Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theor...We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theorem for the first and a strong convergence theorem for the second.展开更多
This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in ...This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in turbulent carrier flow.The Eulerian framework numerically resolves turbulent carrier flow using a parallelized,finite-volume DNS solver on a staggered Cartesian grid.Particles are tracked using a point-particle method utilizing a Lagrangian particle tracking(LPT)algorithm.The proposed Eulerian-Lagrangian algorithm is validated using an inertial particle-laden turbulent channel flow for different Stokes number cases.The particle concentration profiles and higher-order statistics of the carrier and dispersed phases agree well with the benchmark results.We investigated the effect of fluid velocity interpolation and numerical integration schemes of particle tracking algorithms on particle dispersion statistics.The suitability of fluid velocity interpolation schemes for predicting the particle dispersion statistics is discussed in the framework of the particle tracking algorithm coupled to the finite-volume solver.In addition,we present parallelization strategies implemented in the algorithm and evaluate their parallel performance.展开更多
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl...This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an...Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.展开更多
Abstract:Sand dust belts span approximately one-fifth of the global land surface.In these regions,dust tends to settle on vegetation surfaces,altering the observed reflectance and affecting remote sensing detections.T...Abstract:Sand dust belts span approximately one-fifth of the global land surface.In these regions,dust tends to settle on vegetation surfaces,altering the observed reflectance and affecting remote sensing detections.To enhance the accuracy of maize growth monitoring in dust-affected regions,this study aims to quantify the effect of sand dust retention on maize during the tasseling stage in the Kashgar Prefecture,Xinjiang Uygur Autonomous Region,China,by analyzing changes in canopy reflectance and vegetation indices.First,field sampling was conducted to measure the key canopy structure parameters and dust retention levels of maize,and laboratory spectral measurements were performed on leaf spectral properties under gradient dust retention.The measured data were then used to drive the LargE-Scale remote sensing data and image Simulation framework(LESS)model for simulating realistic maize canopy spectra across different dust levels,with validation against Sentinel-2 imagery.Second,on the basis of the simulated and satellite-derived spectra,the dust resistance of 36 common vegetation indices was systematically evaluated,and new robust dust-resistant indices were developed.The results showed that compared with dust-free maize,the canopy reflectance of dust-retained maize followed an increase–decrease–increase pattern,with critical turning points at 735 and 1325 nm.The maximum reflectance difference of–0.11755(change rate:29.002%)occurred within the 735–1325 nm range at 24 g/m^(2)dust retention,and the minimum reflectance difference of 0.04285(change rate:148.950%)was observed in the 350–735 nm range under the same dust retention level.Among the 36 vegetation indices,only the global environment monitoring index(GEMI)and the ratio of transformed chlorophyll absorption in reflectance index to optimized soil-adjusted vegetation index(TCARI/OSAVI)exhibited dust resistance,with GEMI being effective below 6 g/m^(2)and TCARI/OSAVI remaining stable across all levels(average ratio:0.970).The newly developed indices in this study,(RE3–RE2)/(NIR–RE2),(RE3–RE2)/(RE4–RE2),and(NIR–RE2)/(RE4–RE2),retained values within the predefined dust-resistant range over the full dust retention levels of 0–24 g/m^(2),thus showing a more stable dust resistance compared with the commonly used 36 vegetation indices.Specially,(RE3–RE2)/(RE4–RE2)performed the most robustly in Sentinel-2 imagery,that is,58.020%of pixels were within the dust-resistant range,and an average ratio of 0.937 was obtained for the original-spectra index.This study provides a scientific basis for crop monitoring and management in dust-affected regions.展开更多
A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR...A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR） modeling technique to measure correlations between data streams.It exploits estimated frequencies spectra to extract the essential features of streams.Each stream is represented as the sum of spectral components and the correlation is measured component-wise.Each spectral component is described by four parameters,namely,amplitude,phase,damping rate and frequency.The ε-lag-correlation between two spectral components is calculated.The algorithm uses such information as similarity measures in clustering data streams.Based on a sliding window model,the algorithm can continuously report the most recent clustering results and adjust the number of clusters.Experiments on real and synthetic streams show that the proposed clustering method has a higher speed and clustering quality than other similar methods.展开更多
