To address the issues of unknown target size,blurred edges,background interference and low contrast in infrared small target detection,this paper proposes a method based on density peaks searching and weighted multi-f...To address the issues of unknown target size,blurred edges,background interference and low contrast in infrared small target detection,this paper proposes a method based on density peaks searching and weighted multi-feature local difference.Firstly,an improved high-boost filter is used for preprocessing to eliminate background clutter and high-brightness interference,thereby increasing the probability of capturing real targets in the density peak search.Secondly,a triple-layer window is used to extract features from the area surrounding candidate targets,addressing the uncertainty of small target sizes.By calculating multi-feature local differences between the triple-layer windows,the problems of blurred target edges and low contrast are resolved.To balance the contribution of different features,intra-class distance is used to calculate weights,achieving weighted fusion of multi-feature local differences to obtain the weighted multi-feature local differences of candidate targets.The real targets are then extracted using the interquartile range.Experiments on datasets such as SIRST and IRSTD-IK show that the proposed method is suitable for various complex types and demonstrates good robustness and detection performance.展开更多
Cluster-basedmodels have numerous application scenarios in vehicular ad-hoc networks(VANETs)and can greatly help improve the communication performance of VANETs.However,the frequent movement of vehicles can often lead...Cluster-basedmodels have numerous application scenarios in vehicular ad-hoc networks(VANETs)and can greatly help improve the communication performance of VANETs.However,the frequent movement of vehicles can often lead to changes in the network topology,thereby reducing cluster stability in urban scenarios.To address this issue,we propose a clustering model based on the density peak clustering(DPC)method and sparrow search algorithm(SSA),named SDPC.First,the model constructs a fitness function based on the parameters obtained from the DPC method and deploys the SSA for iterative optimization to select cluster heads(CHs).Then,the vehicles that have not been selected as CHs are assigned to appropriate clusters by comprehensively considering the distance parameter and link-reliability parameter.Finally,cluster maintenance strategies are considered to tackle the changes in the clusters’organizational structure.To verify the performance of the model,we conducted a simulation on a real-world scenario for multiple metrics related to clusters’stability.The results show that compared with the APROVE and the GAPC,SDPC showed clear performance advantages,indicating that SDPC can effectively ensure VANETs’cluster stability in urban scenarios.展开更多
Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a...Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.展开更多
We report a simultaneous observation of two band electromagnetic ion cyclotron(EMIC)waves and toroidal Alfvén waves by the Van Allen Probe mission.Through wave frequency analyses,the mass densityρis found to be ...We report a simultaneous observation of two band electromagnetic ion cyclotron(EMIC)waves and toroidal Alfvén waves by the Van Allen Probe mission.Through wave frequency analyses,the mass densityρis found to be locally peaked at the magnetic equator.Perpendicular fluxes of ions(<100 eV)increase simultaneously with the appearances of EMIC waves,indicating a heating of these ions by EMIC waves.In addition,the measured ion distributions also support the equatorial peak formation,which accords with the result of the frequency analyses.The formation of local mass density peaks at the equator should be due to enhancements of equatorial ion concentrations,which are triggered by EMIC waves’perpendicular heating on low energy ions.展开更多
Performing cluster analysis on molecular conformation is an important way to find the representative conformation in the molecular dynamics trajectories.Usually,it is a critical step for interpreting complex conformat...Performing cluster analysis on molecular conformation is an important way to find the representative conformation in the molecular dynamics trajectories.Usually,it is a critical step for interpreting complex conformational changes or interaction mechanisms.As one of the density-based clustering algorithms,find density peaks(FDP)is an accurate and reasonable candidate for the molecular conformation clustering.However,facing the rapidly increasing simulation length due to the increase in computing power,the low computing efficiency of FDP limits its application potential.Here we propose a marginal extension to FDP named K-means find density peaks(KFDP)to solve the mass source consuming problem.In KFDP,the points are initially clustered by a high efficiency clustering algorithm,such as K-means.Cluster centers are defined as typical points with a weight which represents the cluster size.Then,the weighted typical points are clustered again by FDP,and then are refined as core,boundary,and redefined halo points.In this way,KFDP has comparable accuracy as FDP but its computational complexity is reduced from O(n^(2))to O(n).We apply and test our KFDP method to the trajectory data of multiple small proteins in terms of torsion angle,secondary structure or contact map.The comparing results with K-means and density-based spatial clustering of applications with noise show the validation of the proposed KFDP.展开更多
