Considering the large diameter effect of piles,the influence of different pile-soil analysis methods on the design of monopile foundations for offshore wind turbines has become an urgent problem to be solved.Three dif...Considering the large diameter effect of piles,the influence of different pile-soil analysis methods on the design of monopile foundations for offshore wind turbines has become an urgent problem to be solved.Three different pile-soil models were used to study a large 10 MW monopile wind turbine.By modeling the three models in the SACS software,this paper analyzed the motion response of the overall structure under the conditions of wind and waves.According to the given working conditions,this paper concludes that under the condition of independent wind,the average value of the tower top x-displacement of the rigid connection method is the smalle st,and the standard deviation is the smallest under the condition of independent wave.The results obtained by the p-y curve method are the most conservative.展开更多
This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly m...This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced modules.Each module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength,while different modules are separated by the relatively large inter-module distance for convenience of practical deployment.By accurately modelling the signal amplitudes and phases,as well as projected apertures across all modular elements,we analyse the near-field signal-to-noise ratio(SNR)performance for modular XL-array communications.Based on the non-uniform spherical wave(NUSW)modelling,the closed-form SNR expression is derived in terms of key system parameters,such as the overall modular array size,distances of adjacent modules along all dimensions,and the user's three-dimensional(3D)location.In addition,with the number of modules in different dimensions increasing infinitely,the asymptotic SNR scaling laws are revealed.Furthermore,we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases,e.g.,the collocated XL-array architecture,the uniform plane wave(UPW)based far-field modelling,and the modular extremely large-scale uniform linear array(XL-ULA)of onedimension.Extensive simulation results are presented to validate our findings.展开更多
Radio links are extensively used for voice and data communications at long distance. We analyze the radio propagation parameters that affect the received signal level on radio links in Rwanda and we determine the best...Radio links are extensively used for voice and data communications at long distance. We analyze the radio propagation parameters that affect the received signal level on radio links in Rwanda and we determine the best path loss model for prediction of the received signal level. Various models of propagation and the mathematical expressions of path loss are described here in order to come to the prediction of those propagation effects. By analyzing data collected for two links of MTN Rwanda: Gahengeri-Kibungo and Gahengeri-Jali, we find that the best predicting model is the normal distribution.展开更多
A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a force...A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.展开更多
The streamflow over the Yellow River basin is simulated using the PRECIS (Providing REgional Climates for Impacts Studies) regional climate model driven by 15-year (1979-1993) ECMWF reanalysis data as the initial ...The streamflow over the Yellow River basin is simulated using the PRECIS (Providing REgional Climates for Impacts Studies) regional climate model driven by 15-year (1979-1993) ECMWF reanalysis data as the initial and lateral boundary conditions and an off-line large-scale routing model (LRM). The LRM uses physical catchment and river channel information and allows streamflow to be predicted for large continental rivers with a 1°×1° spatial resolution. The results show that the PRECIS model can reproduce the general southeast to northwest gradient distribution of the precipitation over the Yellow River basin, The PRECIS- LRM model combination has the capability to simulate the seasonal and annual streamflow over the Yellow River basin. The simulated streamflow is generally coincident with the naturalized streamflow both in timing and in magnitude.展开更多
In relatively coarse-resolution atmospheric models,cumulus parameterization helps account for the effect of subgridscale convection,which produces supplemental rainfall to the grid-scale precipitation and impacts the ...In relatively coarse-resolution atmospheric models,cumulus parameterization helps account for the effect of subgridscale convection,which produces supplemental rainfall to the grid-scale precipitation and impacts the diurnal cycle of precipitation.In this study,the diurnal cycle of precipitation was studied using the new simplified Arakawa-Schubert scheme in a global non-hydrostatic atmospheric model,i.e.,the Yin-Yang-grid Unified Model for the Atmosphere.Two new diagnostic closures and a convective trigger function were suggested to emphasize the job of the cloud work function corresponding to the free tropospheric large-scale forcing.Numerical results of the 0.25-degree model in 3-month batched real-case simulations revealed an improvement in the diurnal precipitation variation by using a revised trigger function with an enhanced dynamical constraint on the convective initiation and a suitable threshold of the trigger.By reducing the occurrence of convection during peak solar radiation hours,the revised scheme was shown to be effective in delaying the appearance of early-afternoon rainfall peaks over most land areas and accentuating the nocturnal peaks that were wrongly concealed by the more substantial afternoon peak.In addition,the revised scheme enhanced the simulation capability of the precipitation probability density function,such as increasing the extremely low-and high-intensity precipitation events and decreasing small and moderate rainfall events,which contributed to the reduction of precipitation bias over mid-latitude and tropical land areas.展开更多