Traditional two-dimensional（2D） complex resistivity forward modeling is based on Poisson＇s equation but spectral induced polarization（SIP） data are the coproducts of the induced polarization（IP） and the electro...Traditional two-dimensional（2D） complex resistivity forward modeling is based on Poisson＇s equation but spectral induced polarization（SIP） data are the coproducts of the induced polarization（IP） and the electromagnetic induction（EMI） effects.This is especially true under high frequencies,where the EMI effect can exceed the IP effect.2D inversion that only considers the IP effect reduces the reliability of the inversion data.In this paper,we derive differential equations using Maxwell＇s equations.With the introduction of the Cole-Cole model,we use the finite-element method to conduct2 D SIP forward modeling that considers the EMI and IP effects simultaneously.The data-space Occam method,in which different constraints to the model smoothness and parametric boundaries are introduced,is then used to simultaneously obtain the four parameters of the Cole-Cole model using multi-array electric field data.This approach not only improves the stability of the inversion but also significantly reduces the solution ambiguity.To improve the computational efficiency,message passing interface programming was used to accelerate the 2D SIP forward modeling and inversion.Synthetic datasets were tested using both serial and parallel algorithms,and the tests suggest that the proposed parallel algorithm is robust and efficient.展开更多
A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low freq...A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.展开更多
In this paper, we explore a novel ensemble method for spectral clustering. In contrast to the traditional clustering ensemble methods that combine all the obtained clustering results, we propose the adaptive spectral ...In this paper, we explore a novel ensemble method for spectral clustering. In contrast to the traditional clustering ensemble methods that combine all the obtained clustering results, we propose the adaptive spectral clustering ensemble method to achieve a better clustering solution. This method can adaptively assess the number of the component members, which is not owned by many other algorithms. The component clusterings of the ensemble system are generated by spectral clustering （SC） which bears some good characteristics to engender the diverse committees. The selection process works by evaluating the generated component spectral clustering through resampling technique and population-based incremental learning algorithm （PBIL）. Experimental results on UCI datasets demonstrate that the proposed algorithm can achieve better results compared with traditional clustering ensemble methods, especially when the number of component clusterings is large.展开更多
In this article, numerical modeling of borehole radar for well logging in time domain is developed using pseudo-spectral time domain algorithm in axisymmetric cylindrical coordinate for proximate true formation model....In this article, numerical modeling of borehole radar for well logging in time domain is developed using pseudo-spectral time domain algorithm in axisymmetric cylindrical coordinate for proximate true formation model. The conductivity and relative permittivity logging curves are obtained from the data of borehole radar for well logging. Since the relative permittivity logging curve is not affected by salinity of formation water, borehole radar for well logging has obvious advantages as compared with conventional electrical logging. The borehole radar for well logging is a one-transmitter and two-receiver logging tool. The conductivity and relative permittivity logging curves are obtained successfully by measuring the amplitude radio and the time difference of pulse waveform from two receivers. The calculated conductivity and relative permittivity logging curves are close to the true value of surrounding formation, which tests the usability and reliability of borehole radar for well logging. The numerical modeling of borehole radar for well logging laid the important foundation for researching its logging tool.展开更多
