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
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
Clustering filtering is usually a practical method for light detection and ranging(LiDAR)point clouds filtering according to their characteristic attributes.However,the amount of point cloud data is extremely large in...Clustering filtering is usually a practical method for light detection and ranging(LiDAR)point clouds filtering according to their characteristic attributes.However,the amount of point cloud data is extremely large in practice,making it impossible to cluster point clouds data directly,and the filtering error is also too large.Moreover,many existing filtering algorithms have poor classification results in discontinuous terrain.This article proposes a new fast classification filtering algorithm based on density clustering,which can solve the problem of point clouds classification in discontinuous terrain.Based on the spatial density of LiDAR point clouds,also the features of the ground object point clouds and the terrain point clouds,the point clouds are clustered firstly by their elevations,and then the plane point clouds are selected.Thus the number of samples and feature dimensions of data are reduced.Using the DBSCAN clustering filtering method,the original point clouds are finally divided into noise point clouds,ground object point clouds,and terrain point clouds.The experiment uses 15 sets of data samples provided by the International Society for Photogrammetry and Remote Sensing(ISPRS),and the results of the proposed algorithm are compared with the other eight classical filtering algorithms.Quantitative and qualitative analysis shows that the proposed algorithm has good applicability in urban areas and rural areas,and is significantly better than other classic filtering algorithms in discontinuous terrain,with a total error of about 10%.The results show that the proposed method is feasible and can be used in different terrains.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
To fully explore the potential features contained in power load data,an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed.Firstly,a density peak f...To fully explore the potential features contained in power load data,an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed.Firstly,a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data,accurately dividing subsets of data into different categories.Secondly,introducing convolutional block attention mechanism into the bidirectional gated recurrent unit(BiGRU)structure significantly enhances its ability to extract key features.On this basis,in order to make the model more accurately adapt to the dynamic changes in power load data,subsets of different categories of data were used for BiGRU training based on attention mechanism,and extreme gradient boosting was selected as the meta model to effectively integrate multiple sets of historical training information.To further optimize the parameter configuration of the meta model,Bayesian optimization techniques are used to achieve automated adjustment of hyperparameters.Multiple sets of comparative experiments were designed,and the results showed that the average absolute error of the method in this paper was reduced by about 8.33%and 4.28%,respectively,compared with the single model and the combined model,and the determination coefficient reached the highest of 95.99,which proved that the proposed method has a better prediction effect.展开更多
Encephalitis is a brain inflammation disease.Encephalitis can yield to seizures,motor disability,or some loss of vision or hearing.Sometimes,encepha-litis can be a life-threatening and proper diagnosis in an early stag...Encephalitis is a brain inflammation disease.Encephalitis can yield to seizures,motor disability,or some loss of vision or hearing.Sometimes,encepha-litis can be a life-threatening and proper diagnosis in an early stage is very crucial.Therefore,in this paper,we are proposing a deep learning model for computerized detection of Encephalitis from the electroencephalogram data(EEG).Also,we propose a Density-Based Clustering model to classify the distinctive waves of Encephalitis.Customary clustering models usually employ a computed single centroid virtual point to define the cluster configuration,but this single point does not contain adequate information.To precisely extract accurate inner structural data,a multiple centroids approach is employed and defined in this paper,which defines the cluster configuration by allocating weights to each state in the cluster.The multiple EEG view fuzzy learning approach incorporates data from every sin-gle view to enhance the model's clustering performance.Also a fuzzy Density-Based Clustering model with multiple centroids(FDBC)is presented.This model employs multiple real state centroids to define clusters using Partitioning Around Centroids algorithm.The Experimental results validate the medical importance of the proposed clustering model.展开更多