Model Order Reduction (MOR) plays more and more imp or tant role in complex system simulation, design and control recently. For example , for the large-size space structures, VLSI and MEMS (Micro-ElectroMechanical Sys...Model Order Reduction (MOR) plays more and more imp or tant role in complex system simulation, design and control recently. For example , for the large-size space structures, VLSI and MEMS (Micro-ElectroMechanical Systems) etc., in order to shorten the development cost, increase the system co ntrolling accuracy and reduce the complexity of controllers, the reduced order model must be constructed. Even in Virtual Reality (VR), the simulation and d isplay must be in real-time, the model order must be reduced too. The recent advances of MOR research are overviewed in the article. The MOR theor y and methods may be classified as Singular Value decomposition (SVD) based, the Krylov subspace based and others. The merits and demerits of the different meth ods are analyzed, and the existed problems are pointed out. Moreover, the applic ation’s fields are overviewed, and the potential applications are forecaste d. After the existed problems analyzed, the future work is described. There are som e problems in the traditional methods such as SVD and Krylov subspace, they are that it’s difficult to (1)guarantee the stability of the original system, (2) b e adaptive to nonlinear system, and (3) control the modeling accuracy. The f uture works may be solving the above problems on the foundation of the tradition al methods, and applying other methods such as wavelet or signal compression.展开更多
As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerou...As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.展开更多
Wind energy has been rapidly developed in China during the past decades and the installed capacity has been the largest in the world. In the future, utilization of wind power is still expected to carry out in China ma...Wind energy has been rapidly developed in China during the past decades and the installed capacity has been the largest in the world. In the future, utilization of wind power is still expected to carry out in China mainly with a large-scale centralized layout. Here, we examine the potential climatic impacts of large-scale windfarms associated with deployment scale in China using numerical experiments, in which four deployment scenarios were designed. These four scenarios represented relatively small- (484 GW), medium- (2165 GW) and large-scale (3490 GW and 5412 GW) installed wind power capacities, respectively. Results showed that turbulent kinetic energy, wind velocity, and air temperature varied consistently within those windfarms with the largest changes in turbine hub heights. Moreover, the above relatively large- scale windfarms could induce regional wanning with a maximum of above 0.8 °C in North China. This regional warming may be linked to an anomalous circulation pattern with a negative pressure anomaly center in Northeast China and a positive pressure anomaly center in the middle and lower reaches of the Yangtze-Huaihe River Basin.展开更多
BACKGROUND Large-scale functional connectivity(LSFC)patterns in the brain have unique intrinsic characteristics.Abnormal LSFC patterns have been found in patients with dementia,as well as in those with mild cognitive ...BACKGROUND Large-scale functional connectivity(LSFC)patterns in the brain have unique intrinsic characteristics.Abnormal LSFC patterns have been found in patients with dementia,as well as in those with mild cognitive impairment(MCI),and these patterns predicted their cognitive performance.It has been reported that patients with type 2 diabetes mellitus(T2DM)may develop MCI that could progress to dementia.We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM,using connectome-based predictive modeling(CPM)and a support vector machine.AIM To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.METHODS Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls.Cognitive function was assessed using the Montreal Cognitive Assessment(MoCA).Patients with T2DM were divided into two groups,according to the presence(T2DM-C;n=16)or absence(T2DM-NC;n=26)of MCI.Brain regions were marked using Harvard Oxford(HOA-112),automated anatomical labeling(AAL-116),and 264-region functional(Power-264)atlases.LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique.Subsequently,we used a support vector machine based on LSFC patterns for among-group differentiation.The area under the receiver operating characteristic curve determined the appearance of the classification.RESULTS CPM could predict the MoCA scores in patients with T2DM(Pearson’s correlation coefficient between predicted and actual MoCA scores,r=0.32,P=0.0066[HOA-112 atlas];r=0.32,P=0.0078[AAL-116 atlas];r=0.42,P=0.0038[Power-264 atlas]),indicating that LSFC patterns represent cognition-level measures in these patients.Positive(anti-correlated)LSFC networks based on the Power-264 atlas showed the best predictive performance;moreover,we observed new brain regions of interest associated with T2DM-related cognition.The area under the receiver operating characteristic curve values(T2DM-NC group vs.T2DM-C group)were 0.65-0.70,with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value(0.70).Most discriminative and attractive LSFCs were related to the default mode network,limbic system,and basal ganglia.CONCLUSION LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.展开更多
