a critical challenge with significant implications for team success,fan engagement,and financial outcomes.Although,inMajor League Baseball(MLB),statistical methodologies such as sabermetrics have been widely used,the dynamic nature of sports makes accurate performance prediction a difficult task.Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions.This study addresses this challenge by employing the temporal fusion transformer(TFT),an advanced and cutting-edge deep learning model for complex data,to predict pitchers’earned run average(ERA),a key metric in baseball performance analysis.The performance of the TFT model is evaluated against recurrent neural network-based approaches and existing projection systems.In experimental results,the TFT based model consistently outperformed its counterparts,demonstrating superior accuracy in pitcher performance prediction.By leveraging the advanced capabilities of TFT,this study contributes to more precise player evaluations and improves strategic planning in baseball.
基金Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization(South China University of Technology)(2013A061401005)Research Fund(JMSWFW-2110-044)from Zhongshan Jiaming Electric Power Co.,Ltd.
文摘The determination of source-side extracted heating parameters is of great significance to the economic operation of cogeneration systems.This paper investigated the coupling performance of a cogeneration heating and power system multidimensionally based on the operating characteristics of the cogeneration units,the hydraulic and thermodynamic characteristics of the heating network,and the energy loads.Taking a steam network supported by a gas-steam combined cycle cogeneration system as the research case,the interaction effect among the source-side prime movers,the heating networks,and the terminal demand thermal parameters were investigated based on the designed values,the plant testing data,and the validated simulation.The operating maps of the gas-steam combined cycle cogeneration units were obtained using THERMOFLEX,and the minimum source-side steam parameters of the steam network were solved using an inverse solution procedure based on the hydro-thermodynamic coupling model.The cogeneration operating maps indicate that the available operating domain considerably narrows with the rise of the extraction steam pressure and flow rate.The heating network inverse solution demonstrates that the source-side steam pressure and temperature can be optimized from the originally designed 1.11 MPa and 238.8°C to 1.074 MPa and 191.15°C,respectively.Under the operating strategy with the minimum source-side heating parameters,the power peak regulation depth remarkably increases to 18.30%whereas the comprehensive thermal efficiency decreases.The operation under the minimum source-side heating steam parameters can be superior to the originally designed one in the economy at a higher price of the heating steam.At a fuel price of$0.38/kg and the power to fuel price of 0.18 kg/(kW·h),the critical price ratio of heating steam to fuel is 119.1 kg/t.The influence of the power-fuel price ratio on the economic deviation appears relatively weak.
基金supported by the National Key Research and Development Program of China (Grant No.2019YFB1803700)the Key Technologies Research and Development Program of Tianjin (Grant No.20YFZCGX00440).
文摘A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements.Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format,probabilistic shaping,roll-off factor,baud rate,optical signal-to-noise ratio,and chromatic dispersion.The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios.Furthermore,the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network.Compared with the designed VGG-based CNN,the MobileNet-based MTL does not need to train all the classes,and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy,indicating great potential in various monitoring scenarios.
基金Supported by the Natural Science Foundation of China(61076019)the China Postdoctoral Science Foundation(20100481134)+1 种基金the Natural Science Foundation of Jiangsu Province(BK2008387)the Graduate Student Innovation Foundation of Jiangsu Province(CX07B-105z)~~
文摘The network on chip（NoC）is used as a solution for the communication problems in a complex system on chip（SoC）design.To further enhance performances,the NoC architectures,a high level modeling and an evaluation method based on OPNET are proposed to analyze their performances on different injection rates and traffic patterns.Simulation results for general NoC in terms of the average latency and the throughput are analyzed and used as a guideline to make appropriate choices for a given application.Finally,a MPEG4 decoder is mapped on different NoC architectures.Results prove the effectiveness of the evaluation method.
基金The National Natural Science Foundation of China(No.60872004)the Research Fund of National Mobile Communications Research Laboratory of Southeast University(No.2010A08)the Fundamental Research Funds for the Central Universities(No.2009B21814)
文摘An analytical approach to evaluate the performance of the 3G/ad hoc integrated network is presented. A channel model capturing both path loss and shadowing is applied to the analysis so as to characterize power falloff vs. distance. The 3G/ad hoc integrated network scenario model is introduced briefly. Based on this model, several performances of the 3G/ ad hoc integrated network in terms of outage probability, call dropping probability and new call blocking probability are evaluated. The corresponding performance formulae are deduced in accordance with the analytical models. Meanwhile, the formula of the 3G/ad hoc integrated network capacity is deduced on the basis of the formula of the outage probability. It is observed from extensive simulation and numerical analysis that the 3G/ad hoc integrated network remarkably outperforms the 3G network with regards to the network performance. This derived evaluation approach can be applied into planning and optimization of the 3G/ad hoc network.
