The rapid development of mega low earth orbit(LEO)satellite networks is expected to have a significant impact on 6G networks.Unlike terrestrial networks,due to the high-speed movement of satellites,users will frequent...The rapid development of mega low earth orbit(LEO)satellite networks is expected to have a significant impact on 6G networks.Unlike terrestrial networks,due to the high-speed movement of satellites,users will frequently hand over between satellites even if their positions remain unchanged.Furthermore,the extensive coverage characteristic of satellites leads to massive users executing handovers simultaneously.To address these challenges,we propose a novel double grouping-based group handover scheme(DGGH)specifically tailored for mega LEO satellite networks.First,we develop a user grouping strategy based on beam-limited hierarchical clustering to divide users into distinct groups.Next,we reframe the challenge of managing multiple users’simultaneous handovers as a single-objective optimization problem,solving it with a satellite grouping strategy that leverages the accuracy of greedy algorithms and the simplicity of dynamic programming.Additionally,we develop a group handover algorithm based on minimal handover waiting time to improve the satellite grouping process further.The detailed steps of the DGGH scheme’s handover procedure are meticulously outlined.Comprehensive simulations show that the proposed DGGH scheme outperforms single-user handover schemes in terms of handover signaling over-head and handover success rate.展开更多
In response to the challenges presented by the unreliable identity of the master node,high communication overhead,and limited network support size within the Practical Byzantine Fault-Tolerant(PBFT)algorithm for conso...In response to the challenges presented by the unreliable identity of the master node,high communication overhead,and limited network support size within the Practical Byzantine Fault-Tolerant(PBFT)algorithm for consortium chains,we propose an improved PBFT algorithm based on XGBoost grouping called XG-PBFT in this paper.XG-PBFT constructs a dataset by training important parameters that affect node performance,which are used as classification indexes for nodes.The XGBoost algorithm then is employed to train the dataset,and nodes joining the system will be grouped according to the trained grouping model.Among them,the nodes with higher parameter indexes will be assigned to the consensus group to participate in the consensus,and the rest of the nodes will be assigned to the general group to receive the consensus results.In order to reduce the resource waste of the system,XG-PBFT optimizes the consensus protocol for the problem of high complexity of PBFT communication.Finally,we evaluate the performance of XG-PBFT.The experimental results show that XG-PBFT can significantly improve the performance of throughput,consensus delay and communication complexity compared to the original PBFT algorithm,and the performance enhancement is significant compared to other algorithms in the case of a larger number of nodes.The results demonstrate that the XG-PBFT algorithm is more suitable for large-scale consortium chains.展开更多
The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the intera...The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.展开更多
Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear ...Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.展开更多
The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly...The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective optimization.As seen,a variety of decision variable groupingalgorithms have been proposed.However,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto frontier.To solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is proposed.This algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision variables.In the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the algorithm.Evaluation validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.展开更多
Background Despite the recent progress in 3D point cloud processing using deep convolutional neural networks,the inability to extract local features remains a challenging problem.In addition,existing methods consider ...Background Despite the recent progress in 3D point cloud processing using deep convolutional neural networks,the inability to extract local features remains a challenging problem.In addition,existing methods consider only the spatial domain in the feature extraction process.Methods In this paper,we propose a spectral and spatial aggregation convolutional network(S^(2)ANet),which combines spectral and spatial features for point cloud processing.First,we calculate the local frequency of the point cloud in the spectral domain.Then,we use the local frequency to group points and provide a spectral aggregation convolution module to extract the features of the points grouped by the local frequency.We simultaneously extract the local features in the spatial domain to supplement the final features.Results S^(2)ANet was applied in several point cloud analysis tasks;it achieved stateof-the-art classification accuracies of 93.8%,88.0%,and 83.1%on the ModelNet40,ShapeNetCore,and ScanObjectNN datasets,respectively.For indoor scene segmentation,training and testing were performed on the S3DIS dataset,and the mean intersection over union was 62.4%.Conclusions The proposed S^(2)ANet can effectively capture the local geometric information of point clouds,thereby improving accuracy on various tasks.展开更多