The accurate detection of cooperative targets plays a key and foundational role in unmanned aerial vehicle (UAV) landing autonomously. The standard method based on fixed threshold is too susceptible to both illuminati...The accurate detection of cooperative targets plays a key and foundational role in unmanned aerial vehicle (UAV) landing autonomously. The standard method based on fixed threshold is too susceptible to both illumination variations and interference. To overcome issues above, a robust detection algorithm with triple constraints for cooperative targets based on spectral residual (TCSR) is proposed. Firstly, by designing an asymmetric cooperative target, which comprises red background, green H and triangle target, the captured original image is converted into a Lab color space, whose saliency map is yielded by constructing the spectral residual. Then, the triple constraints are developed according to the prior knowledge of the cooperative target. Finally, the salient region in saliency map is considered as the cooperative target, and it meets the triple constraints. Experimental results in complex environments show that the proposed TCSR outperforms the standard methods in higher detection accuracy and lower false alarm rate.展开更多
The Periodicallg Moving Part Modulation (PMPM) for the moving parts in targetprovides important signatures for target recognition. However, most radars operate inmultiple-target mode and can only get discontinuous clu...The Periodicallg Moving Part Modulation (PMPM) for the moving parts in targetprovides important signatures for target recognition. However, most radars operate inmultiple-target mode and can only get discontinuous clusters of the returned pulses, which makes itextremely difficult to extract PMPM signature from the echoes. This paper puts forward theAlternative Iteration Deconvolution based on Minimum Entropy criteria (AIDME) for spectralestimation of extended target's echoes, utilizing the special feature that the PMPM spectra usuallyhave simple structures. Experimental results show that this method can effectively eliminate thesevere influence caused hy the convolution kernel and gain a satisfactory spectral estimation thatapproaches to the true spectrum.展开更多
The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity measure. However, the Euclidean distance m...The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity measure. However, the Euclidean distance measure cannot ful y reveal the complex distribution data, and the result of spectral clustering is very sensitive to the scaling parameter. To solve these problems, a new manifold distance measure and a novel simulated anneal-ing spectral clustering （SASC） algorithm based on the manifold distance measure are proposed. The simulated annealing based on genetic algorithm （SAGA）, characterized by its rapid convergence to the global optimum, is used to cluster the sample points in the spectral mapping space. The proposed algorithm can not only reflect local and global consistency better, but also reduce the sensitivity of spectral clustering to the kernel parameter, which improves the algorithm’s clustering performance. To efficiently apply the algorithm to image segmentation, the Nystrom method is used to reduce the computation complexity. Experimental results show that compared with traditional clustering algorithms and those popular spectral clustering algorithms, the proposed algorithm can achieve better clustering performances on several synthetic datasets, texture images and real images.展开更多
Fourier transform （FF） is a commonly used method in spectral analysis of ocean wave and offshore structure responses, but it is not suitable for records of short length. In this paper another method, wavelet transfo...Fourier transform （FF） is a commonly used method in spectral analysis of ocean wave and offshore structure responses, but it is not suitable for records of short length. In this paper another method, wavelet transform （WT）, is applied to ＇analyze the data of short length. The Morlet wavelet is employed to calculate the spectra density functions for wave records and simulated Floating Production Storage and Offloading （FPSO） vessels＇ responses. Computed wave data include simulated wave data based on JONSWAP spectrum and the recorded data of Storm 149 from North Alwyn. Wavelet method is validated by comparing the statistical characteristics by WF method and those by fast Fourier transform （FFT） method with those of target spectra. The spectral density fnnctions＇ shapes calculated by WT are less malformed and have less error of statistical characteristics compared with those by FT especially when the record lengths decrease.展开更多
To quickly identify the mineral pigments in the Dunhuang murals,a spectral matching algorithm(SMA)based on four methods was combined with laser-induced breakdown spectroscopy(LIBS)for the first time.The optimal range ...To quickly identify the mineral pigments in the Dunhuang murals,a spectral matching algorithm(SMA)based on four methods was combined with laser-induced breakdown spectroscopy(LIBS)for the first time.The optimal range of LIBS spectrum for mineral pigments was determined using the similarity value between two different types of samples of the same pigment.A mineral pigment LIBS database was established by comparing the spectral similarities of tablets and simulated samples,and this database was successfully used to identify unknown pigments on tablet,simulated,and real mural debris samples.The results show that the SMA method coupled with the LIBS technique has great potential for identifying mineral pigments.展开更多