基金supported by the National Science and Technology Support Program (2014BAH24F01)the National Basic Research Program of China(2012CB3 15903)+3 种基金the Program for Key Science and Technology Innovation Team of Zhejiang Province (2011R50010-21,2013TD20)863 Program of China(2015AA015602,2015AA016103)the National Natural Science Foundation of China (61379118)the Fundamental Research Funds for the Central Universities
文摘The Open Flow implementations(SDNs) have been deployed increasingly on varieties of networks in research institutions as well as commercial institutions. To develop an Open Flow implementation, it is required to understand the performance of the network. A few benchmark tools(e.g., Cbench and OFlops) can be used to measure the network performance, while these tools take considerable time to simulate traffic behaviors and generate the required results,therefore extending the development time. In this paper, we present an analytical model, which is based on stochastic network calculus theory, for evaluating the performance of switch to controller.The previous studies show that stochastic network calculus can provide realistic emulation of real network traffic behaviors. Our model is evaluated by using both simulation tool and realistic testbed.The results show the stochastic network calculus based analysis model can realistically measure the network performance of the end-to-end properties between controller and switch.
文摘A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, roadhead ers are m echanical m iners w h ich have b een extensively u se d in tu n n elin g , m ining an d civil indu stries. Perform ance pred ictio n is an im p o rta n t issue for successful ro a d h e a d e r application andgenerally deals w ith m achine selection, p ro d u ctio n rate an d b it consu m p tio n . The m ain aim o f thisresearch is to investigate th e c u ttin g p erfo rm an ce （in stan tan eo u s c u ttin g rates （ICRs）） o f m ed iu m -d u tyro ad h ead ers by using artificial neural n etw o rk （ANN） approach. T here are d ifferent categories forANNs, b u t based o n train in g alg o rith m th e re are tw o m ain k in d s： supervised and u n su p erv ised . Them u lti-lay er p ercep tro n （MLP） an d K ohonen self-organizing feature m ap （KSOFM） are th e m o st w idelyused neu ral netw o rk s for supervised an d u n su p erv ised ones, respectively. For gaining this goal, ad atab ase w as prim arily provided from ro ad h e a d e rs＇ p erfo rm an ce an d geom echanical characteristics o frock form ations in tu n n els and d rift galleries in Tabas coal m ine, th e larg est an d th e only fullymech an ized coal m ine in Iran. T hen th e datab ase w as analyzed in o rd e r to yield th e m ost im p o rtan tfactor for ICR by using relatively im p o rta n t factor in w hich G arson eq u atio n w as utilized. The MLPn etw o rk w as train ed by 3 in p u t p ara m e te rs including rock m ass pro p erties, rock quality d esignation（RQD）, in tact rock p ro p erties such as uniaxial com pressive stre n g th （UCS） an d Brazilian ten sile stren g th（BTS）, and o n e o u tp u t p a ra m e te r （ICR）. In o rd e r to have m ore v alidation o n MLP o u tp u ts, KSOFM visualizationw as applied. The m ean square e rro r （MSE） an d regression coefficient （R ） o f MLP w e re found tobe 5.49 an d 0.97, respectively. M oreover, KSOFM n etw o rk has a m ap size o f 8 x 5 and final qu an tizatio nan d topographic erro rs w e re 0.383 an d 0.032, respectively. The results show th a t MLP neural n etw orkshave a strong capability to p red ict an d ev alu ate th e perfo rm an ce o f m ed iu m -d u ty ro ad h ead ers in coalm easu re rocks. Furtherm ore, it is concluded th a t KSOFM neural n etw o rk is an efficient w ay for u n d e rstand in g system beh av io r an d know ledge extraction. Finally, it is indicated th a t UCS has m ore influenceo n ICR b y applying th e b e st train ed MLP n etw o rk w eig h ts in G arson eq u atio n w h ich is also confirm ed byKSOFM.
基金supported by the National Natural Science Foundation of China (61401225, 61571234)the National Science Foundation of Jiangsu Province (BK20140894, BK20140883, BK20160899)+4 种基金the Six Talented Eminence Foundation of Jiangsu Province (XYDXXJS-044)the National Science Foundation of the Higher Education Institutions of Jiangsu Province (14KJD510007, 16KJB510035)the Jiangsu Planned Projects for Postdoctoral Research Funds (1501125B)China Postdoctoral Science Foundation funded project (2015M581844)the Introduction of Talent Scientific Research Fund of Nanjing University of Posts Telecommunications project (NY213104, NY214190)
文摘A K-tier uplink heterogeneous cellular network is modelled and analysed by accounting for both truncated channel inversion power control and biased user association. Each user has a maximum transmit power constraint and transmits data when it has sufficient transmit power to perform channel inversion. With biased user association, each user is associated with a base station(BS) that provides the maximum received power weighted by a bias factor, but not their nearest BS. Stochastic geometry is used to evaluate the performances of the proposed system model in terms of the outage probability and ergodic rate for each tier as functions of the biased and power control parameters. Simulations validate our analytical derivations. Numerical results show that there exists a trade-off introduced by the power cut-off threshold and the maximum user transmit power constraint. When the maximum user transmit power becomes a binding constraint, the overall performance is independent of BS densities. In addition, we have shown that it is beneficial for the outage and rate performances by optimizing different network parameters such as the power cut-off threshold as well as the biased factors.
