A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems(Lorenz and Rssler...A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems(Lorenz and Rssler) to examine the anti-noise ability for complex systems. Results show that the nonlinear dynamic system analysis method resists noise and reveals the internal dynamics of a weak signal from noise pollution. On this basis, the vertical upward gas–liquid two-phase flow in a 2 mm × 0.81 mm small rectangular channel is investigated. The frequency and energy distributions of the main oscillation mode are revealed by analyzing the time–frequency spectra of the pressure signals of different flow patterns. The positive power spectral density of singular-value frequency entropy and the damping ratio are extracted to characterize the evolution of flow patterns and achieve accurate recognition of different vertical upward gas–liquid flow patterns(bubbly flow:100%, slug flow: 92%, churn flow: 96%, annular flow: 100%). The proposed analysis method will enrich the dynamics theory of multi-phase flow in small channel.展开更多
In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the f...In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is. given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising.展开更多
A strategy for B-spline curve data reduction based on non-uniform B-spline wavelet decomposition is presented. In existing methods of knot removal, ranking the removal knots depends on a procedure of assigning a weigh...A strategy for B-spline curve data reduction based on non-uniform B-spline wavelet decomposition is presented. In existing methods of knot removal, ranking the removal knots depends on a procedure of assigning a weight to each knot to indicate its significance. This is reasonable but not straightforward. Propose is a more straightforward and accurate method to calculate the weight. The wavelet coefficient is taken as a weight for the corresponding knot. The approximating curve and the error can be obtained directly from the wavelet decomposition. By using the hierarchical structure of the wavelet, the error can be computed efficiently in an accumulative manner.展开更多
As the EU's carbon border tax approaches implementation and Generation Z increasingly prioritizes sustainability in their purchasing decisions,ESG has evolved from a moral concern to business survival challenges.B...As the EU's carbon border tax approaches implementation and Generation Z increasingly prioritizes sustainability in their purchasing decisions,ESG has evolved from a moral concern to business survival challenges.By analyzing the latest ESG reports released by prominent apparel brands in the China garment industry,including Bosideng,Anta,Youngor,Li-Ning,and Lilanz,we can see it’s not about a series of emission reduction data but a comprehensive innovation encompassing products,processes,and the entire value chain.展开更多
Cloud backup has been an important issue ever since large quantities of valuable data have been stored on the personal computing devices. Data reduction techniques, such as deduplication, delta encoding, and Lempel-Z...Cloud backup has been an important issue ever since large quantities of valuable data have been stored on the personal computing devices. Data reduction techniques, such as deduplication, delta encoding, and Lempel-Ziv （LZ） compression, performed at the client side before data transfer can help ease cloud backup by saving network bandwidth and reducing cloud storage space. However, client-side data reduction in cloud backup services faces efficiency and privacy challenges. In this paper, we present Pangolin, a secure and efficient cloud backup service for personal data storage by exploiting application awareness. It can speedup backup operations by application-aware client-side data reduction technique, and mitigate data security risks by integrating selective encryption into data reduction for sensitive applications. Our experimental evaluation, based on a prototype implementation, shows that our scheme can improve data reduction efficiency over the state-of-the-art methods by shortening the backup window size to 33%-75%, and its security mechanism for＇ sensitive applications has negligible impact on backup window size.展开更多
As location data are widely available to portable devices, trajectory tracking of moving objects has become an essential technology for most location-based services. To maintain such streaming data of location updates...As location data are widely available to portable devices, trajectory tracking of moving objects has become an essential technology for most location-based services. To maintain such streaming data of location updates from mobile clients, conventional approaches such as time-based regular location updating and distance-based location updating have been used. However, these methods suffer from the large amount of data, redundant location updates, and large trajectory estimation errors due to the varying speed of moving objects. In this paper, we propose a simple but efficient online trajectory data reduction method for portable devices. To solve the problems of redundancy and large estimation errors, the proposed algorithm computes trajectory errors and finds a recent location update that should be sent to the server to satisfy the user requirements. We evaluate the proposed algorithm with real GPS trajectory data consisting of 17 201 trajectories. The intensive simulation results prove that the proposed algorithm always meets the given user requirements and exhibits a data reduction ratio of greater than 87% when the acceptable trajectory error is greater than or equal to 10 meters.展开更多