Finding clusters based on density represents a significant class of clustering algorithms.These methods can discover clusters of various shapes and sizes.The most studied algorithm in this class is theDensity-Based Sp...Finding clusters based on density represents a significant class of clustering algorithms.These methods can discover clusters of various shapes and sizes.The most studied algorithm in this class is theDensity-Based Spatial Clustering of Applications with Noise(DBSCAN).It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects.It requires two input parameters:epsilon(fixed neighborhood radius)and MinPts(the lowest number of objects in epsilon).However,it can’t handle clusters of various densities since it uses a global value for epsilon.This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one.Only user input in the proposed method is the MinPts.Epsilon on the other hand,is computed automatically based on statistical information of the dataset.The proposed method finds the core distance for each object in the dataset,takes the average of these distances as the first value of epsilon,and finds the clusters satisfying this density level.The remaining unclustered objects will be clustered using a new value of epsilon that equals the average core distances of unclustered objects.This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset’s size.The proposed method requires MinPts only as an input parameter because epsilon is computed from data.Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results.Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them.The accuracy of the method ranges from 92%to 100%for the experimented datasets.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
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
The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of ...The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.展开更多
Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic fire...Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls.Many intrusion detection methods are processed through machine learning.Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology.However,almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data.In this paper,a new hybrid learning method is proposed on the basis of features such as density,cluster centers,and nearest neighbors(DCNN).In this algorithm,data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor.k-NN classifier is adopted to classify the new feature vectors.Our experiment shows that DCNN,which combines K-means,clustering-based density,and k-NN classifier,is effective in intrusion detection.展开更多
Clustering is the main method of deinterleaving of radar pulse using multi-parameter.However,the problem in clustering of radar pulses lies in finding the right number of clusters.To solve this problem,a method is pro...Clustering is the main method of deinterleaving of radar pulse using multi-parameter.However,the problem in clustering of radar pulses lies in finding the right number of clusters.To solve this problem,a method is proposed based on Self-Organizing Feature Maps(SOFM) and Composed Density between and within clusters(CDbw).This method firstly extracts the feature of Direction Of Arrival(DOA) data by SOFM using the characteristic of DOA parameter,and then cluster of SOFM.Through computing the cluster validity index CDbw,the right number of clusters is found.The results of simulation show that the method is effective in sorting the data of DOA.展开更多
We propose an optimized cluster density matrix embedding theory(CDMET).It reduces the computational cost of CDMET with simpler bath states.And the result is as accurate as the original one.As a demonstration,we study ...We propose an optimized cluster density matrix embedding theory(CDMET).It reduces the computational cost of CDMET with simpler bath states.And the result is as accurate as the original one.As a demonstration,we study the distant correlations of the Heisenberg J_(1)-J_(2)model on the square lattice.We find that the intermediate phase(0.43≤sssim J_(2)≤sssim 0.62)is divided into two parts.One part is a near-critical region(0.43≤J_(2)≤0.50).The other part is the plaquette valence bond solid(PVB)state(0.51≤J_(2)≤0.62).The spin correlations decay exponentially as a function of distance in the PVB.展开更多
Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating...Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.展开更多
Percolation theory deals with the numbers and properties of the clusters formed in the different occupation probability. In this Paper, we study the calculation method of small clusters. We calcu-lated the small clust...Percolation theory deals with the numbers and properties of the clusters formed in the different occupation probability. In this Paper, we study the calculation method of small clusters. We calcu-lated the small cluster density of 1, 2 and 3 in the percolation model with the exact method and the numerical method. The results of the two methods are very close, which can be verified by each other. We find that the cluster density of all three kinds of small clusters reaches the highest value when the occupation probability is between 0.1 and 0.2. It is very difficult to get the analytical formula for the exact method when the cluster area is relatively large (such as the area is more than 50), so we can get the density value of the cluster by numerical method. We find that the time required calculating the cluster density is proportional to the percolation area, which is indepen-dent of the cluster size and the occupation probability.展开更多
A systematic study on geometry, electronic structure and vibrational properties of N-doped TiO2 anatase cluster, within the framework of the density functional theory, has been performed in this work. The calculations...A systematic study on geometry, electronic structure and vibrational properties of N-doped TiO2 anatase cluster, within the framework of the density functional theory, has been performed in this work. The calculations confirmed that the most structures in substitutional model consist of a two-coordinate bridge structure and a three-coordinate hollow structure. The calculated results can well explain the red shift in N-doped TiO2 observed in experiments. The study provides an illustration for the N-doped anatase from the viewpoint of chemical bonding theory.展开更多
In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling struct...In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,the Density Peak Clustering(DPC)algorithmis used todetermine referential values of indicators for BRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.展开更多