We present a novel unsupervised integrated score framework to generate generic extractive multi- document summaries by ranking sentences based on dynamic programming （DP） strategy. Considering that cluster-based met...We present a novel unsupervised integrated score framework to generate generic extractive multi- document summaries by ranking sentences based on dynamic programming （DP） strategy. Considering that cluster-based methods proposed by other researchers tend to ignore informativeness of words when they generate summaries, our proposed framework takes relevance, diversity, informativeness and length constraint of sentences into consideration comprehensively. We apply Density Peaks Clustering （DPC） to get relevance scores and diversity scores of sentences simultaneously. Our framework produces the best performance on DUC2004, 0.396 of ROUGE-1 score, 0.094 of ROUGE-2 score and 0.143 of ROUGE-SU4 which outperforms a series of popular baselines, such as DUC Best, FGB [7], and BSTM [10].展开更多
The impact of the E×B flow shear stabilization on particle transport and density peaking at JET is analyzed in the framework of integrated modelling with the CRONOS code.For that purpose,plasmas from a power scan...The impact of the E×B flow shear stabilization on particle transport and density peaking at JET is analyzed in the framework of integrated modelling with the CRONOS code.For that purpose,plasmas from a power scan which show a significant increasing of density peaking with the injected neutral beam injection power have been used as a modeling basis.By means of simulations with the quasilinear model GLF23 for the heat and particle transport,a strong link between the particle confinement and E×B flow shear stabilization is found.This is particularly important close to the pedestal region where the particle pinch direction becomes strongly inward for high E×B flow shear values.Such impact introduces some non-negligible deviation from the well-known collisonality dependence of the density peaking,whose general trend has been also obtained in the framework of this modelling by performing pedestal density scans.展开更多
There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of netwo...There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem.Meanwhile, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.展开更多
The density peak （DP） algorithm has been widely used in scientific research due to its novel and effective peak density-based clustering approach. However, the DP algorithm uses each pair of data points several time...The density peak （DP） algorithm has been widely used in scientific research due to its novel and effective peak density-based clustering approach. However, the DP algorithm uses each pair of data points several times when determining cluster centers, yielding high computational complexity. In this paper, we focus on accelerating the time-consuming density peaks algorithm with a graphics processing unit （GPU）. We analyze the principle of the algorithm to locate its computational bottlenecks, and evaluate its potential for parallelism. In light of our analysis, we propose an efficient parallel DP algorithm targeting on a GPU architecture and implement this parallel method with compute unified device architecture （CUDA）, called the ‘CUDA-DP platform＇. Specifically, we use shared memory to improve data locality, which reduces the amount of global memory access. To exploit the coalescing accessing mechanism of CPU, we convert the data structure of the CUDA-DP program from array of structures to structure of arrays. In addition, we introduce a binary search-and-sampling method to avoid sorting a large array. The results of the experiment show that CUDA-DP can achieve a 45-fold acceleration when compared to the central processing unit based density peaks implementation.展开更多
AlAs/GaAs/In0.1Ga0.9As/GaAs/AlAs double-barrier resonant tunneling diodes （DBRTDs） grown on a semi-insulated GaAs substrate with molecular beam epitaxy is demonstrated. By sandwiching the In0.1 Ga0.9 As layer betwee...AlAs/GaAs/In0.1Ga0.9As/GaAs/AlAs double-barrier resonant tunneling diodes （DBRTDs） grown on a semi-insulated GaAs substrate with molecular beam epitaxy is demonstrated. By sandwiching the In0.1 Ga0.9 As layer between GaAs layers, potential wells beside the two sides of barrier are deepened, resulting in an increase of the peak-to-valley current ratio （PVCR） and a peak current density. A special shape of collector is designed in order to reduce contact resistance and non-uniformity of the current;as a result the total chrrent density in the device is increased. The use of thin barriers is also helpful for the improvement of the PVCR and the peak current density in DBRTDs. The devices exhibit a maximum PVCR of 13.98 and a peak current density of 89kA/cm^2 at room temperature.展开更多
The principal resonance of Duffing random external excitation was investigated. oscillator to combined deterministic and The random excitation was taken to be white noise or harmonic with separable random amplitude an...The principal resonance of Duffing random external excitation was investigated. oscillator to combined deterministic and The random excitation was taken to be white noise or harmonic with separable random amplitude and phase. The method of multiple scales was used to determine the equations of modulation of amplitude and phase. The one peak probability density function of each of the two stable stationary solutions was calculated by the linearization method. These two one-peak-density functions were combined using the probability of realization of the two stable stationary solutions to obtain the double peak probability density function. The theoretical analysis are verified by numerical results.展开更多