Imbalanced data classification is one of the major problems in machine learning.This imbalanced dataset typically has significant differences in the number of data samples between its classes.In most cases,the perform...Imbalanced data classification is one of the major problems in machine learning.This imbalanced dataset typically has significant differences in the number of data samples between its classes.In most cases,the performance of the machine learning algorithm such as Support Vector Machine(SVM)is affected when dealing with an imbalanced dataset.The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples.In this paper,a hybrid approach combining data pre-processing technique andSVMalgorithm based on improved Simulated Annealing(SA)was proposed.Firstly,the data preprocessing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed.In this technique,the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data.Next is the training of a balanced dataset using SVM.Since this algorithm requires an iterative process to search for the best penalty parameter during training,an improved SA algorithm was proposed for this task.In this proposed improvement,a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process.Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM.Registering at an average of 89.65%of accuracy for the binary class classification has demonstrated the good performance of the proposed works.展开更多
In order to increase the fault diagnosis efficiency and make the fault data mining be realized, the decision table containing numerical attributes must be discretized for further calculations. The discernibility matri...In order to increase the fault diagnosis efficiency and make the fault data mining be realized, the decision table containing numerical attributes must be discretized for further calculations. The discernibility matrix-based reduction method depends on whether the numerical attributes can be properly discretized or not.So a discretization algorithm based on particle swarm optimization(PSO) is proposed. Moreover, hybrid weights are adopted in the process of particles evolution. Comparative calculations for certain equipment are completed to demonstrate the effectiveness of the proposed algorithm. The results indicate that the proposed algorithm has better performance than other popular algorithms such as class-attribute interdependence maximization(CAIM)discretization method and entropy-based discretization method.展开更多
Various Wireless Sensor Network(WSN)applications require the common task of collecting the data from the sensor nodes using the sink.Since the procedure of collecting data is iterative,an effective technique is necess...Various Wireless Sensor Network(WSN)applications require the common task of collecting the data from the sensor nodes using the sink.Since the procedure of collecting data is iterative,an effective technique is necessary to obtain the data efficiently by reducing the consumption of nodal energy.Hence,a technique for data reduction in WSN is presented in this paper by proposing a prediction algorithm,called Hierarchical Fractional Bidirectional Least-Mean Square(HFBLMS)algorithm.The novel algorithm is designed by modifying Hierarchical Least-Mean Square(HLMS)algorithm with the inclusion of BLMS for bidirectional-based data prediction and Fractional Calculus(FC)in the weight update process.Data redundancy is achieved by transmitting only those data required based on the data predicted at the sensor node and the sink.Moreover,the proposed HFBLMS algorithm reduces the energy consumption in the network by the effective prediction attained by BLMS.Two metrics,such as energy consumption and prediction error,are used for the evaluation of performance of the HFBLMS prediction algorithm,where it can attain energy values of 0.3587 and 0.1953 at the maximum number of rounds and prediction errors of just 0.0213 and 0.0095,using air quality and localization datasets,respectively.展开更多
In aerodynamic optimization, global optimization methods such as genetic algorithms are preferred in many cases because of their advantage on reaching global optimum. However,for complex problems in which large number...In aerodynamic optimization, global optimization methods such as genetic algorithms are preferred in many cases because of their advantage on reaching global optimum. However,for complex problems in which large number of design variables are needed, the computational cost becomes prohibitive, and thus original global optimization strategies are required. To address this need, data dimensionality reduction method is combined with global optimization methods, thus forming a new global optimization system, aiming to improve the efficiency of conventional global optimization. The new optimization system involves applying Proper Orthogonal Decomposition（POD） in dimensionality reduction of design space while maintaining the generality of original design space. Besides, an acceleration approach for samples calculation in surrogate modeling is applied to reduce the computational time while providing sufficient accuracy. The optimizations of a transonic airfoil RAE2822 and the transonic wing ONERA M6 are performed to demonstrate the effectiveness of the proposed new optimization system. In both cases, we manage to reduce the number of design variables from 20 to 10 and from 42 to 20 respectively. The new design optimization system converges faster and it takes 1/3 of the total time of traditional optimization to converge to a better design, thus significantly reducing the overall optimization time and improving the efficiency of conventional global design optimization method.展开更多
