Unmanned aerial vehicles(UAVs)have become one of the key technologies to achieve future data collection due to their high mobility,rapid deployment,low cost,and the ability to establish line-of-sight communication lin...Unmanned aerial vehicles(UAVs)have become one of the key technologies to achieve future data collection due to their high mobility,rapid deployment,low cost,and the ability to establish line-of-sight communication links.However,when UAV swarm perform tasks in narrow spaces,they often encounter various spatial obstacles,building shielding materials,and high-speed node movements,which result in intermittent network communication links and cannot support the smooth comple-tion of tasks.In this paper,a high mobility and dynamic topol-ogy of the UAV swarm is particularly considered and the high dynamic mobile topology-based clustering(HDMTC)algorithm is proposed.Simulation and real flight verification results verify that the proposed HDMTC algorithm achieves higher stability of net-work,longer link expiration time(LET),and longer node lifetime,all of which improve the communication performance for UAV swarm networks.展开更多
To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location al...To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.展开更多
Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition me...Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.展开更多
The observation error model of the underwater acous-tic positioning system is an important factor to influence the positioning accuracy of the underwater target.For the position inconsistency error caused by consideri...The observation error model of the underwater acous-tic positioning system is an important factor to influence the positioning accuracy of the underwater target.For the position inconsistency error caused by considering the underwater tar-get as a mass point,as well as the observation system error,the traditional error model best estimation trajectory(EMBET)with little observed data and too many parameters can lead to the ill-condition of the parameter model.In this paper,a multi-station fusion system error model based on the optimal polynomial con-straint is constructed,and the corresponding observation sys-tem error identification based on improved spectral clustering is designed.Firstly,the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization.Then a multi-sta-tion non-oriented graph network is established,which can address the problem of the inaccurate identification for the sys-tem errors.Moreover,the similarity matrix of the spectral cluster-ing is improved,and the iterative identification for the system errors based on the improved spectral clustering is proposed.Finally,the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accu-rately identify the system errors,and moreover can improve the positioning accuracy for the underwater target positioning.展开更多
Hierarchical clustering analysis based on statistic s is one of the most important mining algorithms, but the traditionary hierarchica l clustering method is based on global comparing, which only takes in Q clusteri n...Hierarchical clustering analysis based on statistic s is one of the most important mining algorithms, but the traditionary hierarchica l clustering method is based on global comparing, which only takes in Q clusteri ng while ignoring R clustering in practice, so it has some limitation especially when the number of sample and index is very large. Furthermore, because of igno ring the association between the different indexes, the clustering result is not good & true. In this paper, we present the model and the algorithm of two-level hierarchi cal clustering which integrates Q clustering with R clustering. Moreover, becaus e two-level hierarchical clustering is based on the respective clustering resul t of each class, the classification of the indexes directly effects on the a ccuracy of the final clustering result, how to appropriately classify the inde xes is the chief and difficult problem we must handle in advance. Although some literatures also have referred to the issue of the classificati on of the indexes, but the articles classify the indexes only according to their superficial signification, which is unscientific. The reasons are as follow s: First, the superficial signification of some indexes usually takes on different meanings and it is easy to be misapprehended by different person. Furthermore, t his classification method seldom make use of history data, the classification re sult is not so objective. Second, for some indexes, its superficial signification didn’t show any mean ings, so simply from the superficial signification, we can’t classify them to c ertain classes. Third, this classification method need the users have higher level knowledge of this field, otherwise it is difficult for the users to understand the signifi cation of some indexes, which sometimes is not available. So in this paper, to this question, we first use R clustering method to cluste ring indexes, dividing p dimension indexes into q classes, then adopt two-level clustering method to get the final result. Obviously, the classification result is more objective and accurate. Moreover, after the first step, we can get the relation of the different indexes and their interaction. We can also know under a certain class indexes, which samples can be clustering to a class. (These semi finished results sometimes are very useful.) The experiments also indicates the effective and accurate of the algorithms. And, the result of R clustering ca n be easily used for the later practice.展开更多