Estimation of peak power density in the vicinity of cellular base stations and comparison of theoretical values with exposure limits for public,offers the possibility of knowing the safety distance from those antennas...Estimation of peak power density in the vicinity of cellular base stations and comparison of theoretical values with exposure limits for public,offers the possibility of knowing the safety distance from those antennas.In this paper,we present results of estimations for the peak power density radiated from antennas,by eliminating or not considering reflected waves from different surfaces.This method is used to estimate the peak power density of non ionizing radiation of cellular antennas,installed in 12 regions of Albanian territory by a cellular operator.We have estimated the safety distances from all types of installed antennas,in order to have a clear idea for the safety distances from cellular base station at 900 MHz.展开更多
Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional...Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.展开更多
The electron density profile peaking and the impurity accumulation in the HL-2A tokamak plasma are observed when three kinds of fuelling methods are separately used at different fuelling particle locations. The densit...The electron density profile peaking and the impurity accumulation in the HL-2A tokamak plasma are observed when three kinds of fuelling methods are separately used at different fuelling particle locations. The density profile becomes more peaked when the line-averaged electron density approaches the Greenwald density limit nG and, consequently, impurity accumulation is often observed. A linear increase regime in the density range ne 〈 0.6nG and a saturation regime in ne 〉 0.6nG are obtained. There is no significant difference in achieved density peaking factor fne between the supersonic molecular beam injection （SMBI） and gas puffing into the plasma main chamber. However, the achieved fne is relatively low, in particular, in the case of density below 0.7nG, when the working gas is puffed into the divertor chamber. A discharge with a density as high as 1.2nG, i.e. ne ： 1.2nG, can be achieved by SMBI just after siliconization as a wall conditioning. The metallic impurities, such as iron and chromium, also increase remarkably when the impurity accumulation happens. The mechanism behind the density peaking and impurity accumulation is studied by investigating both the density peaking factor versus the effective collisionality and the radiation peaking versus density peaking.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
Pulsed microwaves are widely used inradar,navigation, and communication. The average power density is low at narrow pulse widths or large pulse intervals,but pulsed microwaves at certain peak densities exert numerous ...Pulsed microwaves are widely used inradar,navigation, and communication. The average power density is low at narrow pulse widths or large pulse intervals,but pulsed microwaves at certain peak densities exert numerous biological effects, including展开更多
Detecting overlapping communities in attributed networks remains a significant challenge due to the complexity of jointly modeling topological structure and node attributes,the unknown number of communities,and the ne...Detecting overlapping communities in attributed networks remains a significant challenge due to the complexity of jointly modeling topological structure and node attributes,the unknown number of communities,and the need to capture nodes with multiple memberships.To address these issues,we propose a novel framework named density peaks clustering with neutrosophic C-means.First,we construct a consensus embedding by aligning structure-based and attribute-based representations using spectral decomposition and canonical correlation analysis.Then,an improved density peaks algorithm automatically estimates the number of communities and selects initial cluster centers based on a newly designed cluster strength metric.Finally,a neutrosophic C-means algorithm refines the community assignments,modeling uncertainty and overlap explicitly.Experimental results on synthetic and real-world networks demonstrate that the proposed method achieves superior performance in terms of detection accuracy,stability,and its ability to identify overlapping structures.展开更多
To investigate the effect of saturation on the storage-dissipation properties and failure characteristics of red sandstone,as well as the energy mechanism of rockburst prevention by water,a series of uniaxial compress...To investigate the effect of saturation on the storage-dissipation properties and failure characteristics of red sandstone,as well as the energy mechanism of rockburst prevention by water,a series of uniaxial compression and uniaxial loading–unloading tests were conducted under five saturation levels.The effect of saturation on the mechanical properties and elastic energy density was analyzed,and a method for obtaining peak energy density was proposed.The effect of saturation on the energy evolution was examined,and the energy mechanism of water in preventing rockburst was revealed.The results indicate that an increase in saturation of red sandstone decreases the