Large-scale atmospheric information plays an important role in the regional model for the forecasts of weather such as tropical cyclone(TC).However,it is difficult to be fully represented in regional models due to dom...Large-scale atmospheric information plays an important role in the regional model for the forecasts of weather such as tropical cyclone(TC).However,it is difficult to be fully represented in regional models due to domain size and a lack of observation data,particularly at sea used in regional data assimilation.Blending analysis has been developed and implemented in regional models to reintroduce large-scale information from global model to regional analysis.Research of the impact of this large-scale blending scheme for the Global/Regional Assimilation and PrEdiction System(CMA-MESO)regional model on TC forecasting is limited and this study attempts to further progress by examining the adaptivity of the blending scheme using the two-dimensional Discrete Cosine Transform(2D-DCT)filter on the model forecast of Typhoon Haima over Shenzhen,China in 2016 and considering various cut-off wavelengths.Results showed that the error of the 24-hour typhoon track forecast can be reduced to less than 25 km by applying the scale-dependent blending scheme,indicating that the blending analysis is effectively able to minimise the large-scale bias for the initial fields.The improvement of the wind forecast is more evident for u-wind component according to the reduced root mean square errors(RMSEs)by comparing the experiments with and without blending analysis.Furthermore,the higher equitable threat score(ETS)provided implications that the precipitation prediction skills were increased in the 24h forecast by improving the representation of the large-scale feature in the CMA-MESO analysis.Furthermore,significant differences of the track error forecast were found by applying the blending analysis with different cut-off wavelengths from 400 km to 1200 km and the track error can be reduced less than by 10 km with 400 km cut-off wavelength in the first 6h forecast.It highlighted that the blending scheme with dynamic cut-off wavelengths adapted to the development of different TC systems is necessary in order to optimally introduce and ingest the large-scale information from global model to the regional model for improving the TC forecast.In this paper,the methods and data applied in this study will be firstly introduced,before discussion of the results regarding the performance of the blending analysis and its impacts on the wind and precipitation forecast correspondingly,followed by the discussion of the effects of different blending scheme on TC forecasts and the conclusion section.展开更多
The society in the digital transformation era demands new decision schemes such as e-democracy or based on social media.Such novel decision schemes require the participation of many experts/decision makers/stakeholder...The society in the digital transformation era demands new decision schemes such as e-democracy or based on social media.Such novel decision schemes require the participation of many experts/decision makers/stakeholders in the decision processes.As a result,large-scale group decision making(LSGDM)has attracted the attention of many researchers in the last decade and many studies have been conducted in order to face the challenges associated with the topic.Therefore,this paper aims at reviewing the most relevant studies about LSGDM,identifying the most profitable research trends and analyzing them from a critical point of view.To do so,the Web of Science database has been consulted by using different searches.From these results a total of 241 contributions were found and a selection process regarding language,type of contribution and actual relation with the studied topic was then carried out.The 87 contributions finally selected for this review have been analyzed from four points of view that have been highly remarked in the topic,such as the preference structure in which decision-makers’opinions are modeled,the group decision rules used to define the decision making process,the techniques applied to verify the quality of these models and their applications to real world problems solving.Afterwards,a critical analysis of the main limitations of the existing proposals is developed.Finally,taking into account these limitations,new research lines for LSGDM are proposed and the main challenges are stressed out.展开更多
A reduction in network energy consumption and the establishment of green networks have become key scientific problems in academic and industrial research.Existing energy efficiency schemes are based on a known traffic...A reduction in network energy consumption and the establishment of green networks have become key scientific problems in academic and industrial research.Existing energy efficiency schemes are based on a known traffic matrix,and acquiring a real-time traffic matrix in current complex networks is difficult.Therefore,this research investigates how to reduce network energy consumption without a real-time traffic matrix.In particular,this paper proposes an intra-domain energy-efficient routing scheme based on multipath routing.It analyzes the relationship between routing availability and energy-efficient routing and integrates the two mechanisms to satisfy the requirements of availability and energy efficiency.The main research focus is as follows:(1)A link criticality model is evaluated to quantitatively measure the importance of links in a network.(2)On the basis of the link criticality model,this paper analyzes an energy-efficient routing technology based on multipath routing to achieve the goals of availability and energy efficiency simultaneously.(3)An energy-efficient routing algorithm based on multipath routing in large-scale networks is proposed.(4)The proposed method does not require a real-time traffic matrix in the network and is thus easy to apply in practice.(5)The proposed algorithm is verified in several network topologies.Experimental results show that the algorithm can not only reduce network energy consumption but can also ensure routing availability.展开更多
Time-delays,due to the information transmission between subsystems,naturally exist in large-scale systems and the existence of the delay is frequently a source of instability. This paper considers the problems of robu...Time-delays,due to the information transmission between subsystems,naturally exist in large-scale systems and the existence of the delay is frequently a source of instability. This paper considers the problems of robust non-fragile fuzzy control for a class of uncertain discrete nonlinear large-scale systems with time-delay and controller gain perturbations described by T-S fuzzy model. An equivalent T-S fuzzy model is represented for discrete-delay nonlinear large-scale systems. A sufficient condition for the existence of such non-fragile controllers is further derived via the Lyapunov function and the linear matrix inequality( LMI) approach. Simulation results demonstrate the feasibility and the effectiveness of the proposed design and the proper stabilization of the system in spite of controller gain variations and uncertainties.展开更多