To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. ...To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. Compared with the existing fixed-window-based models, the proposed one is an adaptive window-like model that introduces the perceptual grouping strategy into the IQA model. It works as follows： first,it preprocesses the images by clustering similar pixels into a group to the greatest extent; then the structural similarity is used to compute the similarity of the superpixels between reference and distorted images; finally, it integrates all the similarity of superpixels of an image to yield a quality score. Experimental results on three databases（ LIVE, IVC and MICT） showthat the proposed method yields good performance in terms of correlation with human judgments of visual quality.展开更多
The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA...The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA)has been widely used to solve binary problems in the real world.The classification of DCA depends on a data preprocessing procedure to generate input signals,where feature selection and signal categorization are themain work.However,the results of these studies also show that the signal generation of DCA is relatively weak,and all of them utilized a filter strategy to remove unimportant attributes.Ignoring filtered features and applying expertise may not produce an optimal classification result.To overcome these limitations,this study models feature selection and signal categorization into feature grouping problems.This study hybridizes Grouping Genetic Algorithm(GGA)with DCA to propose a novel DCA version,GGA-DCA,for accomplishing feature selection and signal categorization in a search process.The GGA-DCA aims to search for the optimal feature grouping scheme without expertise automatically.In this study,the data coding and operators of GGA are redefined for grouping tasks.The experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.展开更多
Wind loading is one of the most important loads for controlling the design of large-span roof structures. Equivalent static wind loads, which can generally aim at determining a specific response, are widely used by st...Wind loading is one of the most important loads for controlling the design of large-span roof structures. Equivalent static wind loads, which can generally aim at determining a specific response, are widely used by structural designers. A method for equivalent static wind loads applicable to multi-responses is proposed in this paper. A modified load- response-correlation （LRC） method corresponding to a particular peak response is presented, and the similarity algorithm implemented for the group response is described. The main idea of the algorithm is that two responses can be put into one group if the value of one response is close to that of the other response, when the structure is subjected to equivalent static wind loads aiming at the other response. Based on the modified LRC, the grouping response method is put forward to construct equivalent static wind loading. This technique can simultaneously reproduce peak responses for some grouped responses. To verify its computational accuracy, the method is applied to an actual large-span roof structure. Calculation results show that when the similarity of responses in the same group is high, equivalent static wind loads with high accuracy and reasonable magnitude of equivalent static wind distribution can be achieved.展开更多
BACKGROUND: Imaging examination is important for hepatic cirrhosis. But the relationship between magnetic resonance (MR), computed tomography (CT) or ultrasound findings and pathological groups, degree, or reserve fun...BACKGROUND: Imaging examination is important for hepatic cirrhosis. But the relationship between magnetic resonance (MR), computed tomography (CT) or ultrasound findings and pathological groups, degree, or reserve function of the cirrhotic liver is not clear. In this study, we investigated the relationship between the CT groupings of liver cirrhosis and its complications and clinical conditions. METHODS: The CT findings in 357 patients with liver cirrhosis were grouped. The complications were analyzed, included splenomegaly, varicose collateral veins, ascites, pleurorrhea, primary liver carcinoma, gallbladder stone, etc. Blood routine (BRt), and serum usea nitrogen (SUN), creatinine and uric acid were measured and hypersplenia and liver-kidney syndrome were estimated. RESULTS: Three hundred and fifty-seven patients with cirrhosis were divided into homogeneous group (87 patients, 24.4%), segmental group (41, 11.5%), and nodal group (229, 64.2%). The grade of spleen enlargement in the segmental and the nodal groups was significantly greater than that in the homogeneous group (P <0. 01 and P<0.001). The patients with varices were shown in a descending order in the segmental group (70.7%), the nodal group (17.0%) and the homogeneous group (2.3%), respectively. Significant difference was observed among the 3 groups ( P < 0.001). Ascites was seen in 182 patients (79.5%) of the nodal group, in 11 patients (26.8%) of the segmental group and in 9 patients ( 10.3%) of the homogeneous group (P<0.001). Sixty-eight patients (29.7%) in the nodal group had primary liver carcinoma and 1 (2.4%) in the segmental group and 5 (5.8%) in the homogeneous group (P<0.001). The number of patients with decreased concentration of hematoglobin in the nodal group was more than that in the homogeneous group ( P < 0. 001). The mean values of hematoglobin and platelet in the nodal group and the segmental group were significantly lower than those in the homogeneous group ( P < 0. 05 ). The number of patients with increased concentration of SUN in the nodal group and the segmental group was more than that in the homogeneous group (P<0.005). The concentration of SUN in the nodal group was significantly higher than that in the homogeneous group (P <0.002). CONCLUSION: There is a close relationship between the grouping of liver cirrhosis by CT findings and complications caused by the cirrhosis. The grouping is significant for estimating clinical conditions.展开更多