In the process of clothing image researching,how to segment the clothing quickly and accurately and retain the clothing style details as much as possible is the basis of subsequent image analysis.Spectral clustering c...In the process of clothing image researching,how to segment the clothing quickly and accurately and retain the clothing style details as much as possible is the basis of subsequent image analysis.Spectral clustering clothing image segmentation algorithm is a common method in the process of clothing image extraction.However,the traditional model requires high computing power and is easily affected by the initial center of clustering.It often falls into local optimization.Aiming at the above two points,an improved spectral clustering clothing image segmentation algorithm is proposed in this paper.The Nystrom approximation strategy is introduced into the spectral mapping process to reduce the computational complexity.In the clustering stage,this algorithm uses the global optimization advantage of the particle swarm optimization algorithm and selects the sparrow search algorithm to search the optimal initial clustering point,to effectively avoid the occurrence of local optimization.In the end,the effectiveness of this algorithm is verified on clothing images in each environment.展开更多
A pseudospectral method with symplectic algorithm for the solution of time-dependent Schrodinger equations （TDSE） is introduced. The spatial part of the wavefunction is discretized into sparse grid by pseudospectral...A pseudospectral method with symplectic algorithm for the solution of time-dependent Schrodinger equations （TDSE） is introduced. The spatial part of the wavefunction is discretized into sparse grid by pseudospectral method and the time evolution is given in symplectic scheme. This method allows us to obtain a highly accurate and stable solution of TDSE. The effectiveness and efficiency of this method is demonstrated by the high-order harmonic spectra of one-dimensional atom in strong laser field as compared with previously published work. The influence of the additional static electric field is also investigated.展开更多
In conventional linear spectral mixture analysis model,a class is represented by a single endmember.However,the intra-class spectral variability is usually very large,which makes it difficult to represent a class,and ...In conventional linear spectral mixture analysis model,a class is represented by a single endmember.However,the intra-class spectral variability is usually very large,which makes it difficult to represent a class,and in this case,it leads to incorrect unmixing results. Some proposed algorithms play a positive role in overcoming the endmember variability,but there are shortcomings on computation intensive,unsatisfactory unmixing results and so on. Recently,sparse regression has been applied to unmixing,assuming each mixed pixel can be expressed as a linear combination of only a few spectra in a spectral library. It is essentially the same as multiple endmember spectral unmixing. OMP( orthogonal matching pursuit),a sparse reconstruction algorithm,has advantages of simple structure and high efficiency. However,it does not take into account the constraints of abundance non-negativity and abundance sum-to-one( ANC and ASC),leading to undesirable unmixing results. In order to solve these issues,this paper presents an improved OMP algorithm( fully constraint OMP,FOMP) for multiple endmember hyperspectral sparse unmixing. The proposed algorithm overcomes the shortcomings of OMP,and on the other hand,it solves the problem of endmember variability.The ANC and ASC constraints are firstly added into the OMP algorithm,and then the endmember set is refined by the relative increase in root-mean-square-error( RMSE) to avoid over-fitting,finally pixels are unmixed by their optimal endmember set. The simulated and real hyperspectral data experiments show that FOPM unmixing results are ideally comparable and abundance RMSE reduces much lower than OMP and simple spectral mixture analysis( s SMA),and has a strong anti-noise performance. It proves that multiple endmember spectral mixture analysis is more reasonable.展开更多
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
基金supported by the Science and Technology Fund of TNU-Thai Nguyen University of Science.
文摘We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theorem for the first and a strong convergence theorem for the second.
基金supported by the P.G.Senapathy Center for Computing Resources at IIT Madrasfunding provided by the Ministry of Education,Government of Indiasupported by the National Natural Science Foundation of China(Grant Nos.12388101,12472224 and 92252104).