input energy density,elastic energy density,dissipated energy density,peak strength and peak strain;the compaction phase of the stress–strain curve becomes shorter;the failure mode transitions from X-conjugate oblique shear to single oblique shear;the variation in the debris ejection trajectory is as follows:radiation→X-ray→oblique upward parabola→horizontal parabola→oblique downward parabola;the degree of failure intensity and fragmentation is decreased gradually.Elastic energy density is interconnected with both saturation and stress but independent of the loading path.Saturation exhibits a dual effect on the energy storage property,i.e.,increasing saturation increases the energy storage efficiency and reduces the energy storage capacity.The ratio of peak elastic energy density to peak input energy density remains constant irrespective of saturation levels.Water prevents rockburst by decreasing the energy storage capacity of surrounding rock,alleviating the stress of surrounding rock to reduce energy storage,and elevating the energy release threshold of high-energy surrounding rock.The findings of this study contribute to understanding the effect of water on rock failure from an energy perspective,as well as provide theoretical guidance for rockburst prevention by water in deep tunnels.展开更多
To obtain the precise calculation method for the peak energy density and energy evolution properties of rocks subjected to uniaxial compression(UC)before the post-peak stage,particularly at s0.9sc(s denotes stress and...To obtain the precise calculation method for the peak energy density and energy evolution properties of rocks subjected to uniaxial compression(UC)before the post-peak stage,particularly at s0.9sc(s denotes stress and sc is the peak strength),extensive UC and uniaxial graded cyclical loading-unloading(GCLU)tests were performed on four rock types.In the GCLU tests,four unloading stress levels were designated when σ<0.9σc and six unloading stress levels were designated forσ≥0.9σc.The variations in the elastic energy density(ue),dissipative energy density(ud),and energy storage efficiency(C)for the four rock types under GCLU tests were analyzed.Based on the variation of ue whenσ≥0:9σc,a method for calculating the peak energy density was proposed.The energy evolution in rock under UC condition before the post-peak stage was examined.The relationship between C0.9(C atσ≥0:9σc)and mechanical behavior of rocks was explored,and the damage evolution of rock was analyzed in view of energy.Compared with that of the three existing methods,the accuracy of the calculation method of peak energy density proposed in this study is higher.These findings could provide a theoretical foundation for more accurately revealing the failure behavior of rock from an energy perspective.展开更多
基金supported by the National Natural Science Foundation of China (No.52205548)。
文摘To address the issues of unknown target size,blurred edges,background interference and low contrast in infrared small target detection,this paper proposes a method based on density peaks searching and weighted multi-feature local difference.Firstly,an improved high-boost filter is used for preprocessing to eliminate background clutter and high-brightness interference,thereby increasing the probability of capturing real targets in the density peak search.Secondly,a triple-layer window is used to extract features from the area surrounding candidate targets,addressing the uncertainty of small target sizes.By calculating multi-feature local differences between the triple-layer windows,the problems of blurred target edges and low contrast are resolved.To balance the contribution of different features,intra-class distance is used to calculate weights,achieving weighted fusion of multi-feature local differences to obtain the weighted multi-feature local differences of candidate targets.The real targets are then extracted using the interquartile range.Experiments on datasets such as SIRST and IRSTD-IK show that the proposed method is suitable for various complex types and demonstrates good robustness and detection performance.
文摘Cluster-basedmodels have numerous application scenarios in vehicular ad-hoc networks(VANETs)and can greatly help improve the communication performance of VANETs.However,the frequent movement of vehicles can often lead to changes in the network topology,thereby reducing cluster stability in urban scenarios.To address this issue,we propose a clustering model based on the density peak clustering(DPC)method and sparrow search algorithm(SSA),named SDPC.First,the model constructs a fitness function based on the parameters obtained from the DPC method and deploys the SSA for iterative optimization to select cluster heads(CHs).Then,the vehicles that have not been selected as CHs are assigned to appropriate clusters by comprehensively considering the distance parameter and link-reliability parameter.Finally,cluster maintenance strategies are considered to tackle the changes in the clusters’organizational structure.To verify the performance of the model,we conducted a simulation on a real-world scenario for multiple metrics related to clusters’stability.The results show that compared with the APROVE and the GAPC,SDPC showed clear performance advantages,indicating that SDPC can effectively ensure VANETs’cluster stability in urban scenarios.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(61225016)the State Key Program of National Natural Science of China(61533002)
文摘Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.