In the preparation of firing tables, the determination of projectile drag coefficientsthrough firing test radar data reduction is very important. Many methods have been developed for this work but none of them appear ...In the preparation of firing tables, the determination of projectile drag coefficientsthrough firing test radar data reduction is very important. Many methods have been developed for this work but none of them appear to be satisfactory in one Way or another. Inthis paper a multi-spline model of drag coefficient (cd) curve is developed that can guaranteefirst derivative continuity of the cd curve and has good flexibility of fitting accurately to acd curve from subsonic up to supersonic range. Practical firing data reduction tests showboth fast convergence and accurate fitting results. Typical velocity fitting RMS errors are0.05-0.08 m/s.展开更多
In the context of increasing dimensionality of design variables and the complexity of constraints, the efficacy of Surrogate-Based Optimization(SBO) is limited. The traditional linear and nonlinear dimensionality redu...In the context of increasing dimensionality of design variables and the complexity of constraints, the efficacy of Surrogate-Based Optimization(SBO) is limited. The traditional linear and nonlinear dimensionality reduction algorithms are mainly to decompose the mathematical matrix composed of design variables or objective functions in various forms, the smoothness of the design space cannot be guaranteed in the process, and additional constraint functions need to be added in the optimization, which increases the calculation cost. This study presents a new parameterization method to improve both problems of SBO. The new parameterization is addressed by decoupling affine transformations(dilation, rotation, shearing, and translation) within the Grassmannian submanifold, which enables a separate representation of the physical information of the airfoil in a highdimensional space. Building upon this, Principal Geodesic Analysis(PGA) is employed to achieve geometric control, compress the design space, reduce the number of design variables, reduce the dimensions of design variables and enhance predictive performance during the surrogate optimization process. For comparison, a dimensionality reduction space is defined using 95% of the energy,and RAE 2822 for transonic conditions are used as demonstrations. This method significantly enhances the optimization efficiency of the surrogate model while effectively enabling geometric constraints. In three-dimensional problems, it enables simultaneous design of planar shapes for various components of the aircraft and high-order perturbation deformations. Optimization was applied to the ONERA M6 wing, achieving a lift-drag ratio of 18.09, representing a 27.25% improvement compared to the baseline configuration. In comparison to conventional surrogate model optimization methods, which only achieved a 17.97% improvement, this approach demonstrates its superiority.展开更多
The Wide Field Survey Telescope(WFST)is located at 4200 m on Saishiteng Mountain in Lenghu,Qinghai Province,China.It features a primary mirror with a diameter of 2.5 m and a camera equipped with nine CCDs,providing a ...The Wide Field Survey Telescope(WFST)is located at 4200 m on Saishiteng Mountain in Lenghu,Qinghai Province,China.It features a primary mirror with a diameter of 2.5 m and a camera equipped with nine CCDs,providing a wide field of view of approximately 3×3 square degrees.Calibration parameters are essential to ensure the precision of astrometric observations with the WFST.These parameters are derived from geometric distortion(GD)and gaps through astrometric modeling and are subsequently validated via the Yao’An High Precision Telescope(YAHPT).The GD solutions show maximum distortions between 1.18 and 10.29 pixels for the WFST chips,with central chips exhibiting lower distortion.After applying the GD correction,the precision of the WFST reaches 4 mas.The interchip gaps of the WFST range from 1.922 mm to 7.765 mm,corresponding to 10μm/pixel,aligning with the design and measurements.The calibrated parameters guarantee that the WFST can perform highly accurate astrometric measurements.Furthermore,as the WFST undergoes updates,the parameter model remains consistently applicable.展开更多
A detector's nondestructive readout mode allows its pixels to be read multiple times during integration,enabling generation of a series of"up-the-ramp"images that continuously accumulate photons between ...A detector's nondestructive readout mode allows its pixels to be read multiple times during integration,enabling generation of a series of"up-the-ramp"images that continuously accumulate photons between successive frames.Because noise is correlated across these images,optimal stacking generally requires the images to be weighted unequally to achieve the best possible target signal-to-noise ratio(SNR).Objects in the sky present wildly varied brightness characteristics,and the counts in individual pixels of the same object can also span wide ranges.Therefore,a single set of weights cannot be optimal in all cases.To ensure that the stacked image is easily calibratable,we apply the same weight to all pixels within the same frame.In practice,results for high-SNR cases degraded only slightly when we used weights derived for low-SNR cases,whereas the low-SNR cases remained more sensitive to the weights.Therefore,we propose a quasi-optimal stacking method that maximizes the stacked SNR for the case where the RSN=1 per pixel in the last frame and use simulated data to demonstrate that this approach enhances the SNR more strongly than the equal-weight stacking and ramp fitting methods.Furthermore,we estimate the improvements in the limiting magnitudes for the China Space Station Telescope using the proposed method.When compared with the conventional readout mode,which is equivalent to selecting the last frame from the nondestructive readout,stacking 30 up-the-ramp images can improve the limiting magnitude by approximately 0.5 mag for the telescope's near-infrared observations,effectively reducing readout noise by approximately 62%.展开更多