文摘This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in turbulent carrier flow.The Eulerian framework numerically resolves turbulent carrier flow using a parallelized,finite-volume DNS solver on a staggered Cartesian grid.Particles are tracked using a point-particle method utilizing a Lagrangian particle tracking(LPT)algorithm.The proposed Eulerian-Lagrangian algorithm is validated using an inertial particle-laden turbulent channel flow for different Stokes number cases.The particle concentration profiles and higher-order statistics of the carrier and dispersed phases agree well with the benchmark results.We investigated the effect of fluid velocity interpolation and numerical integration schemes of particle tracking algorithms on particle dispersion statistics.The suitability of fluid velocity interpolation schemes for predicting the particle dispersion statistics is discussed in the framework of the particle tracking algorithm coupled to the finite-volume solver.In addition,we present parallelization strategies implemented in the algorithm and evaluate their parallel performance.
基金supported by the Research Project of China Southern Power Grid(No.056200KK52222031).
文摘This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
基金the National Key Research and Development Program of China (Grant No.2022YFF0711400)the National Space Science Data Center Youth Open Project (Grant No. NSSDC2302001)
文摘Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.
基金supported by the Fundamental Research Funds for the Central Universities(N2001020)the National Natural Science Foundation of China(41201359).
文摘Abstract:Sand dust belts span approximately one-fifth of the global land surface.In these regions,dust tends to settle on vegetation surfaces,altering the observed reflectance and affecting remote sensing detections.To enhance the accuracy of maize growth monitoring in dust-affected regions,this study aims to quantify the effect of sand dust retention on maize during the tasseling stage in the Kashgar Prefecture,Xinjiang Uygur Autonomous Region,China,by analyzing changes in canopy reflectance and vegetation indices.First,field sampling was conducted to measure the key canopy structure parameters and dust retention levels of maize,and laboratory spectral measurements were performed on leaf spectral properties under gradient dust retention.The measured data were then used to drive the LargE-Scale remote sensing data and image Simulation framework(LESS)model for simulating realistic maize canopy spectra across different dust levels,with validation against Sentinel-2 imagery.Second,on the basis of the simulated and satellite-derived spectra,the dust resistance of 36 common vegetation indices was systematically evaluated,and new robust dust-resistant indices were developed.The results showed that compared with dust-free maize,the canopy reflectance of dust-retained maize followed an increase–decrease–increase pattern,with critical turning points at 735 and 1325 nm.The maximum reflectance difference of–0.11755(change rate:29.002%)occurred within the 735–1325 nm range at 24 g/m^(2)dust retention,and the minimum reflectance difference of 0.04285(change rate:148.950%)was observed in the 350–735 nm range under the same dust retention level.Among the 36 vegetation indices,only the global environment monitoring index(GEMI)and the ratio of transformed chlorophyll absorption in reflectance index to optimized soil-adjusted vegetation index(TCARI/OSAVI)exhibited dust resistance,with GEMI being effective below 6 g/m^(2)and TCARI/OSAVI remaining stable across all levels(average ratio:0.970).The newly developed indices in this study,(RE3–RE2)/(NIR–RE2),(RE3–RE2)/(RE4–RE2),and(NIR–RE2)/(RE4–RE2),retained values within the predefined dust-resistant range over the full dust retention levels of 0–24 g/m^(2),thus showing a more stable dust resistance compared with the commonly used 36 vegetation indices.Specially,(RE3–RE2)/(RE4–RE2)performed the most robustly in Sentinel-2 imagery,that is,58.020%of pixels were within the dust-resistant range,and an average ratio of 0.937 was obtained for the original-spectra index.This study provides a scientific basis for crop monitoring and management in dust-affected regions.
