The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influen...The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.展开更多
Accompanying the unceasing progress of integrated circuit manufacturing technology, the mainstream production mode of current semiconductor wafer fabrication is featured with multi-variety, small batch, and individual...Accompanying the unceasing progress of integrated circuit manufacturing technology, the mainstream production mode of current semiconductor wafer fabrication is featured with multi-variety, small batch, and individual customization, which poses a huge challenge to the scheduling of cluster tools with single-wafer-type fabrication. Concurrent processing multiple wafer types in cluster tools, as a novel production pattern, has drawn increasing attention from industry to academia, whereas the corresponding research remains insufficient. This paper investigates the scheduling problems of dual-arm cluster tools with multiple wafer types and residency time constraints. To pursue an easy-to-implement cyclic operation under diverse flow patterns,we develop a novel robot activity strategy called multiplex swap sequence. In the light of the virtual module technology, the workloads that stem from bottleneck process steps and asymmetrical process configuration are balanced satisfactorily. Moreover, several sufficient and necessary conditions with closed-form expressions are obtained for checking the system's schedulability. Finally, efficient algorithms with polynomial complexity are developed to find the periodic scheduling, and its practicability and availability are demonstrated by the offered illustrative examples.展开更多
Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled.However,multiple kernel clustering for incomplete da...Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled.However,multiple kernel clustering for incomplete data is a critical yet challenging task.Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local optimum.To address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete data.The AMKC method rst clusters the initialized incomplete data.Then,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination coefcients.Finally,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel clustering.The three stages in this process are carried out simultaneously until the convergence condition is met.Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art competitors.Meanwhile,the proposed method gains fast convergence speed.展开更多
The current study examined the prevalence and clustering of 5 health-risk behaviors among adolescents in Hawaii, including physical inactivity, low fruit and vegetable consumption, junk food consumption, excessive tel...The current study examined the prevalence and clustering of 5 health-risk behaviors among adolescents in Hawaii, including physical inactivity, low fruit and vegetable consumption, junk food consumption, excessive television time, and inadequate sleep. High school students were recruited from 5 classrooms in Oahu Hawaii. Data were collected in the spring semester of 2011. Proportions were used to describe the prevalence of single and multiple health risk behaviors. Significant health behavior clusters were revealed using an observed-to-expected (O/E) ratio method. Participating adolescents (n = 114) were 11th and 12th grade students with a mean age of 16.28 (SD = 0.62). Participants were predominantly female (75%) and Filipino-American (68%). Seventy-seven percent of adolescents were physically inactive, 90% watched excessive TV, 66% consumed inadequate fruits and vegetables, 94% reported inadequate levels of sleep, and 80% consumed excessive junk food. Overall, 94% reported at least 3 risk factors, 73% reported at least 4 risk factors, and 37% reported all 5 risk factors. No significant clusters were found. Conclusion: Health-risk behaviors cluster and occur more often than expected among adolescents living in Hawaii. Non-significant clustering may be due to insufficient variability within the sample data;future examinations of this highly understudied population are necessary.展开更多
[ Objective] This study aimed to investigate the effects of EMS （ethyl methane sulfonate） treatments on multiplication and differentiation of sugarcane embryonic cell clusters. [ Method] Sugarcane variety Xintaitang...[ Objective] This study aimed to investigate the effects of EMS （ethyl methane sulfonate） treatments on multiplication and differentiation of sugarcane embryonic cell clusters. [ Method] Sugarcane variety Xintaitang 22 （ROC22） was used as the experimental material. After treated with different concentrations of EMS for different time, sugarcane embryonic cell clusters were collected for subculture, differentiation and rooting, to compare and analyze the correlation of differ- ent EMS treatments with the muhiplicafion and differentiation of sugarcane embryonic cell clusters. [ Result] Treating ROC22 embryonic cell clusters with 0. 10% -0.15% EMS for4-6 h led to the best results, which reached the level of semi-lethal dose. Sugarcane embryonic cell clusters treated with EMS were adopted for subculture; results indicated that browning rate of cell clusters was higher than that in control （CK） but embryonic structure proportion was lower than that in control ; in addition, multiplication multiple of sugarcane embryonic cell clusters was also lower than that in control. After treated with EMS, sugarcane em- bryonic cell clusters exhibited significantly lower bud differentiation rate and higher browning rate compared with control. Furthermore, treating sugarcane embryonic cell clusters with 0.15% EMS for 2 h was conducive to plantlet emergence and rooting of sugarcane embryonic cell clusters. [ Conclusion] This study provided the- oretical basis for effective mutagenesis of sugarcane using EMS.展开更多
Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assum...Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance,while local kernel alignment on different sample actually has different contribution to clustering performance.Therefore this assumption could have a negative effective on clustering performance.To solve this issue,we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment,which can learn a proper weight to clustering performance for each local kernel alignment.Specifically,we introduce a new optimization variable-weight-to denote the contribution of each local kernel alignment to clustering performance,and then,weight,kernel combination coefficients and cluster membership are alternately optimized under kernel alignment frame.In addition,we develop a three-step alternate iterative optimization algorithm to address the resultant optimization problem.Broad experiments on five benchmark data sets have been put into effect to evaluate the clustering performance of the proposed algorithm.The experimental results distinctly demonstrate that the proposed algorithm outperforms the typical multiple kernel clustering algorithms,which illustrates the effectiveness of the proposed algorithm.展开更多
In this paper,a novel multi-frame track-before-detect algorithm is proposed,which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise d...In this paper,a novel multi-frame track-before-detect algorithm is proposed,which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase.A criterion of track extrapolation is used to construct state transition set,root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space,then measurement plots of multi-frame are divided into several clusters,and finally multi-frame track-before-detect algorithm is implemented in each cluster.The computational complexity can be reduced by employing the proposed algorithm.Simulation results show that the proposed algorithm can accurately detect multiple targets in close proximity and reduce the number of false tracks.展开更多
As each cluster head(CH)sensor node is used to aggregate,fuse,and forward data from different sensor nodes in an underwater acoustic sensor network(UASN),guaranteeing the data security in a CH is very critical.In this...As each cluster head(CH)sensor node is used to aggregate,fuse,and forward data from different sensor nodes in an underwater acoustic sensor network(UASN),guaranteeing the data security in a CH is very critical.In this paper,a cooperative security monitoring mechanism aided by multiple slave cluster heads(SCHs)is proposed to keep track of the data security of a CH.By designing a low complexity“equilateral triangle algorithm(ETA)”,the optimal SCHs(named as ETA-based multiple SCHs)are selected from the candidate SCHs so as to improve the dispersion and coverage of SCHs and achieve largescale data security monitoring.In addition,by analyzing the entire monitoring process,the close form expression of the probability of the failure attack identification for the SCHs with respect to the probability of attack launched by ordinary nodes is deduced.The simulation results show that the proposed optimal ETA-based multiple SCH cooperation scheme has lower probability of the failure attack identification than that of the existing schemes.In addition,the numerical simulation results are consistent with the theoretical analysis results,thus verifying the effectiveness of the proposed scheme.展开更多
Locating distribution centers optimally is a crucial and systematic task for decision-makers.Optimally located distribution centers can significantly improve the logistics system's efficiency and reduce its operat...Locating distribution centers optimally is a crucial and systematic task for decision-makers.Optimally located distribution centers can significantly improve the logistics system's efficiency and reduce its operational costs.However,it is not an easy task to optimize distribution center locations and previous studies focused primarily on location optimization of a single distribution center.With growing logistics demands,multiple distribution centers become necessary to meet customers' requirements,but few studies have tackled the multiple distribution center locations(MDCLs) problem.This paper presents a comprehensive algorithm to address the MDCLs problem.Fuzzy integration and clustering approach using the improved axiomatic fuzzy set(AFS) theory is developed for location clustering based on multiple hierarchical evaluation criteria.Then,technique for order preference by similarity to ideal solution(TOPSIS) is applied for evaluating and selecting the best candidate for each cluster.Sensitivity analysis is also conducted to assess the influence of each criterion in the location planning decision procedure.Results from a case study in Guiyang,China,reveals that the proposed approach developed in this study outperforms other similar algorithms for MDCLs selection.This new method may easily be extended to address location planning of other types of facilities,including hospitals,fire stations and schools.展开更多
