In this study, we developed the first linear Joint North Sea Wave Project(JONSWAP) spectrum(JS), which involves a transformation from the JS solution to the natural logarithmic scale. This transformation is convenient...In this study, we developed the first linear Joint North Sea Wave Project(JONSWAP) spectrum(JS), which involves a transformation from the JS solution to the natural logarithmic scale. This transformation is convenient for defining the least squares function in terms of the scale and shape parameters. We identified these two wind-dependent parameters to better understand the wind effect on surface waves. Due to its efficiency and high-resolution, we employed the airborne Light Detection and Ranging(LIDAR) system for our measurements. Due to the lack of actual data, we simulated ocean waves in the MATLAB environment, which can be easily translated into industrial programming language. We utilized the Longuet-Higgin(LH) random-phase method to generate the time series of wave records and used the fast Fourier transform(FFT) technique to compute the power spectra density. After validating these procedures, we identified the JS parameters by minimizing the mean-square error of the target spectrum to that of the estimated spectrum obtained by FFT. We determined that the estimation error is relative to the amount of available wave record data. Finally, we found the inverse computation of wind factors(wind speed and wind fetch length) to be robust and sufficiently precise for wave forecasting.展开更多
The research on residents'travel mode choice mainly studies how traffic flows are shared by different traffic modes,which is the prerequisite for the government to establish transportation planning and policy.Trad...The research on residents'travel mode choice mainly studies how traffic flows are shared by different traffic modes,which is the prerequisite for the government to establish transportation planning and policy.Traditional methods based on survey or small data sources are difficult to accurately describe,explain and verify residents'travel mode choice behavior.Recently,thanks to upgrades of urban infrastructures,many real-time location-tracking devices become available.These devices generate massive real-time data,which provides new opportunities to analyze and explain resident travel mode choice behavior more accurately and more comprehensively.This paper surveys the current research status of big data-driven residents'travel mode choice from three aspects:residents'travel mode identification,acquisition of travel mode influencing factors,and travel mode choice model construction.Finally,the limitations of current research and directions of future research are discussed.展开更多
Without any prior information about related wireless transmitting nodes,joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task.Measuring the ...Without any prior information about related wireless transmitting nodes,joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task.Measuring the signal related data based on a group distributed sensor is an efficient way to infer the various characteristics of the signal sources.In this paper,we propose a particle swarm optimization to estimate multiple co-frequency"blind"source nodes,which is based on the received power data measured by the sensors.To distract the mix signals precisely,a genetic algorithm is applied,and it further improves the estimation performance of the system.The simulation results show the efficiency of the proposed algorithm.展开更多
The challenges of power consumption and memory capacity of computers have driven rapid development on non-volatile memories(NVM).NVMs are generally faster than traditional secondary storage devices,write persistently ...The challenges of power consumption and memory capacity of computers have driven rapid development on non-volatile memories(NVM).NVMs are generally faster than traditional secondary storage devices,write persistently and many offer byte addressing capability.Despite these appealing features,NVMs are difficult to manage and program,which makes it hard to use them as a drop-in replacement for dynamic random-access memory(DRAM).Instead,a majority of modern systems use NVMs through the IO and the file system abstractions.Hiding NVMs under these interfaces poses challenges on how to exploit the new hardware’s performance potential in the existing system software framework.In this article,we survey the key technical issues arisen in this area and introduce several recently developed systems each of which offers novel solutions around these issues.展开更多
The new artificial intelligence(AI)era heavily depends on three converging forces:the advance of AI algorithms,the availability of big data,and the popularity of high performance computing platforms.Data-driven intell...The new artificial intelligence(AI)era heavily depends on three converging forces:the advance of AI algorithms,the availability of big data,and the popularity of high performance computing platforms.Data-driven intelligence,or data intelligence,is a new form of AI technologies that leverages the power of big data and advanced learning algorithm.展开更多
基金supported by the Scientific Instruments Development Program of NSFC (No.615278010)the National Key Basic Research Program of China (973 program) under grant No.2014CB845301/2/3
文摘In this study, we developed the first linear Joint North Sea Wave Project(JONSWAP) spectrum(JS), which involves a transformation from the JS solution to the natural logarithmic scale. This transformation is convenient for defining the least squares function in terms of the scale and shape parameters. We identified these two wind-dependent parameters to better understand the wind effect on surface waves. Due to its efficiency and high-resolution, we employed the airborne Light Detection and Ranging(LIDAR) system for our measurements. Due to the lack of actual data, we simulated ocean waves in the MATLAB environment, which can be easily translated into industrial programming language. We utilized the Longuet-Higgin(LH) random-phase method to generate the time series of wave records and used the fast Fourier transform(FFT) technique to compute the power spectra density. After validating these procedures, we identified the JS parameters by minimizing the mean-square error of the target spectrum to that of the estimated spectrum obtained by FFT. We determined that the estimation error is relative to the amount of available wave record data. Finally, we found the inverse computation of wind factors(wind speed and wind fetch length) to be robust and sufficiently precise for wave forecasting.
基金supported in part by National Natural Science Foundation of China(No.61802387)the Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence
文摘The research on residents'travel mode choice mainly studies how traffic flows are shared by different traffic modes,which is the prerequisite for the government to establish transportation planning and policy.Traditional methods based on survey or small data sources are difficult to accurately describe,explain and verify residents'travel mode choice behavior.Recently,thanks to upgrades of urban infrastructures,many real-time location-tracking devices become available.These devices generate massive real-time data,which provides new opportunities to analyze and explain resident travel mode choice behavior more accurately and more comprehensively.This paper surveys the current research status of big data-driven residents'travel mode choice from three aspects:residents'travel mode identification,acquisition of travel mode influencing factors,and travel mode choice model construction.Finally,the limitations of current research and directions of future research are discussed.
文摘Without any prior information about related wireless transmitting nodes,joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task.Measuring the signal related data based on a group distributed sensor is an efficient way to infer the various characteristics of the signal sources.In this paper,we propose a particle swarm optimization to estimate multiple co-frequency"blind"source nodes,which is based on the received power data measured by the sensors.To distract the mix signals precisely,a genetic algorithm is applied,and it further improves the estimation performance of the system.The simulation results show the efficiency of the proposed algorithm.
文摘The challenges of power consumption and memory capacity of computers have driven rapid development on non-volatile memories(NVM).NVMs are generally faster than traditional secondary storage devices,write persistently and many offer byte addressing capability.Despite these appealing features,NVMs are difficult to manage and program,which makes it hard to use them as a drop-in replacement for dynamic random-access memory(DRAM).Instead,a majority of modern systems use NVMs through the IO and the file system abstractions.Hiding NVMs under these interfaces poses challenges on how to exploit the new hardware’s performance potential in the existing system software framework.In this article,we survey the key technical issues arisen in this area and introduce several recently developed systems each of which offers novel solutions around these issues.
文摘The new artificial intelligence(AI)era heavily depends on three converging forces:the advance of AI algorithms,the availability of big data,and the popularity of high performance computing platforms.Data-driven intelligence,or data intelligence,is a new form of AI technologies that leverages the power of big data and advanced learning algorithm.