This paper describes the data release of the LAMOST pilot survey, which includes data reduction, calibration, spectral analysis, data products and data access. The accuracy of the released data and the information abo...This paper describes the data release of the LAMOST pilot survey, which includes data reduction, calibration, spectral analysis, data products and data access. The accuracy of the released data and the information about the FITS headers of spectra are also introduced. The released data set includes 319 000 spectra and a catalog of these objects.展开更多
A data buoy, when moored at the sea, will be influenced by different environmental factors such as wind and wave current. For this reason, the stability of a data buoy is of great importance. This paper describes the ...A data buoy, when moored at the sea, will be influenced by different environmental factors such as wind and wave current. For this reason, the stability of a data buoy is of great importance. This paper describes the analysis and calculation of the stability of a data buoy which has a weight suspended from its bottom under normal working conditions and the influence of the distance from the weight to the bottom of the buoy on the 'righting' ability of the buoy when it is capsized. The paper also provides the curves which show the influence of the size of the weight and the distance from the weight to the bottom of the buoy on the 'righting' ability of the buoy. All these are of great reference value for the design and use of a data buoy at sea and for the design of similar floating bodies. The results from calculations agree with those from the model experiment.展开更多
Large scale dense Wireless Sensor Networks (WSNs) have been progressively employed for different classes of applications for the resolve of precise monitoring. As a result of high density of nodes, both spatially and ...Large scale dense Wireless Sensor Networks (WSNs) have been progressively employed for different classes of applications for the resolve of precise monitoring. As a result of high density of nodes, both spatially and temporally correlated information can be detected by several nodes. Hence, energy can be saved which is a major aspect of these networks. Moreover, by using these advantages of correlations, communication and data exchange can be reduced. In this paper, a novel algorithm that selects the data based on their contextual importance is proposed. The data, which are contextually important, are only transmitted to the upper layer and the remains are ignored. In this way, the proposed method achieves significant data reduction and in turn improves the energy conservation of data gathering.展开更多
Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimu...Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus,the total consumption of energy is optimal.However,the computational complexity will be increased due to data dimension,and this leads to increase in delay in network data transmission and reception.For solving the above-mentioned issues,an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis(ILDA)is proposed for 3D hierarchical clustering WSNs.The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs.This ILDA approach consists of four major steps such as data dimension reduction,distance similarity index introduction,double cluster head technique and node dormancy approach.This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head(CH)selection technique.According to node’s position and residual energy,optimal cluster-head function is generated,and every CH is elected by this formulation.For a 3D spherical structure,under the same network condition,the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering(IDHC)is compared with Distributed Energy-Efficient Clustering(DEEC),Hybrid Energy Efficient Distributed(HEED)and Stable Election Protocol(SEP)techniques.It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput,network residual energy,network lifetime and first node death round.展开更多
In this paper,a quantum-enhanced framework is proposed to optimize observation point selection in environmental data assimilation.The method transforms the task into a QUBO problem,balancing uncertainty reduction and ...In this paper,a quantum-enhanced framework is proposed to optimize observation point selection in environmental data assimilation.The method transforms the task into a QUBO problem,balancing uncertainty reduction and spatial diversity.By leveraging a quantum-inspired optical Ising machine,it avoids the exponential complexity of classical optimization.Tests on the Lorenz-1996 model demonstrate its superiority over traditional methods,enhancing computational efficiency without loss of accuracy.The findings underscore the potential of quantum-inspired optimization for scalable,real-time assimilation in highresolution weather prediction,reducing dimensionality and computational cost.展开更多
The Radial Basis Function（RBF） method with data reduction is an effective way to perform mesh deformation. However, for large deformations on meshes of complex aerodynamic configurations, the efficiency of the RBF m...The Radial Basis Function（RBF） method with data reduction is an effective way to perform mesh deformation. However, for large deformations on meshes of complex aerodynamic configurations, the efficiency of the RBF mesh deformation method still needs to be further improved to fulfill the demand of practical application. To achieve this goal, a multistep RBF method based on a multilevel subspace RBF algorithm is presented to further improve the efficiency of the mesh deformation method in this research. A whole deformation is divided into a series of steps, and the supporting radius is adjusted in accordance with the maximal displacement error. Furthermore, parallel computing is applied to the interpolation to enhance the efficiency. Typical deformation problems of the NASA Common Research Model（CRM） configuration, the DLR-F6 wing-body-nacellepylon configuration, and the DLR-F11 high-lift configuration are tested to verify the feasibility of this method. Test results show that the presented multistep RBF mesh deformation method is efficient and robust in dealing with large deformation problems over complex geometries.展开更多