In order to solve cruise missile route planning problem for low-altitude penetration , a hy- brid particle swarm optimization （ HPSO ） algorithm is proposed. Firstly, K-means clustering algo- rithm is applied to div...In order to solve cruise missile route planning problem for low-altitude penetration , a hy- brid particle swarm optimization （ HPSO ） algorithm is proposed. Firstly, K-means clustering algo- rithm is applied to divide the particle swarm into multiple isolated sub-populations, then niche algo- rithm is adopted to make all particles independently search for optimal values in their own sub-popu- lations. Finally simulated annealing （SA） algorithm is introduced to avoid the weakness of PSO algo- rithm, which can easily be trapped into the local optimum in the search process. The optimal value obtained by every sub-population search corresponds to an optimal route, multiple different optimal routes are provided for cruise missile. Simulation results show that the HPSO algorithm has a fast convergence rate, and the planned routes have flat ballisticpaths and short ranges which meet the low-altitude penetration requirements.展开更多
Aimed at a multiple traveling salesman problem(MTSP)with multiple depots and closed paths,this paper proposes a k-means clustering donkey and a smuggler algorithm(KDSA).The algorithm first uses the k-means clustering ...Aimed at a multiple traveling salesman problem(MTSP)with multiple depots and closed paths,this paper proposes a k-means clustering donkey and a smuggler algorithm(KDSA).The algorithm first uses the k-means clustering method to divide all cities into several categories based on the center of various samples;the large-scale MTSP is divided into multiple separate traveling salesman problems(TSPs),and the TSP is solved through the DSA.The proposed algorithm adopts a solution strategy of clustering first and then carrying out,which can not only greatly reduce the search space of the algorithm but also make the search space more fully explored so that the optimal solution of the problem can be more quickly obtained.The experimental results from solving several test cases in the TSPLIB database show that compared with other related intelligent algorithms,the K-DSA has good solving performance and computational efficiency in MTSPs of different scales,especially with large-scale MTSP and when the convergence speed is faster;thus,the advantages of this algorithm are more obvious compared to other algorithms.展开更多
The geometries, electronic structures of pure carbon clusters were investigated in the range of C 3 to C 6 size. To obtain accurate results, the STO double zeta basis sets with a polarization function are selected. Th...The geometries, electronic structures of pure carbon clusters were investigated in the range of C 3 to C 6 size. To obtain accurate results, the STO double zeta basis sets with a polarization function are selected. The main focuses are on the new geometry, electronic structures of carbon clusters, such as the three dimensional structure for C 4, C 5 and so on. The clusters are found to have more isomers corresponding to different geometries and spin multiplicities close to their ground states. We find that the spin multiplicity of the ground state or close to ground state of odd numbered linear chain is 1. But in even numbered linear chains, the spin multiplicity is 3. In odd numbered cyclic structure without branch chain, the spin multiplicity of the ground state or close ground state is 3, and that of even numbered is 1. But the three dimensional structure disagree those principles.展开更多
A novel multiple PUs (Primary Users) localization algorithm was proposed, which estimates the number of PUs by SVD (Singular Value Decomposition) method and seeks non-cooperative PUs' position by executing k-mean ...A novel multiple PUs (Primary Users) localization algorithm was proposed, which estimates the number of PUs by SVD (Singular Value Decomposition) method and seeks non-cooperative PUs' position by executing k-mean clustering and iterative operations. The simulation results show that the proposed method can determined the number of PUs blindly and achieves better performance than traditional expectation-maximization (EM) algorithm.展开更多
The multiple scattering cluster (MSC) method has been employed to perform a theoretical analysis on carbon is near edge X-ray absorption fine structure of the deuteron acetylene (C2 D2) adsorbed on Si(111)7× 7 at...The multiple scattering cluster (MSC) method has been employed to perform a theoretical analysis on carbon is near edge X-ray absorption fine structure of the deuteron acetylene (C2 D2) adsorbed on Si(111)7× 7 at room temperature. From the MSC study. it is confirmed that the (22D2 molecule is bonded to a pair of adjacent Si adatom and Si restatom with C-Si bond length about 0.18nm. The carbon-deuteron bond is bent away front the surface and the CCD bond angle is about 120°. The molecule plane tilt slightly away from the surface normal. Compared with C2D2 in gas phase, the C-C bond and C-D bond are elongated by about 0.03nm and 0.02nm respectively when acetylene was adsorbed on the subtrate. Keyowrds: adsorption of deuteron acetylene on Si(111)7×7. near edge X- ray absorption fine structure. multiple scattering cluster method展开更多