基金the National Natural Science Foundation of China(41925018,41874194).
文摘We report a simultaneous observation of two band electromagnetic ion cyclotron(EMIC)waves and toroidal Alfvén waves by the Van Allen Probe mission.Through wave frequency analyses,the mass densityρis found to be locally peaked at the magnetic equator.Perpendicular fluxes of ions(<100 eV)increase simultaneously with the appearances of EMIC waves,indicating a heating of these ions by EMIC waves.In addition,the measured ion distributions also support the equatorial peak formation,which accords with the result of the frequency analyses.The formation of local mass density peaks at the equator should be due to enhancements of equatorial ion concentrations,which are triggered by EMIC waves’perpendicular heating on low energy ions.
基金Professor Hong Yu at Intelligent Fishery Innovative Team(No.C202109)in School of Information Engineering of Dalian Ocean University for her support of this workfunded by the National Natural Science Foundation of China(No.31800615 and No.21933010)。
文摘Performing cluster analysis on molecular conformation is an important way to find the representative conformation in the molecular dynamics trajectories.Usually,it is a critical step for interpreting complex conformational changes or interaction mechanisms.As one of the density-based clustering algorithms,find density peaks(FDP)is an accurate and reasonable candidate for the molecular conformation clustering.However,facing the rapidly increasing simulation length due to the increase in computing power,the low computing efficiency of FDP limits its application potential.Here we propose a marginal extension to FDP named K-means find density peaks(KFDP)to solve the mass source consuming problem.In KFDP,the points are initially clustered by a high efficiency clustering algorithm,such as K-means.Cluster centers are defined as typical points with a weight which represents the cluster size.Then,the weighted typical points are clustered again by FDP,and then are refined as core,boundary,and redefined halo points.In this way,KFDP has comparable accuracy as FDP but its computational complexity is reduced from O(n^(2))to O(n).We apply and test our KFDP method to the trajectory data of multiple small proteins in terms of torsion angle,secondary structure or contact map.The comparing results with K-means and density-based spatial clustering of applications with noise show the validation of the proposed KFDP.
文摘We present a novel unsupervised integrated score framework to generate generic extractive multi- document summaries by ranking sentences based on dynamic programming （DP） strategy. Considering that cluster-based methods proposed by other researchers tend to ignore informativeness of words when they generate summaries, our proposed framework takes relevance, diversity, informativeness and length constraint of sentences into consideration comprehensively. We apply Density Peaks Clustering （DPC） to get relevance scores and diversity scores of sentences simultaneously. Our framework produces the best performance on DUC2004, 0.396 of ROUGE-1 score, 0.094 of ROUGE-2 score and 0.143 of ROUGE-SU4 which outperforms a series of popular baselines, such as DUC Best, FGB [7], and BSTM [10].
基金supported by The Franco-Thai scholarship program and Development and Promotion of Science and Technology Talents Projectbeen carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 under grant agreement No.633053。
文摘The impact of the E×B flow shear stabilization on particle transport and density peaking at JET is analyzed in the framework of integrated modelling with the CRONOS code.For that purpose,plasmas from a power scan which show a significant increasing of density peaking with the injected neutral beam injection power have been used as a modeling basis.By means of simulations with the quasilinear model GLF23 for the heat and particle transport,a strong link between the particle confinement and E×B flow shear stabilization is found.This is particularly important close to the pedestal region where the particle pinch direction becomes strongly inward for high E×B flow shear values.Such impact introduces some non-negligible deviation from the well-known collisonality dependence of the density peaking,whose general trend has been also obtained in the framework of this modelling by performing pedestal density scans.
基金The National Natural Science Foundation of China(No.61762031)The Science and Technology Major Project of Guangxi Province(NO.AA19046004)The Natural Science Foundation of Guangxi(No.2021JJA170130).
文摘There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem.Meanwhile, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.