Failure mode and effect analysis(FMEA)is a preven-tative risk evaluation method used to evaluate and eliminate fail-ure modes within a system.However,the traditional FMEA method exhibits many deficiencies that pose ch...Failure mode and effect analysis(FMEA)is a preven-tative risk evaluation method used to evaluate and eliminate fail-ure modes within a system.However,the traditional FMEA method exhibits many deficiencies that pose challenges in prac-tical applications.To improve the conventional FMEA,many modified FMEA models have been suggested.However,the majority of them inadequately address consensus issues and focus on achieving a complete ranking of failure modes.In this research,we propose a new FMEA approach that integrates a two-stage consensus reaching model and a density peak clus-tering algorithm for the assessment and clustering of failure modes.Firstly,we employ the interval 2-tuple linguistic vari-ables(I2TLVs)to express the uncertain risk evaluations provided by FMEA experts.Then,a two-stage consensus reaching model is adopted to enable FMEA experts to reach a consensus.Next,failure modes are categorized into several risk clusters using a density peak clustering algorithm.Finally,the proposed FMEA is illustrated by a case study of load-bearing guidance devices of subway systems.The results show that the proposed FMEA model can more easily to describe the uncertain risk information of failure modes by using the I2TLVs;the introduction of an endogenous feedback mechanism and an exogenous feedback mechanism can accelerate the process of consensus reaching;and the density peak clustering of failure modes successfully improves the practical applicability of FMEA.展开更多
An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the...An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the[B_(5)O_(10)]^(5-)and[B_(3)O_(7)]^(5-)clusters are alternately connected to form 1D[B_(8)O_(15)]_(n)^(6n-)chains,which are further linked by AlO_(4)units to form a 2D monolayer with 7‑membered ring and 10‑membered ring windows.Two adjacent monolayers with opposite orientations further form a porous‑layered structure with six channels through B—O—Al bonds.Compound 1 was characterized by single crystal X‑ray diffraction,powder X‑ray diffraction(PXRD),IR spectroscopy,UV‑Vis diffuse reflection spectroscopy,and thermogravimetric analysis(TGA),respectively.UV‑Vis diffuse reflectance analysis indicates that compound 1 shows a wide transparency range with a short cutoff edge of 201 nm,suggesting it may have potential application in UV regions.CCDC:2383923.展开更多
Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as...Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as the strongly enhanced synergistic interactions between Pt and Co atoms,the obtained Pt-on-Co/C400 catalysts exhib-ited excellent catalytic activity toward the hydrolysis of ammonia borane with an extremely high turnover frequency(TOF)value of 3022 min^(-1)at 303 K.Durability test indicated that the obtained Pt-on-Co/C400 catalysts possessed high catalytic stability,and there were no changes in the catalyst structures and catalytic activities after 10 cycles.展开更多
When performing tasks,unmanned clusters often face a variety of strategy choices.One of the key issues in unmanned cluster tasks is the method through which to design autonomous collaboration and cooperative evolution...When performing tasks,unmanned clusters often face a variety of strategy choices.One of the key issues in unmanned cluster tasks is the method through which to design autonomous collaboration and cooperative evolution mechanisms that allow for unmanned clusters to maximize their overall task effective-ness under the condition of strategic diversity.This paper ana-lyzes these task requirements from three perspectives:the diver-sity of the decision space,information network construction,and the autonomous collaboration mechanism.Then,this paper pro-poses a method for solving the problem of strategy selection diversity under two network structures.Next,this paper presents a Moran-rule-based evolution dynamics model for unmanned cluster strategies and a vision-driven-mechanism-based evolu-tion dynamics model for unmanned cluster strategy in the con-text of strategy selection diversity according to various unmanned cluster application scenarios.Finally,this paper pro-vides a simulation analysis of the effects of relevant parameters such as the payoff factor and cluster size on cooperative evolu-tion in autonomous cluster collaboration for the two types of models.On this basis,this paper presents advice for effectively addressing diverse choices in unmanned cluster tasks,thereby providing decision support for practical applications of unmanned cluster tasks.展开更多
Aiming at the complexity of seismic gestation mechanism and spatial distribution, we hypothesize that the seismic data are composed of background earthquakes and anomaly earthquakes in a certain temporal-spatial scope...Aiming at the complexity of seismic gestation mechanism and spatial distribution, we hypothesize that the seismic data are composed of background earthquakes and anomaly earthquakes in a certain temporal-spatial scope. Also the background earthquakes and anomaly earthquakes both satisfy the 2-D Poisson process of different parameters respectively. In the paper, the concept of N-th order distance is introduced in order to transform 2-D superimposed Poisson process into 1-D mixture density function. On the basis of choosing the distance, mixture density function is decomposed to recognize the anomaly earthquakes through genetic algorithm. Combined with the temporal scanning of C value, the algorithm is applied to the recognition on spatial pattern of foreshock anomalies by exam-ples of Songpan and Longling sequences in the southwest of China.展开更多