Intra-body communication(IBC)is a novel short-range non-RF(radio frequency)wire-less communication technique specified by the IEEE 802.15.6 using the human body as a transmis-sion medium.In this work,a new modeling me...Intra-body communication(IBC)is a novel short-range non-RF(radio frequency)wire-less communication technique specified by the IEEE 802.15.6 using the human body as a transmis-sion medium.In this work,a new modeling method of the IBC system based on the composite fad-ing channel is proposed,where the cascaded filter is used to express the composite fading channel and the modulation method in transmitter and receiver.The composite fading channel combines with the average attenuation,group delay,multipath effect,and shadowing effect.The modulation is adopted orthogonal frequency division multiplexing(OFDM),and thereby the constellation,cyc-lic prefix,bit error rate,and pilot mode are determined.As a result,the whole process and multi-parameter simulation of IBC system can be achieved.It provides a theoretical foundation for the system design of the intra-body communication and will promote its application to the wireless body area network(WBAN).展开更多
A continuous-time fuzzy large-scale system F consists of some interconnected Takagi-Sugeno fuzzy subsystems. Two sufficient conditions for the asymptotic stability of this system (namely, theorem 1 and theorem 2) are...A continuous-time fuzzy large-scale system F consists of some interconnected Takagi-Sugeno fuzzy subsystems. Two sufficient conditions for the asymptotic stability of this system (namely, theorem 1 and theorem 2) are derived via a multiple Lyapunov function approach. In theorem 1, the information of membership functions of fuzzy rules should be known in order to analyze the stability of F. But in general this information is not easy to be acquired for their time-varying property. So theorem 2 is provided to judge the asymptotic stability of F, based on which there is no need to know the information of membership functions in stability analysis. Finally, a numerical example is given to show the utility of the method proposed in this paper.展开更多
A modified shifting bottleneck algorithm was proposed to solve scheduling problems of a large-scale job shop.Firstly,a new structured algorithm was employed for sub-problems so as to reduce the computational burden an...A modified shifting bottleneck algorithm was proposed to solve scheduling problems of a large-scale job shop.Firstly,a new structured algorithm was employed for sub-problems so as to reduce the computational burden and suit for large-scale instances more effectively.The modified cycle avoidance method,incorporating with the disjunctive graph model and topological sort algorithm,was applied to guaranteeing the feasibility of solutions with considering delayed precedence constraints.Finally,simulation experiments were carried out to verify the feasibility and effectiveness of the modified method.The results demonstrate that the proposed algorithm can solve the large-scale job shop scheduling problems(JSSPs) within a reasonable period of time and obtaining satisfactory solutions simultaneously.展开更多
Social media data created a paradigm shift in assessing situational awareness during a natural disaster or emergencies such as wildfire, hurricane, tropical storm etc. Twitter as an emerging data source is an effectiv...Social media data created a paradigm shift in assessing situational awareness during a natural disaster or emergencies such as wildfire, hurricane, tropical storm etc. Twitter as an emerging data source is an effective and innovative digital platform to observe trend from social media users’ perspective who are direct or indirect witnesses of the calamitous event. This paper aims to collect and analyze twitter data related to the recent wildfire in California to perform a trend analysis by classifying firsthand and credible information from Twitter users. This work investigates tweets on the recent wildfire in California and classifies them based on witnesses into two types: 1) direct witnesses and 2) indirect witnesses. The collected and analyzed information can be useful for law enforcement agencies and humanitarian organizations for communication and verification of the situational awareness during wildfire hazards. Trend analysis is an aggregated approach that includes sentimental analysis and topic modeling performed through domain-expert manual annotation and machine learning. Trend analysis ultimately builds a fine-grained analysis to assess evacuation routes and provide valuable information to the firsthand emergency responders<span style="font-family:Verdana;">.</span>展开更多
基金financially supported by the Open Research Fund of Hunan Provincial Key Laboratory of Key Technology on Hydropower Development (Grant No.PKLHD202003)the National Natural Science Foundation of China (Grant Nos.52071058 and 51939002)+1 种基金the National Natural Science Foundation of Liaoning Province (Grant No.2022-KF-18-01)Fundamental Research Funds for the Central University (Grant No.DUT20ZD219)。
文摘Considering the large diameter effect of piles,the influence of different pile-soil analysis methods on the design of monopile foundations for offshore wind turbines has become an urgent problem to be solved.Three different pile-soil models were used to study a large 10 MW monopile wind turbine.By modeling the three models in the SACS software,this paper analyzed the motion response of the overall structure under the conditions of wind and waves.According to the given working conditions,this paper concludes that under the condition of independent wind,the average value of the tower top x-displacement of the rigid connection method is the smalle st,and the standard deviation is the smallest under the condition of independent wave.The results obtained by the p-y curve method are the most conservative.