基金supported by the National Basic Research Program(973)of China(No.2014CB340303)the National Natural Science Foundation of China(Nos.61502509 and 61222205)+1 种基金the Program for New Century Excellent Talents in Universitythe Fok Ying-Tong Education Foundation(No.141066)
文摘The density peak （DP） algorithm has been widely used in scientific research due to its novel and effective peak density-based clustering approach. However, the DP algorithm uses each pair of data points several times when determining cluster centers, yielding high computational complexity. In this paper, we focus on accelerating the time-consuming density peaks algorithm with a graphics processing unit （GPU）. We analyze the principle of the algorithm to locate its computational bottlenecks, and evaluate its potential for parallelism. In light of our analysis, we propose an efficient parallel DP algorithm targeting on a GPU architecture and implement this parallel method with compute unified device architecture （CUDA）, called the ‘CUDA-DP platform＇. Specifically, we use shared memory to improve data locality, which reduces the amount of global memory access. To exploit the coalescing accessing mechanism of CPU, we convert the data structure of the CUDA-DP program from array of structures to structure of arrays. In addition, we introduce a binary search-and-sampling method to avoid sorting a large array. The results of the experiment show that CUDA-DP can achieve a 45-fold acceleration when compared to the central processing unit based density peaks implementation.
文摘AlAs/GaAs/In0.1Ga0.9As/GaAs/AlAs double-barrier resonant tunneling diodes （DBRTDs） grown on a semi-insulated GaAs substrate with molecular beam epitaxy is demonstrated. By sandwiching the In0.1 Ga0.9 As layer between GaAs layers, potential wells beside the two sides of barrier are deepened, resulting in an increase of the peak-to-valley current ratio （PVCR） and a peak current density. A special shape of collector is designed in order to reduce contact resistance and non-uniformity of the current;as a result the total chrrent density in the device is increased. The use of thin barriers is also helpful for the improvement of the PVCR and the peak current density in DBRTDs. The devices exhibit a maximum PVCR of 13.98 and a peak current density of 89kA/cm^2 at room temperature.
基金Project supported by the National Natural Science Foundation of China (Key Program) (No.10332030)the Natural Science Foundation of Guangdong Province of China (No.04011640)
文摘The principal resonance of Duffing random external excitation was investigated. oscillator to combined deterministic and The random excitation was taken to be white noise or harmonic with separable random amplitude and phase. The method of multiple scales was used to determine the equations of modulation of amplitude and phase. The one peak probability density function of each of the two stable stationary solutions was calculated by the linearization method. These two one-peak-density functions were combined using the probability of realization of the two stable stationary solutions to obtain the double peak probability density function. The theoretical analysis are verified by numerical results.
文摘Estimation of peak power density in the vicinity of cellular base stations and comparison of theoretical values with exposure limits for public,offers the possibility of knowing the safety distance from those antennas.In this paper,we present results of estimations for the peak power density radiated from antennas,by eliminating or not considering reflected waves from different surfaces.This method is used to estimate the peak power density of non ionizing radiation of cellular antennas,installed in 12 regions of Albanian territory by a cellular operator.We have estimated the safety distances from all types of installed antennas,in order to have a clear idea for the safety distances from cellular base station at 900 MHz.
基金National Natural Science Foundation of China Nos.61962054 and 62372353.
文摘Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.
基金supported partially by the National Natural Science Foundation of China (Grant No 10475022)
文摘The electron density profile peaking and the impurity accumulation in the HL-2A tokamak plasma are observed when three kinds of fuelling methods are separately used at different fuelling particle locations. The density profile becomes more peaked when the line-averaged electron density approaches the Greenwald density limit nG and, consequently, impurity accumulation is often observed. A linear increase regime in the density range ne 〈 0.6nG and a saturation regime in ne 〉 0.6nG are obtained. There is no significant difference in achieved density peaking factor fne between the supersonic molecular beam injection （SMBI） and gas puffing into the plasma main chamber. However, the achieved fne is relatively low, in particular, in the case of density below 0.7nG, when the working gas is puffed into the divertor chamber. A discharge with a density as high as 1.2nG, i.e. ne ： 1.2nG, can be achieved by SMBI just after siliconization as a wall conditioning. The metallic impurities, such as iron and chromium, also increase remarkably when the impurity accumulation happens. The mechanism behind the density peaking and impurity accumulation is studied by investigating both the density peaking factor versus the effective collisionality and the radiation peaking versus density peaking.