The increase in the number of devices with a massive revolution in mobile technology leads to increase the capacity of the wireless communications net-works. Multi-user Multiple-Input Multiple-Output is an advanced pr...The increase in the number of devices with a massive revolution in mobile technology leads to increase the capacity of the wireless communications net-works. Multi-user Multiple-Input Multiple-Output is an advanced procedure of Multiple-Input Multiple-Output, which improves the performance of Wireless Local Area Networks. Moreover, Multi-user Multiple-Input Multiple-Output leads the Wireless Local Area Networks toward covering more areas. Due to the growth of the number of clients and requirements, researchers try to improve the performance of the Medium Access Control protocol of Multi-user Multiple-Input Multiple-Output technology to serve the user better, by supporting different data sizes, and reducing the waiting time to be able to transmit data quickly. In this paper, we propose a Clustering Multi-user Multiple-Input Multiple-Output protocol, which is an improved Medium Access Control protocol for Multi-user Multiple-Input Multiple-Out-put based on MIMOMate clustering technique and Padovan Backoff Algorithm. Utilizing MIMOMMate focuses on the signal power which only serves the user in that cluster, minimizes the energy consumption and increases the capacity. The implementation of Clustering Multi-user Multiple-Input Multiple-Output performs on the Network Simulator (NS2.34) platform. The results show that Clustering Multi-user Multiple-Input Multiple-Output protocol improves the throughput by 89.8%, and reduces the latency of wireless communication by 43.9% in scenarios with contention. As a result, the overall performances of the network are improved.展开更多
基金supported by the National Natural Science Foundation of China(61401475)
文摘The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.
基金supported in part by the National Natural Science Foundation of China(71361014,61973242,61573265,51665018)the Major Fundamental Research Program of the Natural Science Foundation of Shaanxi Province(2017ZDJC-34)。
文摘Accompanying the unceasing progress of integrated circuit manufacturing technology, the mainstream production mode of current semiconductor wafer fabrication is featured with multi-variety, small batch, and individual customization, which poses a huge challenge to the scheduling of cluster tools with single-wafer-type fabrication. Concurrent processing multiple wafer types in cluster tools, as a novel production pattern, has drawn increasing attention from industry to academia, whereas the corresponding research remains insufficient. This paper investigates the scheduling problems of dual-arm cluster tools with multiple wafer types and residency time constraints. To pursue an easy-to-implement cyclic operation under diverse flow patterns,we develop a novel robot activity strategy called multiplex swap sequence. In the light of the virtual module technology, the workloads that stem from bottleneck process steps and asymmetrical process configuration are balanced satisfactorily. Moreover, several sufficient and necessary conditions with closed-form expressions are obtained for checking the system's schedulability. Finally, efficient algorithms with polynomial complexity are developed to find the periodic scheduling, and its practicability and availability are demonstrated by the offered illustrative examples.
基金funded by National Natural Science Foundation of China under Grant Nos.61972057 and U1836208Hunan Provincial Natural Science Foundation of China under Grant No.2019JJ50655+3 种基金Scientic Research Foundation of Hunan Provincial Education Department of China under Grant No.18B160Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle Infrastructure Systems(Changsha University of Science and Technology)under Grant No.kfj180402the“Double First-class”International Cooperation and Development Scientic Research Project of Changsha University of Science and Technology under Grant No.2018IC25the Researchers Supporting Project No.(RSP-2020/102)King Saud University,Riyadh,Saudi Arabia.
文摘Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled.However,multiple kernel clustering for incomplete data is a critical yet challenging task.Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local optimum.To address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete data.The AMKC method rst clusters the initialized incomplete data.Then,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination coefcients.Finally,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel clustering.The three stages in this process are carried out simultaneously until the convergence condition is met.Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art competitors.Meanwhile,the proposed method gains fast convergence speed.