基金Supported by the National Natural Science Foundation of China(51406031)
文摘A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems(Lorenz and Rssler) to examine the anti-noise ability for complex systems. Results show that the nonlinear dynamic system analysis method resists noise and reveals the internal dynamics of a weak signal from noise pollution. On this basis, the vertical upward gas–liquid two-phase flow in a 2 mm × 0.81 mm small rectangular channel is investigated. The frequency and energy distributions of the main oscillation mode are revealed by analyzing the time–frequency spectra of the pressure signals of different flow patterns. The positive power spectral density of singular-value frequency entropy and the damping ratio are extracted to characterize the evolution of flow patterns and achieve accurate recognition of different vertical upward gas–liquid flow patterns(bubbly flow:100%, slug flow: 92%, churn flow: 96%, annular flow: 100%). The proposed analysis method will enrich the dynamics theory of multi-phase flow in small channel.
文摘In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is. given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising.
基金Supported by the Natural Science Foundation of China (50075032) and State High-Technology Development Program of China (2001AA421150)
文摘A strategy for B-spline curve data reduction based on non-uniform B-spline wavelet decomposition is presented. In existing methods of knot removal, ranking the removal knots depends on a procedure of assigning a weight to each knot to indicate its significance. This is reasonable but not straightforward. Propose is a more straightforward and accurate method to calculate the weight. The wavelet coefficient is taken as a weight for the corresponding knot. The approximating curve and the error can be obtained directly from the wavelet decomposition. By using the hierarchical structure of the wavelet, the error can be computed efficiently in an accumulative manner.
文摘As the EU's carbon border tax approaches implementation and Generation Z increasingly prioritizes sustainability in their purchasing decisions,ESG has evolved from a moral concern to business survival challenges.By analyzing the latest ESG reports released by prominent apparel brands in the China garment industry,including Bosideng,Anta,Youngor,Li-Ning,and Lilanz,we can see it’s not about a series of emission reduction data but a comprehensive innovation encompassing products,processes,and the entire value chain.
基金supported in part by the National High Technology Research and Development 863 Program of China under Grant No.2013AA013201the National Natural Science Foundation of China under Grant Nos.61025009,61232003,61120106005,61170288,and 61379146
文摘Cloud backup has been an important issue ever since large quantities of valuable data have been stored on the personal computing devices. Data reduction techniques, such as deduplication, delta encoding, and Lempel-Ziv （LZ） compression, performed at the client side before data transfer can help ease cloud backup by saving network bandwidth and reducing cloud storage space. However, client-side data reduction in cloud backup services faces efficiency and privacy challenges. In this paper, we present Pangolin, a secure and efficient cloud backup service for personal data storage by exploiting application awareness. It can speedup backup operations by application-aware client-side data reduction technique, and mitigate data security risks by integrating selective encryption into data reduction for sensitive applications. Our experimental evaluation, based on a prototype implementation, shows that our scheme can improve data reduction efficiency over the state-of-the-art methods by shortening the backup window size to 33%-75%, and its security mechanism for＇ sensitive applications has negligible impact on backup window size.
基金supported by the Incheon National University Research Grant of Korea in 2011
文摘As location data are widely available to portable devices, trajectory tracking of moving objects has become an essential technology for most location-based services. To maintain such streaming data of location updates from mobile clients, conventional approaches such as time-based regular location updating and distance-based location updating have been used. However, these methods suffer from the large amount of data, redundant location updates, and large trajectory estimation errors due to the varying speed of moving objects. In this paper, we propose a simple but efficient online trajectory data reduction method for portable devices. To solve the problems of redundancy and large estimation errors, the proposed algorithm computes trajectory errors and finds a recent location update that should be sent to the server to satisfy the user requirements. We evaluate the proposed algorithm with real GPS trajectory data consisting of 17 201 trajectories. The intensive simulation results prove that the proposed algorithm always meets the given user requirements and exhibits a data reduction ratio of greater than 87% when the acceptable trajectory error is greater than or equal to 10 meters.