Space-based Automatic Dependent Surveillance-Broadcast(ADS-B)technology can eliminate the blind spots of terrestrial ADS-B systems because of its global coverage capability.However,the space-based ADS-B system faces n...Space-based Automatic Dependent Surveillance-Broadcast(ADS-B)technology can eliminate the blind spots of terrestrial ADS-B systems because of its global coverage capability.However,the space-based ADS-B system faces new problems such as extremely low Signal-toNoise Ratio(SNR)and serious co-channel interference,which result in long update intervals.To minimize the position message update interval at an update probability of 95%with full coverage constraint,this paper presents an optimization model of digital multi-beamforming for space-based ADS-B.Then,a coevolution method DECCG_A&A is proposed to enhance the optimization efficiency by using an improved adaptive grouping strategy.The strategy is based on the locations of uncovered areas and the aircraft density under the coverage of each beam.Simulation results show that the update interval can be effectively controlled to be below 8 seconds compared with other existing methods,and DECCG_A&A is superior in convergence to the Genetic Algorithm(GA)as well as the coevolution algorithms using other grouping strategies.Overall,the proposed optimization model and method can significantly reduce the update interval,thus improving the surveillance performance of space-based ADS-B for air traffic control.展开更多
It is difficult to rescue people from outside, and emergency evacuation is still a main measure to decrease casualties in high-rise building fires. To improve evacuation efficiency, a valid and easily manipulated grou...It is difficult to rescue people from outside, and emergency evacuation is still a main measure to decrease casualties in high-rise building fires. To improve evacuation efficiency, a valid and easily manipulated grouping evacuation strategy is proposed. Occupants escape in groups according to the shortest evacuation route is determined by graph theory. In order to evaluate and find the optimal grouping, computational experiments are performed to design and simulate the evacuation processes. A case study shown the application in detail and quantitative research conclusions is obtained. The thoughts and approaches of this study can be used to guide actual high-rise building evacuation processes in future.展开更多
A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we pro...A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.展开更多
In this paper,a new communication model is built named grouping D2D(GD2D).Different from the traditional D2D coordination,we proposed GD2D communication in licensed and unlicensed spectrum simultaneously.We formulate ...In this paper,a new communication model is built named grouping D2D(GD2D).Different from the traditional D2D coordination,we proposed GD2D communication in licensed and unlicensed spectrum simultaneously.We formulate a resource allocation problem,which aims at maximizing the energy efficiency(EE)of the system while guaranteeing the quality-of-service(Qos)of users.To efficiently solve this problem,the non-convex optimization problem is first transformed into a convex optimization problem.By transforming the fractional-form problem into an equivalent subtractive-form problem,an iterative power allocation algorithm is proposed to maximize the system EE.Moreover,the optimal closedform power allocation expressions are derived by the Lagrangian approach.Simulation results show that our algorithm achieves higher EE performance than the traditional D2D communication scheme.展开更多
The effects of different yaw angles on the aerodynamic performance of city electric multiple units(EMUs)were investigated in a wind tunnel using a 1:16.8 scaled model.Pressure scanning valve and six-component box-type...The effects of different yaw angles on the aerodynamic performance of city electric multiple units(EMUs)were investigated in a wind tunnel using a 1:16.8 scaled model.Pressure scanning valve and six-component box-type aerodynamic balance were used to test the pressure distribution and aerodynamic force of the head car respectively from the 1.5-and 3-coach grouping city EMU models.Meanwhile,the effects of the yaw angles on the pressure distribution of the streamlined head as well as the aerodynamic forces of the train were analyzed.The experimental results showed that the pressure coefficient was the smallest at the maximum slope of the main shape-line.The side force coefficient and pressure coefficient along the head car cross-section were most affected by crosswind when the yaw angle was 55°,and replacing a 3-coach grouping with a 1.5-coach grouping had obvious advantages for wind tunnel testing when the yaw angle was within 24.2°.In addition,the relative errors of lift coefficient C_(L),roll moment coefficient C_(Mx),side force coefficient C_(S),and drag coefficient C_(D)between the 1.5-and 3-coach cases were below 5.95%,which all met the requirements of the experimental accuracy.展开更多