The clustering evaluation can be used to scientifically classify the objects to be evaluated according to the information aggregation of various evaluation rules. In grey weighted clustering evaluation, the index clus...The clustering evaluation can be used to scientifically classify the objects to be evaluated according to the information aggregation of various evaluation rules. In grey weighted clustering evaluation, the index clustering rule relies on the construction of the whitenization weight function, while the existing construction method of the linear function lacks the construction mechanism analysis and validity explanation. A normative construction principle is put forward by analyzing the construction mechanism of the function. Through proving the normative principle of the function,the basic modal function(BMF) is proposed and characterized by different function forms. Then, a new type of the whitenization weight function and its grey clustering evaluation model algorithm are given by studying the mechanism and nature of the construction of different forms of the function. Finally, the comparative study for self-innovation capability of defense science and technology industry(DSTI) is taken as an example. The results show that the different construction ways of the function have an effect on the clustering result. The proposed construction mechanism can better explain the index clustering rules and evaluation effectiveness,which will perfect the theoretical system of grey clustering evaluation and be applied to practice effectively.展开更多
This paper proposes a suppression method of the deceptive false target(FT) produced by digital radio frequency memory(DRFM) in a multistatic radar system. The simulated deceptive false targets from DRFM cannot be easi...This paper proposes a suppression method of the deceptive false target(FT) produced by digital radio frequency memory(DRFM) in a multistatic radar system. The simulated deceptive false targets from DRFM cannot be easily discriminated and suppressed with traditional radar systems. Therefore, multistatic radar has attracted considerable interest as it provides improved performance against deception jamming due to several separated receivers. This paper first investigates the received signal model in the presence of multiple false targets in all receivers of the multistatic radar. Then, obtain the propagation time delays of the false targets based on the cross-correlation test of the received signals in different receivers. In doing so, local-density-based spatial clustering of applications with noise(LDBSCAN) is proposed to discriminate the FTs from the physical targets(PTs) after compensating the FTs time delays, where the FTs are approximately coincident with one position, while PTs possess small dispersion.Numerical simulations are carried out to demonstrate the feasibility and validness of the proposed method.展开更多
Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-me...Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-means method the seeds are modified,and for each IFS a membership degree to each of the clusters is estimated.In the end of the algorithm,all the given IFSs are clustered according to the estimated membership degrees.Furthermore,the algorithm is extended for clustering interval-valued intuitionistic fuzzy sets(IVIFSs).Finally,the developed algorithms are illustrated through conducting experiments on both the real-world and simulated data sets.展开更多
The existing trajectory clustering （TRACLUS） is sensitive to the input parameters c and MinLns. The parameter value is changed a little, but cluster results are entirely different. Aiming at this vulnerability, a sh...The existing trajectory clustering （TRACLUS） is sensitive to the input parameters c and MinLns. The parameter value is changed a little, but cluster results are entirely different. Aiming at this vulnerability, a shielding parameters sensitivity trajectory cluster （SPSTC） algorithm is proposed which is insensitive to the input parameters. Firstly, some definitions about the core distance and reachable distance of line segment are presented, and then the algorithm generates cluster sorting according to the core dis- tance and reachable distance. Secondly, the reachable plots of line segment sets are constructed according to the cluster sorting and reachable distance. Thirdly, a parameterized sequence is extracted according to the reachable plot, and then the final trajectory cluster based on the parameterized sequence is acquired. The parameterized sequence represents the inner cluster structure of trajectory data. Experiments on real data sets and test data sets show that the SPSTC algorithm effectively reduces the sensitivity to the input parameters, meanwhile it can obtain the better quality of the trajectory cluster.展开更多
基金supported by the National Key Research and Development Program of China(2024YFB4504500)Shanghai Collaborative Innovation Project(24xtcx00500).