文摘Imbalanced data classification is one of the major problems in machine learning.This imbalanced dataset typically has significant differences in the number of data samples between its classes.In most cases,the performance of the machine learning algorithm such as Support Vector Machine(SVM)is affected when dealing with an imbalanced dataset.The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples.In this paper,a hybrid approach combining data pre-processing technique andSVMalgorithm based on improved Simulated Annealing(SA)was proposed.Firstly,the data preprocessing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed.In this technique,the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data.Next is the training of a balanced dataset using SVM.Since this algorithm requires an iterative process to search for the best penalty parameter during training,an improved SA algorithm was proposed for this task.In this proposed improvement,a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process.Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM.Registering at an average of 89.65%of accuracy for the binary class classification has demonstrated the good performance of the proposed works.
基金the National Natural Science Foundation of China(No.51775090)the General Program of Civil Aviation Flight University of China(No.J2015-39)
文摘In order to increase the fault diagnosis efficiency and make the fault data mining be realized, the decision table containing numerical attributes must be discretized for further calculations. The discernibility matrix-based reduction method depends on whether the numerical attributes can be properly discretized or not.So a discretization algorithm based on particle swarm optimization(PSO) is proposed. Moreover, hybrid weights are adopted in the process of particles evolution. Comparative calculations for certain equipment are completed to demonstrate the effectiveness of the proposed algorithm. The results indicate that the proposed algorithm has better performance than other popular algorithms such as class-attribute interdependence maximization(CAIM)discretization method and entropy-based discretization method.
文摘Various Wireless Sensor Network(WSN)applications require the common task of collecting the data from the sensor nodes using the sink.Since the procedure of collecting data is iterative,an effective technique is necessary to obtain the data efficiently by reducing the consumption of nodal energy.Hence,a technique for data reduction in WSN is presented in this paper by proposing a prediction algorithm,called Hierarchical Fractional Bidirectional Least-Mean Square(HFBLMS)algorithm.The novel algorithm is designed by modifying Hierarchical Least-Mean Square(HLMS)algorithm with the inclusion of BLMS for bidirectional-based data prediction and Fractional Calculus(FC)in the weight update process.Data redundancy is achieved by transmitting only those data required based on the data predicted at the sensor node and the sink.Moreover,the proposed HFBLMS algorithm reduces the energy consumption in the network by the effective prediction attained by BLMS.Two metrics,such as energy consumption and prediction error,are used for the evaluation of performance of the HFBLMS prediction algorithm,where it can attain energy values of 0.3587 and 0.1953 at the maximum number of rounds and prediction errors of just 0.0213 and 0.0095,using air quality and localization datasets,respectively.
基金supported by the National Natural Science Foundation of China (No. 11502211)
文摘In aerodynamic optimization, global optimization methods such as genetic algorithms are preferred in many cases because of their advantage on reaching global optimum. However,for complex problems in which large number of design variables are needed, the computational cost becomes prohibitive, and thus original global optimization strategies are required. To address this need, data dimensionality reduction method is combined with global optimization methods, thus forming a new global optimization system, aiming to improve the efficiency of conventional global optimization. The new optimization system involves applying Proper Orthogonal Decomposition（POD） in dimensionality reduction of design space while maintaining the generality of original design space. Besides, an acceleration approach for samples calculation in surrogate modeling is applied to reduce the computational time while providing sufficient accuracy. The optimizations of a transonic airfoil RAE2822 and the transonic wing ONERA M6 are performed to demonstrate the effectiveness of the proposed new optimization system. In both cases, we manage to reduce the number of design variables from 20 to 10 and from 42 to 20 respectively. The new design optimization system converges faster and it takes 1/3 of the total time of traditional optimization to converge to a better design, thus significantly reducing the overall optimization time and improving the efficiency of conventional global design optimization method.