IEEE 802.11ax,which is an emerging WLAN standard,aims at providing highly efficient communication in ultra-dense wireless networks.However,due to a large number of stations(STAs)in the ultra-dense device deployment sc...IEEE 802.11ax,which is an emerging WLAN standard,aims at providing highly efficient communication in ultra-dense wireless networks.However,due to a large number of stations(STAs)in the ultra-dense device deployment scenarios,the potentially high packet collision rate significantly decreases the communication efficiency of WLAN.In this paper,we propose an adaptive STA grouping scheme to overcome this dense network challenge in IEEE 802.11ax by using Buffer State Report(BSR)based Two-stage Mechanism(BTM).In order to achieve the optimal efficiency of BSR delivery,we analyze the functional relationship between STA number in group and Resource Unit(RU)efficiency.Based on this analysis results,an adaptive STA grouping algorithm with variable group size is proposed to achieve efficient grouping in BTM.The numerical results demonstrate that the proposed adaptive BTM grouping algorithm significantly improves the BSR delivery efficiency and the throughput of overall system and each STA in the ultra-dense wireless network.展开更多
In this paper,a novel bit-level image encryption method based on dynamic grouping is proposed.In the proposed method,the plain-image is divided into several groups randomly,then permutation-diffusion process on bit le...In this paper,a novel bit-level image encryption method based on dynamic grouping is proposed.In the proposed method,the plain-image is divided into several groups randomly,then permutation-diffusion process on bit level is carried out.The keystream generated by logistic map is related to the plain-image,which confuses the relationship between the plain-image and the cipher-image.The computer simulation results of statistical analysis,information entropy analysis and sensitivity analysis show that the proposed encryption method is secure and reliable enough to be used for communication application.展开更多
Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity.The user’s access over the internet creates massive data processing over the in...Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity.The user’s access over the internet creates massive data processing over the internet.Big data require an intelligent feature selection model by addressing huge varieties of data.Traditional feature selection techniques are only applicable to simple data mining.Intelligent techniques are needed in big data processing and machine learning for an efficient classification.Major feature selection algorithms read the input features as they are.Then,the features are preprocessed and classified.Here,an algorithm does not consider the relatedness.During feature selection,all features are misread as outputs.Accordingly,a less optimal solution is achieved.In our proposed research,we focus on the feature selection by using supervised learning techniques called grey wolf optimization(GWO)with decomposed random differential grouping(DrnDG-GWO).First,decomposition of features into subsets based on relatedness in variables is performed.Random differential grouping is performed using a fitness value of two variables.Now,every subset is regarded as a population in GWO techniques.The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research.Once the features are optimized,we classify using advanced kNN process for accurate data classification.The result of DrnDGGWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm.The accuracy and time complexity of the proposed algorithm are 98%and 5 s,which are better than the existing techniques.展开更多
Genus Grateloupia is one of the most speciose genera in family Halymeniales. It is also one of the sources for natural materials, food and medicine. With different environments, Grateloupia change their morphological ...Genus Grateloupia is one of the most speciose genera in family Halymeniales. It is also one of the sources for natural materials, food and medicine. With different environments, Grateloupia change their morphological characteristics, making their morphological identification very difficult. In addition, few of the species diversity in this genus has been described before. In this study, phylogenetic analysis based on rbc L gene sequence was employed to group Grateloupia collected from three locations along Chinese coast. The microsatellites were also used to evaluate their genetic diversity. In total, the tissue parts of 6 putative species were collected from G. asiatica, G. livida, G. lanceolate, G. catenata, G. turuturu and G. filicina. In order to evaluate their genetic diversity and then conserve them better, 40 microsatellites available for Grateloupia were used to evaluate their genetic diversity, and 11 microsatellites were found to be applicable to determine the genetic diversity of G. asiatica. It was found that the genetic diversity of G. asiatica around Qingdao was very rich. We suggested that the species of genus Grateloupia should be identified based on rbc L phylogenetic analysis before the diversity evaluation with microsatellites. The microsatellites should be developed for each species of Grateloupia so that their genetic diversity can be evaluated appropriately.展开更多
A review concerning the methods of studying and describing wave groups is presented in this paper. After analysing 78 field records collected in the Shijiu Port, China, the measured parameters of wave groups and some ...A review concerning the methods of studying and describing wave groups is presented in this paper. After analysing 78 field records collected in the Shijiu Port, China, the measured parameters of wave groups and some factors describing wave groupness and their variations are given. Moreover, these results are compared with those of theory.展开更多
基金supported in part by the State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster(No.MS01240103)the National Natural Science Foundation of China(No.62071146,No.62431009)+1 种基金the National 2011 Collaborative Innovation Center of Wireless Communication Technologies(No.2242022k60006)the Research Project Fund of Songjiang Laboratory(No.SL20230104).