The reactions of anionic zirconium oxide clusters ZrxOy- with C2H6 and C4H10 are investi-gated by a time of flight mass spectrometer coupled with a laser vaporization cluster source.Hydrogen containing products Zr2O5H...The reactions of anionic zirconium oxide clusters ZrxOy- with C2H6 and C4H10 are investi-gated by a time of flight mass spectrometer coupled with a laser vaporization cluster source.Hydrogen containing products Zr2O5H- and Zr3O7H- are observed after the reaction. Den-sity functional theory calculations indicate that the hydrogen abstraction is favorable in the reaction of Zr2O5- with C2H6, which supports that the observed Zr2O5H- and Zr3O7H- are due to hydrogen atom abstraction from the alkane molecules. This work shows a newpossible pathway in the reaction of zirconium oxide cluster anions with alkane molecules.展开更多
Due to depletion interactions, a few of colloidal spheres will be packed into cluster or clusters, even a phase transition may take place if the volume fraction of system is large enough. In a binary colloidal system,...Due to depletion interactions, a few of colloidal spheres will be packed into cluster or clusters, even a phase transition may take place if the volume fraction of system is large enough. In a binary colloidal system, if the mole fraction of one component is very small, then it can be taken as the impurity of the other component. In this work, the effect of impurity on critical conditions of colloidal cluster nucleation was studied by Carnahan-Starling state equation and the principle of entropy maximum. The results show that, even the mole fraction of small-spheres is very small, the critical volume fraction is obvious smaller than that of one component system, so the influence on critical volume fraction from impurity is very huge and cannot be ignored. In addition, it is also found that, the larger the volume fraction of the system is, the larger cluster density can be packed, however, the critical size of nucleating cluster is almost independent of the density of the cluster.展开更多
CFSFDP(Clustering by fast search and find of density peak)is a simple and crisp density clustering algorithm.It does not only have the advantages of density clustering algorithm,but also can find the peak of cluster a...CFSFDP(Clustering by fast search and find of density peak)is a simple and crisp density clustering algorithm.It does not only have the advantages of density clustering algorithm,but also can find the peak of cluster automatically.However,the lack of adaptability makes it difficult to apply in intrusion detection.The new input cannot be updated in time to the existing profiles,and rebuilding profiles would waste a lot of time and computation.Therefore,an adaptive anomaly detection algorithm based on CFSFDP is proposed in this paper.By analyzing the influence of new input on center,edge and discrete points,the adaptive problem mainly focuses on processing with the generation of new cluster by new input.The improved algorithm can integrate new input into the existing clustering without changing the original profiles.Meanwhile,the improved algorithm takes the advantage of multi-core parallel computing to deal with redundant computing.A large number of experiments on intrusion detection on Android platform and KDDCUP 1999 show that the improved algorithm can update the profiles adaptively without affecting the original detection performance.Compared with the other classical algorithms,the improved algorithm based on CFSFDP has the good basic performance and more room of improvement.展开更多
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 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The Natural Science Foundation of Hunan Province,China(No.2020JJ4601)Open Fund of the Key Laboratory of Highway Engi-neering of Ministry of Education(No.kfj190203).
文摘Clustering filtering is usually a practical method for light detection and ranging(LiDAR)point clouds filtering according to their characteristic attributes.However,the amount of point cloud data is extremely large in practice,making it impossible to cluster point clouds data directly,and the filtering error is also too large.Moreover,many existing filtering algorithms have poor classification results in discontinuous terrain.This article proposes a new fast classification filtering algorithm based on density clustering,which can solve the problem of point clouds classification in discontinuous terrain.Based on the spatial density of LiDAR point clouds,also the features of the ground object point clouds and the terrain point clouds,the point clouds are clustered firstly by their elevations,and then the plane point clouds are selected.Thus the number of samples and feature dimensions of data are reduced.Using the DBSCAN clustering filtering method,the original point clouds are finally divided into noise point clouds,ground object point clouds,and terrain point clouds.The experiment uses 15 sets of data samples provided by the International Society for Photogrammetry and Remote Sensing(ISPRS),and the results of the proposed algorithm are compared with the other eight classical filtering algorithms.Quantitative and qualitative analysis shows that the proposed algorithm has good applicability in urban areas and rural areas,and is significantly better than other classic filtering algorithms in discontinuous terrain,with a total error of about 10%.The results show that the proposed method is feasible and can be used in different terrains.
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 in part by the Fundamental Research Funds for the Liaoning Universities(LJ212410146025)the Graduate Science and Technology Innovation Project of University of Science and Technology Liaoning(LKDYC202310).