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.
基金supported by the Foundation of Astronaut Research and Training Center of China [No.SMFA14B06 and No.14ZS017]
文摘Pulsed microwaves are widely used inradar,navigation, and communication. The average power density is low at narrow pulse widths or large pulse intervals,but pulsed microwaves at certain peak densities exert numerous biological effects, including
基金supported by the Natural Science Foundation of China(Grant No.72571150)。
文摘Detecting overlapping communities in attributed networks remains a significant challenge due to the complexity of jointly modeling topological structure and node attributes,the unknown number of communities,and the need to capture nodes with multiple memberships.To address these issues,we propose a novel framework named density peaks clustering with neutrosophic C-means.First,we construct a consensus embedding by aligning structure-based and attribute-based representations using spectral decomposition and canonical correlation analysis.Then,an improved density peaks algorithm automatically estimates the number of communities and selects initial cluster centers based on a newly designed cluster strength metric.Finally,a neutrosophic C-means algorithm refines the community assignments,modeling uncertainty and overlap explicitly.Experimental results on synthetic and real-world networks demonstrate that the proposed method achieves superior performance in terms of detection accuracy,stability,and its ability to identify overlapping structures.
基金supported by the National Natural Science Foundation of China(52104133,52304227)the Natural Science Foundation of Hunan Province(2021JJ40465,2023JJ40548)the Opening Foundation of the State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines(SKLMRDPC20KF03).
文摘To investigate the effect of saturation on the storage-dissipation properties and failure characteristics of red sandstone,as well as the energy mechanism of rockburst prevention by water,a series of uniaxial compression and uniaxial loading–unloading tests were conducted under five saturation levels.The effect of saturation on the mechanical properties and elastic energy density was analyzed,and a method for obtaining peak energy density was proposed.The effect of saturation on the energy evolution was examined,and the energy mechanism of water in preventing rockburst was revealed.The results indicate that an increase in saturation of red sandstone decreases the input energy density,elastic energy density,dissipated energy density,peak strength and peak strain;the compaction phase of the stress–strain curve becomes shorter;the failure mode transitions from X-conjugate oblique shear to single oblique shear;the variation in the debris ejection trajectory is as follows:radiation→X-ray→oblique upward parabola→horizontal parabola→oblique downward parabola;the degree of failure intensity and fragmentation is decreased gradually.Elastic energy density is interconnected with both saturation and stress but independent of the loading path.Saturation exhibits a dual effect on the energy storage property,i.e.,increasing saturation increases the energy storage efficiency and reduces the energy storage capacity.The ratio of peak elastic energy density to peak input energy density remains constant irrespective of saturation levels.Water prevents rockburst by decreasing the energy storage capacity of surrounding rock,alleviating the stress of surrounding rock to reduce energy storage,and elevating the energy release threshold of high-energy surrounding rock.The findings of this study contribute to understanding the effect of water on rock failure from an energy perspective,as well as provide theoretical guidance for rockburst prevention by water in deep tunnels.
基金the National Natural Science Foundation of China(Grant Nos.52104133 and 52304227)the Postdoctoral Foundation of Henan Province(Grant No.HN2022015)are appreciated.
文摘To obtain the precise calculation method for the peak energy density and energy evolution properties of rocks subjected to uniaxial compression(UC)before the post-peak stage,particularly at s0.9sc(s denotes stress and sc is the peak strength),extensive UC and uniaxial graded cyclical loading-unloading(GCLU)tests were performed on four rock types.In the GCLU tests,four unloading stress levels were designated when σ<0.9σc and six unloading stress levels were designated forσ≥0.9σc.The variations in the elastic energy density(ue),dissipative energy density(ud),and energy storage efficiency(C)for the four rock types under GCLU tests were analyzed.Based on the variation of ue whenσ≥0:9σc,a method for calculating the peak energy density was proposed.The energy evolution in rock under UC condition before the post-peak stage was examined.The relationship between C0.9(C atσ≥0:9σc)and mechanical behavior of rocks was explored,and the damage evolution of rock was analyzed in view of energy.Compared with that of the three existing methods,the accuracy of the calculation method of peak energy density proposed in this study is higher.These findings could provide a theoretical foundation for more accurately revealing the failure behavior of rock from an energy perspective.