基金Supported by the National Natural Science Foundation of China （20436040）.
文摘Water-using operations in the process industry have demands for water usually both on water quality and temperature, and the existing mathematical models of heat exchange networks cannot guarantee the energy performance of a water network optimal. In this paper, the effects of non-isothermal merging on energy performance of water allocation networks are analyzed, which include utility consumption, total heat exchange load, and number of heat exchange matches. Three principles are proposed to express the effects of non-isothermal merging on energy performance of water allocation networks. A rule of non-isothermal merging without increasing utility consumption is deduced. And an approach to improve energy performance of water allocation network is presented. A case study is given to demonstrate the method.
基金National Science Foundation of China(Nos.61673157,62076086,61972129 and 61702154)Key Research and Development Program in Anhui Province(Nos.202004d07020008 and 201904d07020010).
文摘Palmprint recognition and palm vein recognition are two emerging biometrics technologies.In the past two decades,many traditional methods have been proposed for palmprint recognition and palm vein recognition,and have achieved impressive results.However,the research on deep learningbased palmprint recognition and palm vein recognition is still very preliminary.In this paper,in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition indepth,we conduct performance evaluation of seventeen representative and classic convolutional neural networks(CNNs)on one 3D palmprint database,five 2D palmprint databases and two palm vein databases.A lot of experiments have been carried out in the conditions of different network structures,different learning rates,and different numbers of network layers.We have also conducted experiments on both separate data mode and mixed data mode.Experimental results show that these classic CNNs can achieve promising recognition results,and the recognition performance of recently proposed CNNs is better.Particularly,among classic CNNs,one of the recently proposed classic CNNs,i.e.,EfficientNet achieves the best recognition accuracy.However,the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.
基金supported by the Funds for Creative Research Groups of China(No.60821063)the State Key Program of National Natural Science of China(No.60534010)+3 种基金the National 973 Program of China(No.2009CB320604)the Funds of National Science of China(No.60674021,60804024)the 111 Project(No.B08015)the Funds of PhD program of MOE,China(No.20060145019)
文摘This paper studies the problems of H-infinity performance optimization and controller design for continuous-time NCSs with both sensor-to-controller and controller-to-actuator communication constraints （limited communication channels）. By taking the derivative character of network-induced delay into full consideration and defining new Lyapunov functions, linear matrix inequalities （LMIs）-based H-infinity performance optimization and controller design are presented for NCSs with limited communication channels. If there do not exist any constraints on the communication channels, the proposed design methods are also applicable. The merit of the proposed methods lies in their Jess conservativeness, which is achieved by avoiding the utilization of bounding inequalities for cross products of vectors. The simulation results illustrate the merit and effectiveness of the proposed H-infinity controller design for NCSs with limited communication channels.
基金supported in part by the National Natural Science Foundation of China (No. 91638205, 91438206, 61771286, 61621091)
文摘With rapid development of unmanned aerial vehicles(UAVs), more and more UAVs access satellite networks for data transmission. To improve the spectral efficiency, non-orthogonal multiple access(NOMA) is adopted to integrate UAVs into the satellite network, where multiple satellites cooperatively serve the UAVs and mobile terminal using the Ku-band and above. Taking into account the rain fading and the fading correlation, the outage performance is first analytically obtained for fixed power allocation and then efficiently calculated by the proposed power allocation algorithm to guarantee the user fairness. Simulation results verify the outage performance analysis and show the performance improvement of the proposed power allocation scheme.
基金support from the Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications(BHRC-2024-1)Beijing Jiaotong University,the National Natural Science Foundation of China(U21A20445).
文摘In High-Speed Railways(HSRs),the Train Control and Management System(TCMS)plays a crucial role.However,as the demand for train networks grows,the limitations of traditional wired connections have become apparent.This paper designs and implements a Wireless Train Communication Network(WTCN)to enhance the existing train network infrastructure.To address the challenges that wireless communication technology faces in the unique environment of high-speed rail,this study first analyzes various onboard environments and simulates several typical scenarios in the laboratory.Integrating the specific application scenarios and service characteristics of the high-speed train control network,we conduct measurements and validations of WiFi performance,exploring the specific impacts of different factors on throughput and delay.