文摘To fully explore the potential features contained in power load data,an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed.Firstly,a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data,accurately dividing subsets of data into different categories.Secondly,introducing convolutional block attention mechanism into the bidirectional gated recurrent unit(BiGRU)structure significantly enhances its ability to extract key features.On this basis,in order to make the model more accurately adapt to the dynamic changes in power load data,subsets of different categories of data were used for BiGRU training based on attention mechanism,and extreme gradient boosting was selected as the meta model to effectively integrate multiple sets of historical training information.To further optimize the parameter configuration of the meta model,Bayesian optimization techniques are used to achieve automated adjustment of hyperparameters.Multiple sets of comparative experiments were designed,and the results showed that the average absolute error of the method in this paper was reduced by about 8.33%and 4.28%,respectively,compared with the single model and the combined model,and the determination coefficient reached the highest of 95.99,which proved that the proposed method has a better prediction effect.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R113)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Encephalitis is a brain inflammation disease.Encephalitis can yield to seizures,motor disability,or some loss of vision or hearing.Sometimes,encepha-litis can be a life-threatening and proper diagnosis in an early stage is very crucial.Therefore,in this paper,we are proposing a deep learning model for computerized detection of Encephalitis from the electroencephalogram data(EEG).Also,we propose a Density-Based Clustering model to classify the distinctive waves of Encephalitis.Customary clustering models usually employ a computed single centroid virtual point to define the cluster configuration,but this single point does not contain adequate information.To precisely extract accurate inner structural data,a multiple centroids approach is employed and defined in this paper,which defines the cluster configuration by allocating weights to each state in the cluster.The multiple EEG view fuzzy learning approach incorporates data from every sin-gle view to enhance the model's clustering performance.Also a fuzzy Density-Based Clustering model with multiple centroids(FDBC)is presented.This model employs multiple real state centroids to define clusters using Partitioning Around Centroids algorithm.The Experimental results validate the medical importance of the proposed clustering model.
基金The author extends his appreciation to theDeputyship forResearch&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number(IFPSAU-2021/01/17758).
文摘Finding clusters based on density represents a significant class of clustering algorithms.These methods can discover clusters of various shapes and sizes.The most studied algorithm in this class is theDensity-Based Spatial Clustering of Applications with Noise(DBSCAN).It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects.It requires two input parameters:epsilon(fixed neighborhood radius)and MinPts(the lowest number of objects in epsilon).However,it can’t handle clusters of various densities since it uses a global value for epsilon.This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one.Only user input in the proposed method is the MinPts.Epsilon on the other hand,is computed automatically based on statistical information of the dataset.The proposed method finds the core distance for each object in the dataset,takes the average of these distances as the first value of epsilon,and finds the clusters satisfying this density level.The remaining unclustered objects will be clustered using a new value of epsilon that equals the average core distances of unclustered objects.This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset’s size.The proposed method requires MinPts only as an input parameter because epsilon is computed from data.Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results.Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them.The accuracy of the method ranges from 92%to 100%for the experimented datasets.
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 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.
文摘The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.
文摘Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls.Many intrusion detection methods are processed through machine learning.Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology.However,almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data.In this paper,a new hybrid learning method is proposed on the basis of features such as density,cluster centers,and nearest neighbors(DCNN).In this algorithm,data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor.k-NN classifier is adopted to classify the new feature vectors.Our experiment shows that DCNN,which combines K-means,clustering-based density,and k-NN classifier,is effective in intrusion detection.
文摘Clustering is the main method of deinterleaving of radar pulse using multi-parameter.However,the problem in clustering of radar pulses lies in finding the right number of clusters.To solve this problem,a method is proposed based on Self-Organizing Feature Maps(SOFM) and Composed Density between and within clusters(CDbw).This method firstly extracts the feature of Direction Of Arrival(DOA) data by SOFM using the characteristic of DOA parameter,and then cluster of SOFM.Through computing the cluster validity index CDbw,the right number of clusters is found.The results of simulation show that the method is effective in sorting the data of DOA.
文摘We propose an optimized cluster density matrix embedding theory(CDMET).It reduces the computational cost of CDMET with simpler bath states.And the result is as accurate as the original one.As a demonstration,we study the distant correlations of the Heisenberg J_(1)-J_(2)model on the square lattice.We find that the intermediate phase(0.43≤sssim J_(2)≤sssim 0.62)is divided into two parts.One part is a near-critical region(0.43≤J_(2)≤0.50).The other part is the plaquette valence bond solid(PVB)state(0.51≤J_(2)≤0.62).The spin correlations decay exponentially as a function of distance in the PVB.
基金supported in part by the National Key Research and Development Program of China under Grant 2018YFA0702501in part by NSFC under Grant 41974126,41674116 and 42004101。
文摘Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.