基金supported by the National Key R&D Program of China with Grant number 2019YFB1803400the National Natural Science Foundation of China under Grant number 62071114the Fundamental Research Funds for the Central Universities of China under grant numbers 3204002004A2 and 2242022k30005。
文摘This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced modules.Each module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength,while different modules are separated by the relatively large inter-module distance for convenience of practical deployment.By accurately modelling the signal amplitudes and phases,as well as projected apertures across all modular elements,we analyse the near-field signal-to-noise ratio(SNR)performance for modular XL-array communications.Based on the non-uniform spherical wave(NUSW)modelling,the closed-form SNR expression is derived in terms of key system parameters,such as the overall modular array size,distances of adjacent modules along all dimensions,and the user's three-dimensional(3D)location.In addition,with the number of modules in different dimensions increasing infinitely,the asymptotic SNR scaling laws are revealed.Furthermore,we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases,e.g.,the collocated XL-array architecture,the uniform plane wave(UPW)based far-field modelling,and the modular extremely large-scale uniform linear array(XL-ULA)of onedimension.Extensive simulation results are presented to validate our findings.
文摘Radio links are extensively used for voice and data communications at long distance. We analyze the radio propagation parameters that affect the received signal level on radio links in Rwanda and we determine the best path loss model for prediction of the received signal level. Various models of propagation and the mathematical expressions of path loss are described here in order to come to the prediction of those propagation effects. By analyzing data collected for two links of MTN Rwanda: Gahengeri-Kibungo and Gahengeri-Jali, we find that the best predicting model is the normal distribution.
基金supported by the Ministry of Trade,Industry & Energy(MOTIE,Korea) under Industrial Technology Innovation Program (No.10063424,'development of distant speech recognition and multi-task dialog processing technologies for in-door conversational robots')
文摘A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.
文摘The streamflow over the Yellow River basin is simulated using the PRECIS (Providing REgional Climates for Impacts Studies) regional climate model driven by 15-year (1979-1993) ECMWF reanalysis data as the initial and lateral boundary conditions and an off-line large-scale routing model (LRM). The LRM uses physical catchment and river channel information and allows streamflow to be predicted for large continental rivers with a 1°×1° spatial resolution. The results show that the PRECIS model can reproduce the general southeast to northwest gradient distribution of the precipitation over the Yellow River basin, The PRECIS- LRM model combination has the capability to simulate the seasonal and annual streamflow over the Yellow River basin. The simulated streamflow is generally coincident with the naturalized streamflow both in timing and in magnitude.
基金supported by the National Natural Science Foundation of China(Grant Nos.42375153,42075151).
文摘In relatively coarse-resolution atmospheric models,cumulus parameterization helps account for the effect of subgridscale convection,which produces supplemental rainfall to the grid-scale precipitation and impacts the diurnal cycle of precipitation.In this study,the diurnal cycle of precipitation was studied using the new simplified Arakawa-Schubert scheme in a global non-hydrostatic atmospheric model,i.e.,the Yin-Yang-grid Unified Model for the Atmosphere.Two new diagnostic closures and a convective trigger function were suggested to emphasize the job of the cloud work function corresponding to the free tropospheric large-scale forcing.Numerical results of the 0.25-degree model in 3-month batched real-case simulations revealed an improvement in the diurnal precipitation variation by using a revised trigger function with an enhanced dynamical constraint on the convective initiation and a suitable threshold of the trigger.By reducing the occurrence of convection during peak solar radiation hours,the revised scheme was shown to be effective in delaying the appearance of early-afternoon rainfall peaks over most land areas and accentuating the nocturnal peaks that were wrongly concealed by the more substantial afternoon peak.In addition,the revised scheme enhanced the simulation capability of the precipitation probability density function,such as increasing the extremely low-and high-intensity precipitation events and decreasing small and moderate rainfall events,which contributed to the reduction of precipitation bias over mid-latitude and tropical land areas.