文摘Unmanned aerial vehicles(UAVs)have become one of the key technologies to achieve future data collection due to their high mobility,rapid deployment,low cost,and the ability to establish line-of-sight communication links.However,when UAV swarm perform tasks in narrow spaces,they often encounter various spatial obstacles,building shielding materials,and high-speed node movements,which result in intermittent network communication links and cannot support the smooth comple-tion of tasks.In this paper,a high mobility and dynamic topol-ogy of the UAV swarm is particularly considered and the high dynamic mobile topology-based clustering(HDMTC)algorithm is proposed.Simulation and real flight verification results verify that the proposed HDMTC algorithm achieves higher stability of net-work,longer link expiration time(LET),and longer node lifetime,all of which improve the communication performance for UAV swarm networks.
基金National Natural Science Foundation of China(62202477)。
文摘To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.
基金supported by the National Natural Science Foundation of China (Project No.72301293)。
文摘Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.
基金This work was supported by the National Natural Science Foundation of China(61903086,61903366,62001115)the Natural Science Foundation of Hunan Province(2019JJ50745,2020JJ4280,2021JJ40133)the Fundamentals and Basic of Applications Research Foundation of Guangdong Province(2019A1515110136).
文摘The observation error model of the underwater acous-tic positioning system is an important factor to influence the positioning accuracy of the underwater target.For the position inconsistency error caused by considering the underwater tar-get as a mass point,as well as the observation system error,the traditional error model best estimation trajectory(EMBET)with little observed data and too many parameters can lead to the ill-condition of the parameter model.In this paper,a multi-station fusion system error model based on the optimal polynomial con-straint is constructed,and the corresponding observation sys-tem error identification based on improved spectral clustering is designed.Firstly,the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization.Then a multi-sta-tion non-oriented graph network is established,which can address the problem of the inaccurate identification for the sys-tem errors.Moreover,the similarity matrix of the spectral cluster-ing is improved,and the iterative identification for the system errors based on the improved spectral clustering is proposed.Finally,the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accu-rately identify the system errors,and moreover can improve the positioning accuracy for the underwater target positioning.
文摘Hierarchical clustering analysis based on statistic s is one of the most important mining algorithms, but the traditionary hierarchica l clustering method is based on global comparing, which only takes in Q clusteri ng while ignoring R clustering in practice, so it has some limitation especially when the number of sample and index is very large. Furthermore, because of igno ring the association between the different indexes, the clustering result is not good & true. In this paper, we present the model and the algorithm of two-level hierarchi cal clustering which integrates Q clustering with R clustering. Moreover, becaus e two-level hierarchical clustering is based on the respective clustering resul t of each class, the classification of the indexes directly effects on the a ccuracy of the final clustering result, how to appropriately classify the inde xes is the chief and difficult problem we must handle in advance. Although some literatures also have referred to the issue of the classificati on of the indexes, but the articles classify the indexes only according to their superficial signification, which is unscientific. The reasons are as follow s: First, the superficial signification of some indexes usually takes on different meanings and it is easy to be misapprehended by different person. Furthermore, t his classification method seldom make use of history data, the classification re sult is not so objective. Second, for some indexes, its superficial signification didn’t show any mean ings, so simply from the superficial signification, we can’t classify them to c ertain classes. Third, this classification method need the users have higher level knowledge of this field, otherwise it is difficult for the users to understand the signifi cation of some indexes, which sometimes is not available. So in this paper, to this question, we first use R clustering method to cluste ring indexes, dividing p dimension indexes into q classes, then adopt two-level clustering method to get the final result. Obviously, the classification result is more objective and accurate. Moreover, after the first step, we can get the relation of the different indexes and their interaction. We can also know under a certain class indexes, which samples can be clustering to a class. (These semi finished results sometimes are very useful.) The experiments also indicates the effective and accurate of the algorithms. And, the result of R clustering ca n be easily used for the later practice.
基金supported by the Fundamental Research Funds for the Central Universities(22120240094)Humanities and Social Science Fund of Ministry of Education China(22YJA630082).