文摘The rapid development of mega low earth orbit(LEO)satellite networks is expected to have a significant impact on 6G networks.Unlike terrestrial networks,due to the high-speed movement of satellites,users will frequently hand over between satellites even if their positions remain unchanged.Furthermore,the extensive coverage characteristic of satellites leads to massive users executing handovers simultaneously.To address these challenges,we propose a novel double grouping-based group handover scheme(DGGH)specifically tailored for mega LEO satellite networks.First,we develop a user grouping strategy based on beam-limited hierarchical clustering to divide users into distinct groups.Next,we reframe the challenge of managing multiple users’simultaneous handovers as a single-objective optimization problem,solving it with a satellite grouping strategy that leverages the accuracy of greedy algorithms and the simplicity of dynamic programming.Additionally,we develop a group handover algorithm based on minimal handover waiting time to improve the satellite grouping process further.The detailed steps of the DGGH scheme’s handover procedure are meticulously outlined.Comprehensive simulations show that the proposed DGGH scheme outperforms single-user handover schemes in terms of handover signaling over-head and handover success rate.
文摘In response to the challenges presented by the unreliable identity of the master node,high communication overhead,and limited network support size within the Practical Byzantine Fault-Tolerant(PBFT)algorithm for consortium chains,we propose an improved PBFT algorithm based on XGBoost grouping called XG-PBFT in this paper.XG-PBFT constructs a dataset by training important parameters that affect node performance,which are used as classification indexes for nodes.The XGBoost algorithm then is employed to train the dataset,and nodes joining the system will be grouped according to the trained grouping model.Among them,the nodes with higher parameter indexes will be assigned to the consensus group to participate in the consensus,and the rest of the nodes will be assigned to the general group to receive the consensus results.In order to reduce the resource waste of the system,XG-PBFT optimizes the consensus protocol for the problem of high complexity of PBFT communication.Finally,we evaluate the performance of XG-PBFT.The experimental results show that XG-PBFT can significantly improve the performance of throughput,consensus delay and communication complexity compared to the original PBFT algorithm,and the performance enhancement is significant compared to other algorithms in the case of a larger number of nodes.The results demonstrate that the XG-PBFT algorithm is more suitable for large-scale consortium chains.
基金supported in part by the Central Government Guides Local Science and TechnologyDevelopment Funds(Grant No.YDZJSX2021A038)in part by theNational Natural Science Foundation of China under(Grant No.61806138)in part by the China University Industry-University-Research Collaborative Innovation Fund(Future Network Innovation Research and Application Project)(Grant 2021FNA04014).
文摘The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.
基金supported by the Key R&D Project of the Ministry of Science and Technology of China(2020YFB1808005)。
文摘Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.
基金the Liaoning Province Nature Fundation Project(2022-MS-291)the National Programme for Foreign Expert Projects(G2022006008L)+2 种基金the Basic Research Projects of Liaoning Provincial Department of Education(LJKMZ20220781,LJKMZ20220783,LJKQZ20222457)King Saud University funded this study through theResearcher Support Program Number(RSPD2023R704)King Saud University,Riyadh,Saudi Arabia.
文摘The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective optimization.As seen,a variety of decision variable groupingalgorithms have been proposed.However,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto frontier.To solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is proposed.This algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision variables.In the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the algorithm.Evaluation validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.
文摘Background Despite the recent progress in 3D point cloud processing using deep convolutional neural networks,the inability to extract local features remains a challenging problem.In addition,existing methods consider only the spatial domain in the feature extraction process.Methods In this paper,we propose a spectral and spatial aggregation convolutional network(S^(2)ANet),which combines spectral and spatial features for point cloud processing.First,we calculate the local frequency of the point cloud in the spectral domain.Then,we use the local frequency to group points and provide a spectral aggregation convolution module to extract the features of the points grouped by the local frequency.We simultaneously extract the local features in the spatial domain to supplement the final features.Results S^(2)ANet was applied in several point cloud analysis tasks;it achieved stateof-the-art classification accuracies of 93.8%,88.0%,and 83.1%on the ModelNet40,ShapeNetCore,and ScanObjectNN datasets,respectively.For indoor scene segmentation,training and testing were performed on the S3DIS dataset,and the mean intersection over union was 62.4%.Conclusions The proposed S^(2)ANet can effectively capture the local geometric information of point clouds,thereby improving accuracy on various tasks.