文摘Model Order Reduction (MOR) plays more and more imp or tant role in complex system simulation, design and control recently. For example , for the large-size space structures, VLSI and MEMS (Micro-ElectroMechanical Systems) etc., in order to shorten the development cost, increase the system co ntrolling accuracy and reduce the complexity of controllers, the reduced order model must be constructed. Even in Virtual Reality (VR), the simulation and d isplay must be in real-time, the model order must be reduced too. The recent advances of MOR research are overviewed in the article. The MOR theor y and methods may be classified as Singular Value decomposition (SVD) based, the Krylov subspace based and others. The merits and demerits of the different meth ods are analyzed, and the existed problems are pointed out. Moreover, the applic ation’s fields are overviewed, and the potential applications are forecaste d. After the existed problems analyzed, the future work is described. There are som e problems in the traditional methods such as SVD and Krylov subspace, they are that it’s difficult to (1)guarantee the stability of the original system, (2) b e adaptive to nonlinear system, and (3) control the modeling accuracy. The f uture works may be solving the above problems on the foundation of the tradition al methods, and applying other methods such as wavelet or signal compression.
基金This work was supported by National Key R&D Program of China under Grant 2018YFB1800802in part by the National Natural Science Foundation of China under Grant No.61771488,No.61631020 and No.61827801+1 种基金in part by State Key Laboratory of Air Traffic Management System and Technology under Grant No.SKLATM201808in part by Postgraduate Research and Practice Innovation Program of Jiangsu Province under No.KYCX190188.
文摘As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.
基金s We acknowledged the financial support of the National Key Research and Development Program of China (2018YFB1502803), the National Natural Science Foundation of China (41475066), and Tsinghua University Initiative Sci entific Research Program (20131089357, 20131089356).
文摘Wind energy has been rapidly developed in China during the past decades and the installed capacity has been the largest in the world. In the future, utilization of wind power is still expected to carry out in China mainly with a large-scale centralized layout. Here, we examine the potential climatic impacts of large-scale windfarms associated with deployment scale in China using numerical experiments, in which four deployment scenarios were designed. These four scenarios represented relatively small- (484 GW), medium- (2165 GW) and large-scale (3490 GW and 5412 GW) installed wind power capacities, respectively. Results showed that turbulent kinetic energy, wind velocity, and air temperature varied consistently within those windfarms with the largest changes in turbine hub heights. Moreover, the above relatively large- scale windfarms could induce regional wanning with a maximum of above 0.8 °C in North China. This regional warming may be linked to an anomalous circulation pattern with a negative pressure anomaly center in Northeast China and a positive pressure anomaly center in the middle and lower reaches of the Yangtze-Huaihe River Basin.
基金Supported by the National Natural Science Foundation of China,No.81771815.
文摘BACKGROUND Large-scale functional connectivity(LSFC)patterns in the brain have unique intrinsic characteristics.Abnormal LSFC patterns have been found in patients with dementia,as well as in those with mild cognitive impairment(MCI),and these patterns predicted their cognitive performance.It has been reported that patients with type 2 diabetes mellitus(T2DM)may develop MCI that could progress to dementia.We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM,using connectome-based predictive modeling(CPM)and a support vector machine.AIM To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.METHODS Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls.Cognitive function was assessed using the Montreal Cognitive Assessment(MoCA).Patients with T2DM were divided into two groups,according to the presence(T2DM-C;n=16)or absence(T2DM-NC;n=26)of MCI.Brain regions were marked using Harvard Oxford(HOA-112),automated anatomical labeling(AAL-116),and 264-region functional(Power-264)atlases.LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique.Subsequently,we used a support vector machine based on LSFC patterns for among-group differentiation.The area under the receiver operating characteristic curve determined the appearance of the classification.RESULTS CPM could predict the MoCA scores in patients with T2DM(Pearson’s correlation coefficient between predicted and actual MoCA scores,r=0.32,P=0.0066[HOA-112 atlas];r=0.32,P=0.0078[AAL-116 atlas];r=0.42,P=0.0038[Power-264 atlas]),indicating that LSFC patterns represent cognition-level measures in these patients.Positive(anti-correlated)LSFC networks based on the Power-264 atlas showed the best predictive performance;moreover,we observed new brain regions of interest associated with T2DM-related cognition.The area under the receiver operating characteristic curve values(T2DM-NC group vs.T2DM-C group)were 0.65-0.70,with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value(0.70).Most discriminative and attractive LSFCs were related to the default mode network,limbic system,and basal ganglia.CONCLUSION LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.