文摘Failure mode and effect analysis(FMEA)is a preven-tative risk evaluation method used to evaluate and eliminate fail-ure modes within a system.However,the traditional FMEA method exhibits many deficiencies that pose challenges in prac-tical applications.To improve the conventional FMEA,many modified FMEA models have been suggested.However,the majority of them inadequately address consensus issues and focus on achieving a complete ranking of failure modes.In this research,we propose a new FMEA approach that integrates a two-stage consensus reaching model and a density peak clus-tering algorithm for the assessment and clustering of failure modes.Firstly,we employ the interval 2-tuple linguistic vari-ables(I2TLVs)to express the uncertain risk evaluations provided by FMEA experts.Then,a two-stage consensus reaching model is adopted to enable FMEA experts to reach a consensus.Next,failure modes are categorized into several risk clusters using a density peak clustering algorithm.Finally,the proposed FMEA is illustrated by a case study of load-bearing guidance devices of subway systems.The results show that the proposed FMEA model can more easily to describe the uncertain risk information of failure modes by using the I2TLVs;the introduction of an endogenous feedback mechanism and an exogenous feedback mechanism can accelerate the process of consensus reaching;and the density peak clustering of failure modes successfully improves the practical applicability of FMEA.
文摘An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the[B_(5)O_(10)]^(5-)and[B_(3)O_(7)]^(5-)clusters are alternately connected to form 1D[B_(8)O_(15)]_(n)^(6n-)chains,which are further linked by AlO_(4)units to form a 2D monolayer with 7‑membered ring and 10‑membered ring windows.Two adjacent monolayers with opposite orientations further form a porous‑layered structure with six channels through B—O—Al bonds.Compound 1 was characterized by single crystal X‑ray diffraction,powder X‑ray diffraction(PXRD),IR spectroscopy,UV‑Vis diffuse reflection spectroscopy,and thermogravimetric analysis(TGA),respectively.UV‑Vis diffuse reflectance analysis indicates that compound 1 shows a wide transparency range with a short cutoff edge of 201 nm,suggesting it may have potential application in UV regions.CCDC:2383923.
文摘Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as the strongly enhanced synergistic interactions between Pt and Co atoms,the obtained Pt-on-Co/C400 catalysts exhib-ited excellent catalytic activity toward the hydrolysis of ammonia borane with an extremely high turnover frequency(TOF)value of 3022 min^(-1)at 303 K.Durability test indicated that the obtained Pt-on-Co/C400 catalysts possessed high catalytic stability,and there were no changes in the catalyst structures and catalytic activities after 10 cycles.
基金supported by the National Natural Science Foundation of China(72471240).
文摘When performing tasks,unmanned clusters often face a variety of strategy choices.One of the key issues in unmanned cluster tasks is the method through which to design autonomous collaboration and cooperative evolution mechanisms that allow for unmanned clusters to maximize their overall task effective-ness under the condition of strategic diversity.This paper ana-lyzes these task requirements from three perspectives:the diver-sity of the decision space,information network construction,and the autonomous collaboration mechanism.Then,this paper pro-poses a method for solving the problem of strategy selection diversity under two network structures.Next,this paper presents a Moran-rule-based evolution dynamics model for unmanned cluster strategies and a vision-driven-mechanism-based evolu-tion dynamics model for unmanned cluster strategy in the con-text of strategy selection diversity according to various unmanned cluster application scenarios.Finally,this paper pro-vides a simulation analysis of the effects of relevant parameters such as the payoff factor and cluster size on cooperative evolu-tion in autonomous cluster collaboration for the two types of models.On this basis,this paper presents advice for effectively addressing diverse choices in unmanned cluster tasks,thereby providing decision support for practical applications of unmanned cluster tasks.
基金Supported by National Natural Science Foundation of China （60874063）, and Innovation and Scientific Research Foundation of Graduate Student of Heilongjiang Province （YJSCX2012-263HLJ）
基金National Science Fund for Distinguished Young Scholars (40225004), The CAS Hundred Scholars Program.