基金The National Natural Science Foundation of China(No.81272501)the National Basic Research Program of China(973Program)(No.2011CB707904)Taishan Scholars Program of Shandong Province,China(No.ts20120505)
文摘To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. Compared with the existing fixed-window-based models, the proposed one is an adaptive window-like model that introduces the perceptual grouping strategy into the IQA model. It works as follows： first,it preprocesses the images by clustering similar pixels into a group to the greatest extent; then the structural similarity is used to compute the similarity of the superpixels between reference and distorted images; finally, it integrates all the similarity of superpixels of an image to yield a quality score. Experimental results on three databases（ LIVE, IVC and MICT） showthat the proposed method yields good performance in terms of correlation with human judgments of visual quality.
基金NSFC http://www.nsfc.gov.cn/for the support through Grants No.61877045Fundamental Research Project of Shenzhen Science and Technology Program for the support through Grants No.JCYJ2016042815-3956266.
文摘The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA)has been widely used to solve binary problems in the real world.The classification of DCA depends on a data preprocessing procedure to generate input signals,where feature selection and signal categorization are themain work.However,the results of these studies also show that the signal generation of DCA is relatively weak,and all of them utilized a filter strategy to remove unimportant attributes.Ignoring filtered features and applying expertise may not produce an optimal classification result.To overcome these limitations,this study models feature selection and signal categorization into feature grouping problems.This study hybridizes Grouping Genetic Algorithm(GGA)with DCA to propose a novel DCA version,GGA-DCA,for accomplishing feature selection and signal categorization in a search process.The GGA-DCA aims to search for the optimal feature grouping scheme without expertise automatically.In this study,the data coding and operators of GGA are redefined for grouping tasks.The experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.
基金Ministry of Science and Technology of China Under Grant No.SLDRCE10-B-04the National Natural Science Foundation Under Grant No.50621062
文摘Wind loading is one of the most important loads for controlling the design of large-span roof structures. Equivalent static wind loads, which can generally aim at determining a specific response, are widely used by structural designers. A method for equivalent static wind loads applicable to multi-responses is proposed in this paper. A modified load- response-correlation （LRC） method corresponding to a particular peak response is presented, and the similarity algorithm implemented for the group response is described. The main idea of the algorithm is that two responses can be put into one group if the value of one response is close to that of the other response, when the structure is subjected to equivalent static wind loads aiming at the other response. Based on the modified LRC, the grouping response method is put forward to construct equivalent static wind loading. This technique can simultaneously reproduce peak responses for some grouped responses. To verify its computational accuracy, the method is applied to an actual large-span roof structure. Calculation results show that when the similarity of responses in the same group is high, equivalent static wind loads with high accuracy and reasonable magnitude of equivalent static wind distribution can be achieved.
文摘BACKGROUND: Imaging examination is important for hepatic cirrhosis. But the relationship between magnetic resonance (MR), computed tomography (CT) or ultrasound findings and pathological groups, degree, or reserve function of the cirrhotic liver is not clear. In this study, we investigated the relationship between the CT groupings of liver cirrhosis and its complications and clinical conditions. METHODS: The CT findings in 357 patients with liver cirrhosis were grouped. The complications were analyzed, included splenomegaly, varicose collateral veins, ascites, pleurorrhea, primary liver carcinoma, gallbladder stone, etc. Blood routine (BRt), and serum usea nitrogen (SUN), creatinine and uric acid were measured and hypersplenia and liver-kidney syndrome were estimated. RESULTS: Three hundred and fifty-seven patients with cirrhosis were divided into homogeneous group (87 patients, 24.4%), segmental group (41, 11.5%), and nodal group (229, 64.2%). The grade of spleen enlargement in the segmental and the nodal groups was significantly greater than that in the homogeneous group (P <0. 01 and P<0.001). The patients with varices were shown in a descending order in the segmental group (70.7%), the nodal group (17.0%) and the homogeneous group (2.3%), respectively. Significant difference was observed among the 3 groups ( P < 0.001). Ascites was seen in 182 patients (79.5%) of the nodal group, in 11 patients (26.8%) of the segmental group and in 9 patients ( 10.3%) of the homogeneous group (P<0.001). Sixty-eight patients (29.7%) in the nodal group had primary liver carcinoma and 1 (2.4%) in the segmental group and 5 (5.8%) in the homogeneous group (P<0.001). The number of patients with decreased concentration of hematoglobin in the nodal group was more than that in the homogeneous group ( P < 0. 001). The mean values of hematoglobin and platelet in the nodal group and the segmental group were significantly lower than those in the homogeneous group ( P < 0. 05 ). The number of patients with increased concentration of SUN in the nodal group and the segmental group was more than that in the homogeneous group (P<0.005). The concentration of SUN in the nodal group was significantly higher than that in the homogeneous group (P <0.002). CONCLUSION: There is a close relationship between the grouping of liver cirrhosis by CT findings and complications caused by the cirrhosis. The grouping is significant for estimating clinical conditions.