文摘The current study examined the prevalence and clustering of 5 health-risk behaviors among adolescents in Hawaii, including physical inactivity, low fruit and vegetable consumption, junk food consumption, excessive television time, and inadequate sleep. High school students were recruited from 5 classrooms in Oahu Hawaii. Data were collected in the spring semester of 2011. Proportions were used to describe the prevalence of single and multiple health risk behaviors. Significant health behavior clusters were revealed using an observed-to-expected (O/E) ratio method. Participating adolescents (n = 114) were 11th and 12th grade students with a mean age of 16.28 (SD = 0.62). Participants were predominantly female (75%) and Filipino-American (68%). Seventy-seven percent of adolescents were physically inactive, 90% watched excessive TV, 66% consumed inadequate fruits and vegetables, 94% reported inadequate levels of sleep, and 80% consumed excessive junk food. Overall, 94% reported at least 3 risk factors, 73% reported at least 4 risk factors, and 37% reported all 5 risk factors. No significant clusters were found. Conclusion: Health-risk behaviors cluster and occur more often than expected among adolescents living in Hawaii. Non-significant clustering may be due to insufficient variability within the sample data;future examinations of this highly understudied population are necessary.
文摘[ Objective] This study aimed to investigate the effects of EMS （ethyl methane sulfonate） treatments on multiplication and differentiation of sugarcane embryonic cell clusters. [ Method] Sugarcane variety Xintaitang 22 （ROC22） was used as the experimental material. After treated with different concentrations of EMS for different time, sugarcane embryonic cell clusters were collected for subculture, differentiation and rooting, to compare and analyze the correlation of differ- ent EMS treatments with the muhiplicafion and differentiation of sugarcane embryonic cell clusters. [ Result] Treating ROC22 embryonic cell clusters with 0. 10% -0.15% EMS for4-6 h led to the best results, which reached the level of semi-lethal dose. Sugarcane embryonic cell clusters treated with EMS were adopted for subculture; results indicated that browning rate of cell clusters was higher than that in control （CK） but embryonic structure proportion was lower than that in control ; in addition, multiplication multiple of sugarcane embryonic cell clusters was also lower than that in control. After treated with EMS, sugarcane em- bryonic cell clusters exhibited significantly lower bud differentiation rate and higher browning rate compared with control. Furthermore, treating sugarcane embryonic cell clusters with 0.15% EMS for 2 h was conducive to plantlet emergence and rooting of sugarcane embryonic cell clusters. [ Conclusion] This study provided the- oretical basis for effective mutagenesis of sugarcane using EMS.
基金This work was supported by the National Key R&D Program of China(No.2018YFB1003203)National Natural Science Foundation of China(Nos.61672528,61773392,61772561)+1 种基金Educational Commission of Hu Nan Province,China(No.14B193)the Key Research&Development Plan of Hunan Province(No.2018NK2012).
文摘Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance,while local kernel alignment on different sample actually has different contribution to clustering performance.Therefore this assumption could have a negative effective on clustering performance.To solve this issue,we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment,which can learn a proper weight to clustering performance for each local kernel alignment.Specifically,we introduce a new optimization variable-weight-to denote the contribution of each local kernel alignment to clustering performance,and then,weight,kernel combination coefficients and cluster membership are alternately optimized under kernel alignment frame.In addition,we develop a three-step alternate iterative optimization algorithm to address the resultant optimization problem.Broad experiments on five benchmark data sets have been put into effect to evaluate the clustering performance of the proposed algorithm.The experimental results distinctly demonstrate that the proposed algorithm outperforms the typical multiple kernel clustering algorithms,which illustrates the effectiveness of the proposed algorithm.
基金supported by the Innovation Project of Science and Technology Commission of the Central Military Commission,China(No.19-HXXX-01-ZD-006-XXX-XX)。
文摘In this paper,a novel multi-frame track-before-detect algorithm is proposed,which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase.A criterion of track extrapolation is used to construct state transition set,root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space,then measurement plots of multi-frame are divided into several clusters,and finally multi-frame track-before-detect algorithm is implemented in each cluster.The computational complexity can be reduced by employing the proposed algorithm.Simulation results show that the proposed algorithm can accurately detect multiple targets in close proximity and reduce the number of false tracks.