基金The National Natural Science Foundation of China(No.60673060)the Natural Science Foundation of Jiangsu Province(No.BK2005047)
文摘A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR） modeling technique to measure correlations between data streams.It exploits estimated frequencies spectra to extract the essential features of streams.Each stream is represented as the sum of spectral components and the correlation is measured component-wise.Each spectral component is described by four parameters,namely,amplitude,phase,damping rate and frequency.The ε-lag-correlation between two spectral components is calculated.The algorithm uses such information as similarity measures in clustering data streams.Based on a sliding window model,the algorithm can continuously report the most recent clustering results and adjust the number of clusters.Experiments on real and synthetic streams show that the proposed clustering method has a higher speed and clustering quality than other similar methods.
基金jointly sponsored by the National Natural Science Foundation of China(Grant No.41374078)the Geological Survey Projects of the Ministry of Land and Resources of China(Grant Nos.12120113086100 and 12120113101300)Beijing Higher Education Young Elite Teacher Project
文摘Traditional two-dimensional（2D） complex resistivity forward modeling is based on Poisson＇s equation but spectral induced polarization（SIP） data are the coproducts of the induced polarization（IP） and the electromagnetic induction（EMI） effects.This is especially true under high frequencies,where the EMI effect can exceed the IP effect.2D inversion that only considers the IP effect reduces the reliability of the inversion data.In this paper,we derive differential equations using Maxwell＇s equations.With the introduction of the Cole-Cole model,we use the finite-element method to conduct2 D SIP forward modeling that considers the EMI and IP effects simultaneously.The data-space Occam method,in which different constraints to the model smoothness and parametric boundaries are introduced,is then used to simultaneously obtain the four parameters of the Cole-Cole model using multi-array electric field data.This approach not only improves the stability of the inversion but also significantly reduces the solution ambiguity.To improve the computational efficiency,message passing interface programming was used to accelerate the 2D SIP forward modeling and inversion.Synthetic datasets were tested using both serial and parallel algorithms,and the tests suggest that the proposed parallel algorithm is robust and efficient.
基金supported by the National Natural Science Foundation of China (6117212711071002)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education (20113401110006)the Innovative Research Team of 211 Project in Anhui University (KJTD007A)
文摘A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.
基金Supported by the National Natural Science Foundation of China (60661003)the Research Project Department of Education of Jiangxi Province (GJJ10566)
文摘In this paper, we explore a novel ensemble method for spectral clustering. In contrast to the traditional clustering ensemble methods that combine all the obtained clustering results, we propose the adaptive spectral clustering ensemble method to achieve a better clustering solution. This method can adaptively assess the number of the component members, which is not owned by many other algorithms. The component clusterings of the ensemble system are generated by spectral clustering （SC） which bears some good characteristics to engender the diverse committees. The selection process works by evaluating the generated component spectral clustering through resampling technique and population-based incremental learning algorithm （PBIL）. Experimental results on UCI datasets demonstrate that the proposed algorithm can achieve better results compared with traditional clustering ensemble methods, especially when the number of component clusterings is large.
基金supported by the Open Fund of Key Laboratory of Geo-detection (China University of Geosciences,Beijing),Ministry of Education (No. GDL0805)
文摘In this article, numerical modeling of borehole radar for well logging in time domain is developed using pseudo-spectral time domain algorithm in axisymmetric cylindrical coordinate for proximate true formation model. The conductivity and relative permittivity logging curves are obtained from the data of borehole radar for well logging. Since the relative permittivity logging curve is not affected by salinity of formation water, borehole radar for well logging has obvious advantages as compared with conventional electrical logging. The borehole radar for well logging is a one-transmitter and two-receiver logging tool. The conductivity and relative permittivity logging curves are obtained successfully by measuring the amplitude radio and the time difference of pulse waveform from two receivers. The calculated conductivity and relative permittivity logging curves are close to the true value of surrounding formation, which tests the usability and reliability of borehole radar for well logging. The numerical modeling of borehole radar for well logging laid the important foundation for researching its logging tool.