文摘Space-based Automatic Dependent Surveillance-Broadcast(ADS-B)technology can eliminate the blind spots of terrestrial ADS-B systems because of its global coverage capability.However,the space-based ADS-B system faces new problems such as extremely low Signal-toNoise Ratio(SNR)and serious co-channel interference,which result in long update intervals.To minimize the position message update interval at an update probability of 95%with full coverage constraint,this paper presents an optimization model of digital multi-beamforming for space-based ADS-B.Then,a coevolution method DECCG_A&A is proposed to enhance the optimization efficiency by using an improved adaptive grouping strategy.The strategy is based on the locations of uncovered areas and the aircraft density under the coverage of each beam.Simulation results show that the update interval can be effectively controlled to be below 8 seconds compared with other existing methods,and DECCG_A&A is superior in convergence to the Genetic Algorithm(GA)as well as the coevolution algorithms using other grouping strategies.Overall,the proposed optimization model and method can significantly reduce the update interval,thus improving the surveillance performance of space-based ADS-B for air traffic control.
基金supported by Beijing University of Civil Engineering and Architecture Nature Science(ZF16078,X18067)
文摘It is difficult to rescue people from outside, and emergency evacuation is still a main measure to decrease casualties in high-rise building fires. To improve evacuation efficiency, a valid and easily manipulated grouping evacuation strategy is proposed. Occupants escape in groups according to the shortest evacuation route is determined by graph theory. In order to evaluate and find the optimal grouping, computational experiments are performed to design and simulate the evacuation processes. A case study shown the application in detail and quantitative research conclusions is obtained. The thoughts and approaches of this study can be used to guide actual high-rise building evacuation processes in future.
基金supported by the National Natural Science Foundation of China(6200220861572063+1 种基金61603225)the Natural Science Foundation of Shandong Province(ZR2016FQ04)。
文摘A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.
基金supported in part by the National Natural Science Foundation of China under Grant no.61473066 and Grant no.61601109in part by the Fundamental Research Funds for the Central Universities under Grant No.N152305001.
文摘In this paper,a new communication model is built named grouping D2D(GD2D).Different from the traditional D2D coordination,we proposed GD2D communication in licensed and unlicensed spectrum simultaneously.We formulate a resource allocation problem,which aims at maximizing the energy efficiency(EE)of the system while guaranteeing the quality-of-service(Qos)of users.To efficiently solve this problem,the non-convex optimization problem is first transformed into a convex optimization problem.By transforming the fractional-form problem into an equivalent subtractive-form problem,an iterative power allocation algorithm is proposed to maximize the system EE.Moreover,the optimal closedform power allocation expressions are derived by the Lagrangian approach.Simulation results show that our algorithm achieves higher EE performance than the traditional D2D communication scheme.
基金Project(2020YFA0710903) supported by the National Key R&D Program of ChinaProjects(2020zzts111, 2020zzts117)supported by the Graduate Student Independent Innovation Project of Central South University,ChinaProject(202037)supported by Transport Department of Hunan Province Technology Innovation Project,China。
文摘The effects of different yaw angles on the aerodynamic performance of city electric multiple units(EMUs)were investigated in a wind tunnel using a 1:16.8 scaled model.Pressure scanning valve and six-component box-type aerodynamic balance were used to test the pressure distribution and aerodynamic force of the head car respectively from the 1.5-and 3-coach grouping city EMU models.Meanwhile,the effects of the yaw angles on the pressure distribution of the streamlined head as well as the aerodynamic forces of the train were analyzed.The experimental results showed that the pressure coefficient was the smallest at the maximum slope of the main shape-line.The side force coefficient and pressure coefficient along the head car cross-section were most affected by crosswind when the yaw angle was 55°,and replacing a 3-coach grouping with a 1.5-coach grouping had obvious advantages for wind tunnel testing when the yaw angle was within 24.2°.In addition,the relative errors of lift coefficient C_(L),roll moment coefficient C_(Mx),side force coefficient C_(S),and drag coefficient C_(D)between the 1.5-and 3-coach cases were below 5.95%,which all met the requirements of the experimental accuracy.