文摘In the preparation of firing tables, the determination of projectile drag coefficientsthrough firing test radar data reduction is very important. Many methods have been developed for this work but none of them appear to be satisfactory in one Way or another. Inthis paper a multi-spline model of drag coefficient (cd) curve is developed that can guaranteefirst derivative continuity of the cd curve and has good flexibility of fitting accurately to acd curve from subsonic up to supersonic range. Practical firing data reduction tests showboth fast convergence and accurate fitting results. Typical velocity fitting RMS errors are0.05-0.08 m/s.
基金supported by the National Natural Science Foundation of China (No.92371201).
文摘In the context of increasing dimensionality of design variables and the complexity of constraints, the efficacy of Surrogate-Based Optimization(SBO) is limited. The traditional linear and nonlinear dimensionality reduction algorithms are mainly to decompose the mathematical matrix composed of design variables or objective functions in various forms, the smoothness of the design space cannot be guaranteed in the process, and additional constraint functions need to be added in the optimization, which increases the calculation cost. This study presents a new parameterization method to improve both problems of SBO. The new parameterization is addressed by decoupling affine transformations(dilation, rotation, shearing, and translation) within the Grassmannian submanifold, which enables a separate representation of the physical information of the airfoil in a highdimensional space. Building upon this, Principal Geodesic Analysis(PGA) is employed to achieve geometric control, compress the design space, reduce the number of design variables, reduce the dimensions of design variables and enhance predictive performance during the surrogate optimization process. For comparison, a dimensionality reduction space is defined using 95% of the energy,and RAE 2822 for transonic conditions are used as demonstrations. This method significantly enhances the optimization efficiency of the surrogate model while effectively enabling geometric constraints. In three-dimensional problems, it enables simultaneous design of planar shapes for various components of the aircraft and high-order perturbation deformations. Optimization was applied to the ONERA M6 wing, achieving a lift-drag ratio of 18.09, representing a 27.25% improvement compared to the baseline configuration. In comparison to conventional surrogate model optimization methods, which only achieved a 17.97% improvement, this approach demonstrates its superiority.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA0350300)the National Natural Science Foundation of China(12203105,12103091,62394351,12073008)the China Manned Space Project(CMS-CSST-2021-A12,CMS-CSST-2021-B10).
文摘The Wide Field Survey Telescope(WFST)is located at 4200 m on Saishiteng Mountain in Lenghu,Qinghai Province,China.It features a primary mirror with a diameter of 2.5 m and a camera equipped with nine CCDs,providing a wide field of view of approximately 3×3 square degrees.Calibration parameters are essential to ensure the precision of astrometric observations with the WFST.These parameters are derived from geometric distortion(GD)and gaps through astrometric modeling and are subsequently validated via the Yao’An High Precision Telescope(YAHPT).The GD solutions show maximum distortions between 1.18 and 10.29 pixels for the WFST chips,with central chips exhibiting lower distortion.After applying the GD correction,the precision of the WFST reaches 4 mas.The interchip gaps of the WFST range from 1.922 mm to 7.765 mm,corresponding to 10μm/pixel,aligning with the design and measurements.The calibrated parameters guarantee that the WFST can perform highly accurate astrometric measurements.Furthermore,as the WFST undergoes updates,the parameter model remains consistently applicable.
基金supported by the National Key R&D Program of China (2022YFF0503400)the National Natural Science Foundation of China grant (U1931208)China Manned Space Program through its Space Application System.
文摘A detector's nondestructive readout mode allows its pixels to be read multiple times during integration,enabling generation of a series of"up-the-ramp"images that continuously accumulate photons between successive frames.Because noise is correlated across these images,optimal stacking generally requires the images to be weighted unequally to achieve the best possible target signal-to-noise ratio(SNR).Objects in the sky present wildly varied brightness characteristics,and the counts in individual pixels of the same object can also span wide ranges.Therefore,a single set of weights cannot be optimal in all cases.To ensure that the stacked image is easily calibratable,we apply the same weight to all pixels within the same frame.In practice,results for high-SNR cases degraded only slightly when we used weights derived for low-SNR cases,whereas the low-SNR cases remained more sensitive to the weights.Therefore,we propose a quasi-optimal stacking method that maximizes the stacked SNR for the case where the RSN=1 per pixel in the last frame and use simulated data to demonstrate that this approach enhances the SNR more strongly than the equal-weight stacking and ramp fitting methods.Furthermore,we estimate the improvements in the limiting magnitudes for the China Space Station Telescope using the proposed method.When compared with the conventional readout mode,which is equivalent to selecting the last frame from the nondestructive readout,stacking 30 up-the-ramp images can improve the limiting magnitude by approximately 0.5 mag for the telescope's near-infrared observations,effectively reducing readout noise by approximately 62%.