基金Project of Shenzhen Science and Technology Innovation Commission(KCXFZ20201221173610028)。
文摘Large-scale atmospheric information plays an important role in the regional model for the forecasts of weather such as tropical cyclone(TC).However,it is difficult to be fully represented in regional models due to domain size and a lack of observation data,particularly at sea used in regional data assimilation.Blending analysis has been developed and implemented in regional models to reintroduce large-scale information from global model to regional analysis.Research of the impact of this large-scale blending scheme for the Global/Regional Assimilation and PrEdiction System(CMA-MESO)regional model on TC forecasting is limited and this study attempts to further progress by examining the adaptivity of the blending scheme using the two-dimensional Discrete Cosine Transform(2D-DCT)filter on the model forecast of Typhoon Haima over Shenzhen,China in 2016 and considering various cut-off wavelengths.Results showed that the error of the 24-hour typhoon track forecast can be reduced to less than 25 km by applying the scale-dependent blending scheme,indicating that the blending analysis is effectively able to minimise the large-scale bias for the initial fields.The improvement of the wind forecast is more evident for u-wind component according to the reduced root mean square errors(RMSEs)by comparing the experiments with and without blending analysis.Furthermore,the higher equitable threat score(ETS)provided implications that the precipitation prediction skills were increased in the 24h forecast by improving the representation of the large-scale feature in the CMA-MESO analysis.Furthermore,significant differences of the track error forecast were found by applying the blending analysis with different cut-off wavelengths from 400 km to 1200 km and the track error can be reduced less than by 10 km with 400 km cut-off wavelength in the first 6h forecast.It highlighted that the blending scheme with dynamic cut-off wavelengths adapted to the development of different TC systems is necessary in order to optimally introduce and ingest the large-scale information from global model to the regional model for improving the TC forecast.In this paper,the methods and data applied in this study will be firstly introduced,before discussion of the results regarding the performance of the blending analysis and its impacts on the wind and precipitation forecast correspondingly,followed by the discussion of the effects of different blending scheme on TC forecasts and the conclusion section.
基金supported by the Spanish Ministry of Economy and Competitiveness through the Spanish National Project PGC2018-099402-B-I00the Postdoctoral fellow Ramón y Cajal(RYC-2017-21978)+6 种基金the FEDER-UJA project 1380637ERDF,the Spanish Ministry of Science,Innovation and Universities through a Formación de Profesorado Universitario(FPU2019/01203)grantthe Junta de Andalucía,Andalusian Plan for Research,Development,and Innovation(POSTDOC 21-00461)the National Natural Science Foundation of China(61300167,61976120)the Natural Science Foundation of Jiangsu Province(BK20191445)the Natural Science Key Foundation of Jiangsu Education Department(21KJA510004)Qing Lan Project of Jiangsu Province。
文摘The society in the digital transformation era demands new decision schemes such as e-democracy or based on social media.Such novel decision schemes require the participation of many experts/decision makers/stakeholders in the decision processes.As a result,large-scale group decision making(LSGDM)has attracted the attention of many researchers in the last decade and many studies have been conducted in order to face the challenges associated with the topic.Therefore,this paper aims at reviewing the most relevant studies about LSGDM,identifying the most profitable research trends and analyzing them from a critical point of view.To do so,the Web of Science database has been consulted by using different searches.From these results a total of 241 contributions were found and a selection process regarding language,type of contribution and actual relation with the studied topic was then carried out.The 87 contributions finally selected for this review have been analyzed from four points of view that have been highly remarked in the topic,such as the preference structure in which decision-makers’opinions are modeled,the group decision rules used to define the decision making process,the techniques applied to verify the quality of these models and their applications to real world problems solving.Afterwards,a critical analysis of the main limitations of the existing proposals is developed.Finally,taking into account these limitations,new research lines for LSGDM are proposed and the main challenges are stressed out.
基金supported by the Program of Hainan Association for Science and Technology Plans to Youth R&D Innovation(QCXM201910)the National Natural Science Foundation of China(Nos.61702315,61802092)+1 种基金the Applied Basic Research Plan of Shanxi Province(No.2201901D211168)the Key R&D Program(International Science and Technology Cooperation Project)of Shanxi Province China(No.201903D421003).