文摘IEEE 802.11ax,which is an emerging WLAN standard,aims at providing highly efficient communication in ultra-dense wireless networks.However,due to a large number of stations(STAs)in the ultra-dense device deployment scenarios,the potentially high packet collision rate significantly decreases the communication efficiency of WLAN.In this paper,we propose an adaptive STA grouping scheme to overcome this dense network challenge in IEEE 802.11ax by using Buffer State Report(BSR)based Two-stage Mechanism(BTM).In order to achieve the optimal efficiency of BSR delivery,we analyze the functional relationship between STA number in group and Resource Unit(RU)efficiency.Based on this analysis results,an adaptive STA grouping algorithm with variable group size is proposed to achieve efficient grouping in BTM.The numerical results demonstrate that the proposed adaptive BTM grouping algorithm significantly improves the BSR delivery efficiency and the throughput of overall system and each STA in the ultra-dense wireless network.
文摘In this paper,a novel bit-level image encryption method based on dynamic grouping is proposed.In the proposed method,the plain-image is divided into several groups randomly,then permutation-diffusion process on bit level is carried out.The keystream generated by logistic map is related to the plain-image,which confuses the relationship between the plain-image and the cipher-image.The computer simulation results of statistical analysis,information entropy analysis and sensitivity analysis show that the proposed encryption method is secure and reliable enough to be used for communication application.
文摘Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity.The user’s access over the internet creates massive data processing over the internet.Big data require an intelligent feature selection model by addressing huge varieties of data.Traditional feature selection techniques are only applicable to simple data mining.Intelligent techniques are needed in big data processing and machine learning for an efficient classification.Major feature selection algorithms read the input features as they are.Then,the features are preprocessed and classified.Here,an algorithm does not consider the relatedness.During feature selection,all features are misread as outputs.Accordingly,a less optimal solution is achieved.In our proposed research,we focus on the feature selection by using supervised learning techniques called grey wolf optimization(GWO)with decomposed random differential grouping(DrnDG-GWO).First,decomposition of features into subsets based on relatedness in variables is performed.Random differential grouping is performed using a fitness value of two variables.Now,every subset is regarded as a population in GWO techniques.The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research.Once the features are optimized,we classify using advanced kNN process for accurate data classification.The result of DrnDGGWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm.The accuracy and time complexity of the proposed algorithm are 98%and 5 s,which are better than the existing techniques.
基金supported by the Marine Industry Research Special Funds for Public Welfare Projects(No.201205024)
文摘Genus Grateloupia is one of the most speciose genera in family Halymeniales. It is also one of the sources for natural materials, food and medicine. With different environments, Grateloupia change their morphological characteristics, making their morphological identification very difficult. In addition, few of the species diversity in this genus has been described before. In this study, phylogenetic analysis based on rbc L gene sequence was employed to group Grateloupia collected from three locations along Chinese coast. The microsatellites were also used to evaluate their genetic diversity. In total, the tissue parts of 6 putative species were collected from G. asiatica, G. livida, G. lanceolate, G. catenata, G. turuturu and G. filicina. In order to evaluate their genetic diversity and then conserve them better, 40 microsatellites available for Grateloupia were used to evaluate their genetic diversity, and 11 microsatellites were found to be applicable to determine the genetic diversity of G. asiatica. It was found that the genetic diversity of G. asiatica around Qingdao was very rich. We suggested that the species of genus Grateloupia should be identified based on rbc L phylogenetic analysis before the diversity evaluation with microsatellites. The microsatellites should be developed for each species of Grateloupia so that their genetic diversity can be evaluated appropriately.
文摘A review concerning the methods of studying and describing wave groups is presented in this paper. After analysing 78 field records collected in the Shijiu Port, China, the measured parameters of wave groups and some factors describing wave groupness and their variations are given. Moreover, these results are compared with those of theory.