基金supported in part by the Joint Fund of Science and Technology Department of Liaoning Province and State Key Laboratory of Robotics,China under Grant 2021-KF-22-08in part by the Basic Research Program of Science and Technology of Shenzhen,China under Grant JCYJ20190809161805508in part by the National Natural Science Foundation of China under Grant 62271423 and Grant 41976178.
文摘As each cluster head(CH)sensor node is used to aggregate,fuse,and forward data from different sensor nodes in an underwater acoustic sensor network(UASN),guaranteeing the data security in a CH is very critical.In this paper,a cooperative security monitoring mechanism aided by multiple slave cluster heads(SCHs)is proposed to keep track of the data security of a CH.By designing a low complexity“equilateral triangle algorithm(ETA)”,the optimal SCHs(named as ETA-based multiple SCHs)are selected from the candidate SCHs so as to improve the dispersion and coverage of SCHs and achieve largescale data security monitoring.In addition,by analyzing the entire monitoring process,the close form expression of the probability of the failure attack identification for the SCHs with respect to the probability of attack launched by ordinary nodes is deduced.The simulation results show that the proposed optimal ETA-based multiple SCH cooperation scheme has lower probability of the failure attack identification than that of the existing schemes.In addition,the numerical simulation results are consistent with the theoretical analysis results,thus verifying the effectiveness of the proposed scheme.
基金Project supported by the National Natural Science Foundation of China (Nos. 51028802 and 70902029)the PhD Programs Foundation of Ministry of Education of China (No. 20090092120045)
文摘Locating distribution centers optimally is a crucial and systematic task for decision-makers.Optimally located distribution centers can significantly improve the logistics system's efficiency and reduce its operational costs.However,it is not an easy task to optimize distribution center locations and previous studies focused primarily on location optimization of a single distribution center.With growing logistics demands,multiple distribution centers become necessary to meet customers' requirements,but few studies have tackled the multiple distribution center locations(MDCLs) problem.This paper presents a comprehensive algorithm to address the MDCLs problem.Fuzzy integration and clustering approach using the improved axiomatic fuzzy set(AFS) theory is developed for location clustering based on multiple hierarchical evaluation criteria.Then,technique for order preference by similarity to ideal solution(TOPSIS) is applied for evaluating and selecting the best candidate for each cluster.Sensitivity analysis is also conducted to assess the influence of each criterion in the location planning decision procedure.Results from a case study in Guiyang,China,reveals that the proposed approach developed in this study outperforms other similar algorithms for MDCLs selection.This new method may easily be extended to address location planning of other types of facilities,including hospitals,fire stations and schools.
基金Supported by the National Natural Science Foundation of China(91016004)
文摘In order to solve cruise missile route planning problem for low-altitude penetration , a hy- brid particle swarm optimization （ HPSO ） algorithm is proposed. Firstly, K-means clustering algo- rithm is applied to divide the particle swarm into multiple isolated sub-populations, then niche algo- rithm is adopted to make all particles independently search for optimal values in their own sub-popu- lations. Finally simulated annealing （SA） algorithm is introduced to avoid the weakness of PSO algo- rithm, which can easily be trapped into the local optimum in the search process. The optimal value obtained by every sub-population search corresponds to an optimal route, multiple different optimal routes are provided for cruise missile. Simulation results show that the HPSO algorithm has a fast convergence rate, and the planned routes have flat ballisticpaths and short ranges which meet the low-altitude penetration requirements.
基金the Natural Science Basic Research Program of Shaanxi(2021JQ-368).