文摘A reduction in network energy consumption and the establishment of green networks have become key scientific problems in academic and industrial research.Existing energy efficiency schemes are based on a known traffic matrix,and acquiring a real-time traffic matrix in current complex networks is difficult.Therefore,this research investigates how to reduce network energy consumption without a real-time traffic matrix.In particular,this paper proposes an intra-domain energy-efficient routing scheme based on multipath routing.It analyzes the relationship between routing availability and energy-efficient routing and integrates the two mechanisms to satisfy the requirements of availability and energy efficiency.The main research focus is as follows:(1)A link criticality model is evaluated to quantitatively measure the importance of links in a network.(2)On the basis of the link criticality model,this paper analyzes an energy-efficient routing technology based on multipath routing to achieve the goals of availability and energy efficiency simultaneously.(3)An energy-efficient routing algorithm based on multipath routing in large-scale networks is proposed.(4)The proposed method does not require a real-time traffic matrix in the network and is thus easy to apply in practice.(5)The proposed algorithm is verified in several network topologies.Experimental results show that the algorithm can not only reduce network energy consumption but can also ensure routing availability.
文摘Time-delays,due to the information transmission between subsystems,naturally exist in large-scale systems and the existence of the delay is frequently a source of instability. This paper considers the problems of robust non-fragile fuzzy control for a class of uncertain discrete nonlinear large-scale systems with time-delay and controller gain perturbations described by T-S fuzzy model. An equivalent T-S fuzzy model is represented for discrete-delay nonlinear large-scale systems. A sufficient condition for the existence of such non-fragile controllers is further derived via the Lyapunov function and the linear matrix inequality( LMI) approach. Simulation results demonstrate the feasibility and the effectiveness of the proposed design and the proper stabilization of the system in spite of controller gain variations and uncertainties.
基金the National Natural Sci-ence Foundation of China(No.81671787)the Defense Industrial Technology Development Program(No.JCKY2016208B001).
文摘Intra-body communication(IBC)is a novel short-range non-RF(radio frequency)wire-less communication technique specified by the IEEE 802.15.6 using the human body as a transmis-sion medium.In this work,a new modeling method of the IBC system based on the composite fad-ing channel is proposed,where the cascaded filter is used to express the composite fading channel and the modulation method in transmitter and receiver.The composite fading channel combines with the average attenuation,group delay,multipath effect,and shadowing effect.The modulation is adopted orthogonal frequency division multiplexing(OFDM),and thereby the constellation,cyc-lic prefix,bit error rate,and pilot mode are determined.As a result,the whole process and multi-parameter simulation of IBC system can be achieved.It provides a theoretical foundation for the system design of the intra-body communication and will promote its application to the wireless body area network(WBAN).
文摘A continuous-time fuzzy large-scale system F consists of some interconnected Takagi-Sugeno fuzzy subsystems. Two sufficient conditions for the asymptotic stability of this system (namely, theorem 1 and theorem 2) are derived via a multiple Lyapunov function approach. In theorem 1, the information of membership functions of fuzzy rules should be known in order to analyze the stability of F. But in general this information is not easy to be acquired for their time-varying property. So theorem 2 is provided to judge the asymptotic stability of F, based on which there is no need to know the information of membership functions in stability analysis. Finally, a numerical example is given to show the utility of the method proposed in this paper.
基金National Natural Science Foundations of China(Nos.71471135,61273035)
文摘A modified shifting bottleneck algorithm was proposed to solve scheduling problems of a large-scale job shop.Firstly,a new structured algorithm was employed for sub-problems so as to reduce the computational burden and suit for large-scale instances more effectively.The modified cycle avoidance method,incorporating with the disjunctive graph model and topological sort algorithm,was applied to guaranteeing the feasibility of solutions with considering delayed precedence constraints.Finally,simulation experiments were carried out to verify the feasibility and effectiveness of the modified method.The results demonstrate that the proposed algorithm can solve the large-scale job shop scheduling problems(JSSPs) within a reasonable period of time and obtaining satisfactory solutions simultaneously.
文摘Social media data created a paradigm shift in assessing situational awareness during a natural disaster or emergencies such as wildfire, hurricane, tropical storm etc. Twitter as an emerging data source is an effective and innovative digital platform to observe trend from social media users’ perspective who are direct or indirect witnesses of the calamitous event. This paper aims to collect and analyze twitter data related to the recent wildfire in California to perform a trend analysis by classifying firsthand and credible information from Twitter users. This work investigates tweets on the recent wildfire in California and classifies them based on witnesses into two types: 1) direct witnesses and 2) indirect witnesses. The collected and analyzed information can be useful for law enforcement agencies and humanitarian organizations for communication and verification of the situational awareness during wildfire hazards. Trend analysis is an aggregated approach that includes sentimental analysis and topic modeling performed through domain-expert manual annotation and machine learning. Trend analysis ultimately builds a fine-grained analysis to assess evacuation routes and provide valuable information to the firsthand emergency responders<span style="font-family:Verdana;">.</span>