基金supported by the National Natural Science Foundation of China(61135001)the Scientific Research Program of Shaanxi Provincial Department of Education(16JK1499)+2 种基金the Doctoral Fund of Xi’an University of Science and Technology(2015QDJ007)the Cultivation of Xi’an University of Science and Technology(2014015)the Ministry of Education Key Laboratory of Information Fusion Technology(LIFT2015-G-1)
文摘The accurate detection of cooperative targets plays a key and foundational role in unmanned aerial vehicle (UAV) landing autonomously. The standard method based on fixed threshold is too susceptible to both illumination variations and interference. To overcome issues above, a robust detection algorithm with triple constraints for cooperative targets based on spectral residual (TCSR) is proposed. Firstly, by designing an asymmetric cooperative target, which comprises red background, green H and triangle target, the captured original image is converted into a Lab color space, whose saliency map is yielded by constructing the spectral residual. Then, the triple constraints are developed according to the prior knowledge of the cooperative target. Finally, the salient region in saliency map is considered as the cooperative target, and it meets the triple constraints. Experimental results in complex environments show that the proposed TCSR outperforms the standard methods in higher detection accuracy and lower false alarm rate.
文摘The Periodicallg Moving Part Modulation (PMPM) for the moving parts in targetprovides important signatures for target recognition. However, most radars operate inmultiple-target mode and can only get discontinuous clusters of the returned pulses, which makes itextremely difficult to extract PMPM signature from the echoes. This paper puts forward theAlternative Iteration Deconvolution based on Minimum Entropy criteria (AIDME) for spectralestimation of extended target's echoes, utilizing the special feature that the PMPM spectra usuallyhave simple structures. Experimental results show that this method can effectively eliminate thesevere influence caused hy the convolution kernel and gain a satisfactory spectral estimation thatapproaches to the true spectrum.
基金supported by the National Natural Science Foundationof China(61272119)
文摘The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity measure. However, the Euclidean distance measure cannot ful y reveal the complex distribution data, and the result of spectral clustering is very sensitive to the scaling parameter. To solve these problems, a new manifold distance measure and a novel simulated anneal-ing spectral clustering （SASC） algorithm based on the manifold distance measure are proposed. The simulated annealing based on genetic algorithm （SAGA）, characterized by its rapid convergence to the global optimum, is used to cluster the sample points in the spectral mapping space. The proposed algorithm can not only reflect local and global consistency better, but also reduce the sensitivity of spectral clustering to the kernel parameter, which improves the algorithm’s clustering performance. To efficiently apply the algorithm to image segmentation, the Nystrom method is used to reduce the computation complexity. Experimental results show that compared with traditional clustering algorithms and those popular spectral clustering algorithms, the proposed algorithm can achieve better clustering performances on several synthetic datasets, texture images and real images.
文摘Fourier transform （FF） is a commonly used method in spectral analysis of ocean wave and offshore structure responses, but it is not suitable for records of short length. In this paper another method, wavelet transform （WT）, is applied to ＇analyze the data of short length. The Morlet wavelet is employed to calculate the spectra density functions for wave records and simulated Floating Production Storage and Offloading （FPSO） vessels＇ responses. Computed wave data include simulated wave data based on JONSWAP spectrum and the recorded data of Storm 149 from North Alwyn. Wavelet method is validated by comparing the statistical characteristics by WF method and those by fast Fourier transform （FFT） method with those of target spectra. The spectral density fnnctions＇ shapes calculated by WT are less malformed and have less error of statistical characteristics compared with those by FT especially when the record lengths decrease.
基金supported by the National Key Research and Development Program of China(No.2019YFC1520701)National Natural Science Foundation of China(Nos.61965015,61741513)+2 种基金the 2020 Industry Support Plan Project in University of Gansu Province(No.2020C-17)the Young Teachers Scientific Research Ability Promotion Plan of Northwest Normal University Province(No.NWNW-LKQN2019-1)the Funds for Innovative Fundamental Research Group Project of Gansu Province(No.21JR7RA131)。
文摘To quickly identify the mineral pigments in the Dunhuang murals,a spectral matching algorithm(SMA)based on four methods was combined with laser-induced breakdown spectroscopy(LIBS)for the first time.The optimal range of LIBS spectrum for mineral pigments was determined using the similarity value between two different types of samples of the same pigment.A mineral pigment LIBS database was established by comparing the spectral similarities of tablets and simulated samples,and this database was successfully used to identify unknown pigments on tablet,simulated,and real mural debris samples.The results show that the SMA method coupled with the LIBS technique has great potential for identifying mineral pigments.