文摘This paper describes the data release of the LAMOST pilot survey, which includes data reduction, calibration, spectral analysis, data products and data access. The accuracy of the released data and the information about the FITS headers of spectra are also introduced. The released data set includes 319 000 spectra and a catalog of these objects.
文摘A data buoy, when moored at the sea, will be influenced by different environmental factors such as wind and wave current. For this reason, the stability of a data buoy is of great importance. This paper describes the analysis and calculation of the stability of a data buoy which has a weight suspended from its bottom under normal working conditions and the influence of the distance from the weight to the bottom of the buoy on the 'righting' ability of the buoy when it is capsized. The paper also provides the curves which show the influence of the size of the weight and the distance from the weight to the bottom of the buoy on the 'righting' ability of the buoy. All these are of great reference value for the design and use of a data buoy at sea and for the design of similar floating bodies. The results from calculations agree with those from the model experiment.
文摘Large scale dense Wireless Sensor Networks (WSNs) have been progressively employed for different classes of applications for the resolve of precise monitoring. As a result of high density of nodes, both spatially and temporally correlated information can be detected by several nodes. Hence, energy can be saved which is a major aspect of these networks. Moreover, by using these advantages of correlations, communication and data exchange can be reduced. In this paper, a novel algorithm that selects the data based on their contextual importance is proposed. The data, which are contextually important, are only transmitted to the upper layer and the remains are ignored. In this way, the proposed method achieves significant data reduction and in turn improves the energy conservation of data gathering.
文摘Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus,the total consumption of energy is optimal.However,the computational complexity will be increased due to data dimension,and this leads to increase in delay in network data transmission and reception.For solving the above-mentioned issues,an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis(ILDA)is proposed for 3D hierarchical clustering WSNs.The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs.This ILDA approach consists of four major steps such as data dimension reduction,distance similarity index introduction,double cluster head technique and node dormancy approach.This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head(CH)selection technique.According to node’s position and residual energy,optimal cluster-head function is generated,and every CH is elected by this formulation.For a 3D spherical structure,under the same network condition,the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering(IDHC)is compared with Distributed Energy-Efficient Clustering(DEEC),Hybrid Energy Efficient Distributed(HEED)and Stable Election Protocol(SEP)techniques.It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput,network residual energy,network lifetime and first node death round.
基金supported by the National Natural Science Foundation of China(Grant No.62471058)the State Key Laboratory of Information Photonics and Optical Communications,Beijing University of Posts and Telecommunications,China(Grant No.IPOC2022ZT10)。
文摘In this paper,a quantum-enhanced framework is proposed to optimize observation point selection in environmental data assimilation.The method transforms the task into a QUBO problem,balancing uncertainty reduction and spatial diversity.By leveraging a quantum-inspired optical Ising machine,it avoids the exponential complexity of classical optimization.Tests on the Lorenz-1996 model demonstrate its superiority over traditional methods,enhancing computational efficiency without loss of accuracy.The findings underscore the potential of quantum-inspired optimization for scalable,real-time assimilation in highresolution weather prediction,reducing dimensionality and computational cost.
基金co-supported by the ‘‘111" Project of China (No. B17037)the National Natural Science Foundation of China (No. 11772265)
文摘The Radial Basis Function（RBF） method with data reduction is an effective way to perform mesh deformation. However, for large deformations on meshes of complex aerodynamic configurations, the efficiency of the RBF mesh deformation method still needs to be further improved to fulfill the demand of practical application. To achieve this goal, a multistep RBF method based on a multilevel subspace RBF algorithm is presented to further improve the efficiency of the mesh deformation method in this research. A whole deformation is divided into a series of steps, and the supporting radius is adjusted in accordance with the maximal displacement error. Furthermore, parallel computing is applied to the interpolation to enhance the efficiency. Typical deformation problems of the NASA Common Research Model（CRM） configuration, the DLR-F6 wing-body-nacellepylon configuration, and the DLR-F11 high-lift configuration are tested to verify the feasibility of this method. Test results show that the presented multistep RBF mesh deformation method is efficient and robust in dealing with large deformation problems over complex geometries.