文摘Aimed at a multiple traveling salesman problem(MTSP)with multiple depots and closed paths,this paper proposes a k-means clustering donkey and a smuggler algorithm(KDSA).The algorithm first uses the k-means clustering method to divide all cities into several categories based on the center of various samples;the large-scale MTSP is divided into multiple separate traveling salesman problems(TSPs),and the TSP is solved through the DSA.The proposed algorithm adopts a solution strategy of clustering first and then carrying out,which can not only greatly reduce the search space of the algorithm but also make the search space more fully explored so that the optimal solution of the problem can be more quickly obtained.The experimental results from solving several test cases in the TSPLIB database show that compared with other related intelligent algorithms,the K-DSA has good solving performance and computational efficiency in MTSPs of different scales,especially with large-scale MTSP and when the convergence speed is faster;thus,the advantages of this algorithm are more obvious compared to other algorithms.
文摘The geometries, electronic structures of pure carbon clusters were investigated in the range of C 3 to C 6 size. To obtain accurate results, the STO double zeta basis sets with a polarization function are selected. The main focuses are on the new geometry, electronic structures of carbon clusters, such as the three dimensional structure for C 4, C 5 and so on. The clusters are found to have more isomers corresponding to different geometries and spin multiplicities close to their ground states. We find that the spin multiplicity of the ground state or close to ground state of odd numbered linear chain is 1. But in even numbered linear chains, the spin multiplicity is 3. In odd numbered cyclic structure without branch chain, the spin multiplicity of the ground state or close ground state is 3, and that of even numbered is 1. But the three dimensional structure disagree those principles.
基金Sponsored by the Scientific Research Program of Beijing Municipal Commission of Education ( Grant No. KZ2010100009009)Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality ( Grant No. PHR201008186) Scientific Research Fund of Heilongjiang Provincial Education Department ( Grant No. 11541083)
文摘A novel multiple PUs (Primary Users) localization algorithm was proposed, which estimates the number of PUs by SVD (Singular Value Decomposition) method and seeks non-cooperative PUs' position by executing k-mean clustering and iterative operations. The simulation results show that the proposed method can determined the number of PUs blindly and achieves better performance than traditional expectation-maximization (EM) algorithm.
基金The authors acknowledge the financial support of the National Natural Science Foun-dation of China (Grant No.19974036)
文摘The multiple scattering cluster (MSC) method has been employed to perform a theoretical analysis on carbon is near edge X-ray absorption fine structure of the deuteron acetylene (C2 D2) adsorbed on Si(111)7× 7 at room temperature. From the MSC study. it is confirmed that the (22D2 molecule is bonded to a pair of adjacent Si adatom and Si restatom with C-Si bond length about 0.18nm. The carbon-deuteron bond is bent away front the surface and the CCD bond angle is about 120°. The molecule plane tilt slightly away from the surface normal. Compared with C2D2 in gas phase, the C-C bond and C-D bond are elongated by about 0.03nm and 0.02nm respectively when acetylene was adsorbed on the subtrate. Keyowrds: adsorption of deuteron acetylene on Si(111)7×7. near edge X- ray absorption fine structure. multiple scattering cluster method
文摘The increase in the number of devices with a massive revolution in mobile technology leads to increase the capacity of the wireless communications net-works. Multi-user Multiple-Input Multiple-Output is an advanced procedure of Multiple-Input Multiple-Output, which improves the performance of Wireless Local Area Networks. Moreover, Multi-user Multiple-Input Multiple-Output leads the Wireless Local Area Networks toward covering more areas. Due to the growth of the number of clients and requirements, researchers try to improve the performance of the Medium Access Control protocol of Multi-user Multiple-Input Multiple-Output technology to serve the user better, by supporting different data sizes, and reducing the waiting time to be able to transmit data quickly. In this paper, we propose a Clustering Multi-user Multiple-Input Multiple-Output protocol, which is an improved Medium Access Control protocol for Multi-user Multiple-Input Multiple-Out-put based on MIMOMate clustering technique and Padovan Backoff Algorithm. Utilizing MIMOMMate focuses on the signal power which only serves the user in that cluster, minimizes the energy consumption and increases the capacity. The implementation of Clustering Multi-user Multiple-Input Multiple-Output performs on the Network Simulator (NS2.34) platform. The results show that Clustering Multi-user Multiple-Input Multiple-Output protocol improves the throughput by 89.8%, and reduces the latency of wireless communication by 43.9% in scenarios with contention. As a result, the overall performances of the network are improved.