文摘In the process of clothing image researching,how to segment the clothing quickly and accurately and retain the clothing style details as much as possible is the basis of subsequent image analysis.Spectral clustering clothing image segmentation algorithm is a common method in the process of clothing image extraction.However,the traditional model requires high computing power and is easily affected by the initial center of clustering.It often falls into local optimization.Aiming at the above two points,an improved spectral clustering clothing image segmentation algorithm is proposed in this paper.The Nystrom approximation strategy is introduced into the spectral mapping process to reduce the computational complexity.In the clustering stage,this algorithm uses the global optimization advantage of the particle swarm optimization algorithm and selects the sparrow search algorithm to search the optimal initial clustering point,to effectively avoid the occurrence of local optimization.In the end,the effectiveness of this algorithm is verified on clothing images in each environment.
基金Project supported by the National Natural Science Foundation of China （Grant Nos 10374119 and 10674154）, and The 0ne- Hundred-Talents Project of Chinese Academy of Science.Acknowledgments We gratefully acknowledge Professor Ding P Z and Professor Liu X S for their hospitality and help in symplectic algorithm.
文摘A pseudospectral method with symplectic algorithm for the solution of time-dependent Schrodinger equations （TDSE） is introduced. The spatial part of the wavefunction is discretized into sparse grid by pseudospectral method and the time evolution is given in symplectic scheme. This method allows us to obtain a highly accurate and stable solution of TDSE. The effectiveness and efficiency of this method is demonstrated by the high-order harmonic spectra of one-dimensional atom in strong laser field as compared with previously published work. The influence of the additional static electric field is also investigated.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61405041,61571145)the Key Program of Heilongjiang Natural Science Foundation(Grant No.ZD201216)+2 种基金the Program Excellent Academic Leaders of Harbin(Grant No.RC2013XK009003)the China Postdoctoral Science Foundation(Grant No.2014M551221)the Heilongjiang Postdoctoral Science Found(Grant No.LBH-Z13057)
文摘In conventional linear spectral mixture analysis model,a class is represented by a single endmember.However,the intra-class spectral variability is usually very large,which makes it difficult to represent a class,and in this case,it leads to incorrect unmixing results. Some proposed algorithms play a positive role in overcoming the endmember variability,but there are shortcomings on computation intensive,unsatisfactory unmixing results and so on. Recently,sparse regression has been applied to unmixing,assuming each mixed pixel can be expressed as a linear combination of only a few spectra in a spectral library. It is essentially the same as multiple endmember spectral unmixing. OMP( orthogonal matching pursuit),a sparse reconstruction algorithm,has advantages of simple structure and high efficiency. However,it does not take into account the constraints of abundance non-negativity and abundance sum-to-one( ANC and ASC),leading to undesirable unmixing results. In order to solve these issues,this paper presents an improved OMP algorithm( fully constraint OMP,FOMP) for multiple endmember hyperspectral sparse unmixing. The proposed algorithm overcomes the shortcomings of OMP,and on the other hand,it solves the problem of endmember variability.The ANC and ASC constraints are firstly added into the OMP algorithm,and then the endmember set is refined by the relative increase in root-mean-square-error( RMSE) to avoid over-fitting,finally pixels are unmixed by their optimal endmember set. The simulated and real hyperspectral data experiments show that FOPM unmixing results are ideally comparable and abundance RMSE reduces much lower than OMP and simple spectral mixture analysis( s SMA),and has a strong anti-noise performance. It proves that multiple endmember spectral mixture analysis is more reasonable.