文摘Aiming at the complexity of seismic gestation mechanism and spatial distribution, we hypothesize that the seismic data are composed of background earthquakes and anomaly earthquakes in a certain temporal-spatial scope. Also the background earthquakes and anomaly earthquakes both satisfy the 2-D Poisson process of different parameters respectively. In the paper, the concept of N-th order distance is introduced in order to transform 2-D superimposed Poisson process into 1-D mixture density function. On the basis of choosing the distance, mixture density function is decomposed to recognize the anomaly earthquakes through genetic algorithm. Combined with the temporal scanning of C value, the algorithm is applied to the recognition on spatial pattern of foreshock anomalies by exam-ples of Songpan and Longling sequences in the southwest of China.
基金supported by the National Natural Science Foundation of China(71671090)the Aeronautical Science Foundation of China(2016ZG52068)+1 种基金the Liberal Arts and Social Sciences Foundation of the Ministry of Education(MOE)in China(15YJCZH189)the Qinglan Project for Excellent Youth or Middle-aged Academic Leaders in Jiangsu Province
文摘The clustering evaluation can be used to scientifically classify the objects to be evaluated according to the information aggregation of various evaluation rules. In grey weighted clustering evaluation, the index clustering rule relies on the construction of the whitenization weight function, while the existing construction method of the linear function lacks the construction mechanism analysis and validity explanation. A normative construction principle is put forward by analyzing the construction mechanism of the function. Through proving the normative principle of the function,the basic modal function(BMF) is proposed and characterized by different function forms. Then, a new type of the whitenization weight function and its grey clustering evaluation model algorithm are given by studying the mechanism and nature of the construction of different forms of the function. Finally, the comparative study for self-innovation capability of defense science and technology industry(DSTI) is taken as an example. The results show that the different construction ways of the function have an effect on the clustering result. The proposed construction mechanism can better explain the index clustering rules and evaluation effectiveness,which will perfect the theoretical system of grey clustering evaluation and be applied to practice effectively.
文摘This paper proposes a suppression method of the deceptive false target(FT) produced by digital radio frequency memory(DRFM) in a multistatic radar system. The simulated deceptive false targets from DRFM cannot be easily discriminated and suppressed with traditional radar systems. Therefore, multistatic radar has attracted considerable interest as it provides improved performance against deception jamming due to several separated receivers. This paper first investigates the received signal model in the presence of multiple false targets in all receivers of the multistatic radar. Then, obtain the propagation time delays of the false targets based on the cross-correlation test of the received signals in different receivers. In doing so, local-density-based spatial clustering of applications with noise(LDBSCAN) is proposed to discriminate the FTs from the physical targets(PTs) after compensating the FTs time delays, where the FTs are approximately coincident with one position, while PTs possess small dispersion.Numerical simulations are carried out to demonstrate the feasibility and validness of the proposed method.
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(70625005)
文摘Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-means method the seeds are modified,and for each IFS a membership degree to each of the clusters is estimated.In the end of the algorithm,all the given IFSs are clustered according to the estimated membership degrees.Furthermore,the algorithm is extended for clustering interval-valued intuitionistic fuzzy sets(IVIFSs).Finally,the developed algorithms are illustrated through conducting experiments on both the real-world and simulated data sets.
基金supported by the National High Technology Research and Development Program of China(863 Program)(2007AA01Z404)the Funding of Jiangsu Provincial Innovation Program for Graduate Education(CXLX110206)
文摘The existing trajectory clustering （TRACLUS） is sensitive to the input parameters c and MinLns. The parameter value is changed a little, but cluster results are entirely different. Aiming at this vulnerability, a shielding parameters sensitivity trajectory cluster （SPSTC） algorithm is proposed which is insensitive to the input parameters. Firstly, some definitions about the core distance and reachable distance of line segment are presented, and then the algorithm generates cluster sorting according to the core dis- tance and reachable distance. Secondly, the reachable plots of line segment sets are constructed according to the cluster sorting and reachable distance. Thirdly, a parameterized sequence is extracted according to the reachable plot, and then the final trajectory cluster based on the parameterized sequence is acquired. The parameterized sequence represents the inner cluster structure of trajectory data. Experiments on real data sets and test data sets show that the SPSTC algorithm effectively reduces the sensitivity to the input parameters, meanwhile it can obtain the better quality of the trajectory cluster.