文摘Percolation theory deals with the numbers and properties of the clusters formed in the different occupation probability. In this Paper, we study the calculation method of small clusters. We calcu-lated the small cluster density of 1, 2 and 3 in the percolation model with the exact method and the numerical method. The results of the two methods are very close, which can be verified by each other. We find that the cluster density of all three kinds of small clusters reaches the highest value when the occupation probability is between 0.1 and 0.2. It is very difficult to get the analytical formula for the exact method when the cluster area is relatively large (such as the area is more than 50), so we can get the density value of the cluster by numerical method. We find that the time required calculating the cluster density is proportional to the percolation area, which is indepen-dent of the cluster size and the occupation probability.
基金Supported by the National Natural Science Foundation of China (No. 20503021)National Basic Research Program of China (2007CB815301)
文摘A systematic study on geometry, electronic structure and vibrational properties of N-doped TiO2 anatase cluster, within the framework of the density functional theory, has been performed in this work. The calculations confirmed that the most structures in substitutional model consist of a two-coordinate bridge structure and a three-coordinate hollow structure. The calculated results can well explain the red shift in N-doped TiO2 observed in experiments. The study provides an illustration for the N-doped anatase from the viewpoint of chemical bonding theory.
基金supported by the Natural Science Foundation of China underGrant 61833016 and 61873293the Shaanxi OutstandingYouth Science Foundation underGrant 2020JC-34the Shaanxi Science and Technology Innovation Team under Grant 2022TD-24.
文摘In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,the Density Peak Clustering(DPC)algorithmis used todetermine referential values of indicators for BRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.
基金This work was supported by the Chinese Academy of Sciences （Hundred Talents Fund）, the National Natural Science Foundation of China （No.20703048 and No.20803083）, and the Center of Molecular Science Foundation of Institute of Chemistry, Chinese Academy of Sciences （No.CMS-LX200902）.
文摘The reactions of anionic zirconium oxide clusters ZrxOy- with C2H6 and C4H10 are investi-gated by a time of flight mass spectrometer coupled with a laser vaporization cluster source.Hydrogen containing products Zr2O5H- and Zr3O7H- are observed after the reaction. Den-sity functional theory calculations indicate that the hydrogen abstraction is favorable in the reaction of Zr2O5- with C2H6, which supports that the observed Zr2O5H- and Zr3O7H- are due to hydrogen atom abstraction from the alkane molecules. This work shows a newpossible pathway in the reaction of zirconium oxide cluster anions with alkane molecules.
文摘Due to depletion interactions, a few of colloidal spheres will be packed into cluster or clusters, even a phase transition may take place if the volume fraction of system is large enough. In a binary colloidal system, if the mole fraction of one component is very small, then it can be taken as the impurity of the other component. In this work, the effect of impurity on critical conditions of colloidal cluster nucleation was studied by Carnahan-Starling state equation and the principle of entropy maximum. The results show that, even the mole fraction of small-spheres is very small, the critical volume fraction is obvious smaller than that of one component system, so the influence on critical volume fraction from impurity is very huge and cannot be ignored. In addition, it is also found that, the larger the volume fraction of the system is, the larger cluster density can be packed, however, the critical size of nucleating cluster is almost independent of the density of the cluster.
基金supported in part by the National Key Research and Development Program of China under Grant No.2018YFB1800303the Science and Technology Planning Project of Jilin Province under Grant No.20190302070GXthe Science and Technology Projects of Jilin Provincial Education Department(the 13th five year plan)under Grant Nos.JJKH20190593KJ,JJKH20190546KJ,and JJKH20200795KJ.
文摘CFSFDP(Clustering by fast search and find of density peak)is a simple and crisp density clustering algorithm.It does not only have the advantages of density clustering algorithm,but also can find the peak of cluster automatically.However,the lack of adaptability makes it difficult to apply in intrusion detection.The new input cannot be updated in time to the existing profiles,and rebuilding profiles would waste a lot of time and computation.Therefore,an adaptive anomaly detection algorithm based on CFSFDP is proposed in this paper.By analyzing the influence of new input on center,edge and discrete points,the adaptive problem mainly focuses on processing with the generation of new cluster by new input.The improved algorithm can integrate new input into the existing clustering without changing the original profiles.Meanwhile,the improved algorithm takes the advantage of multi-core parallel computing to deal with redundant computing.A large number of experiments on intrusion detection on Android platform and KDDCUP 1999 show that the improved algorithm can update the profiles adaptively without affecting the original detection performance.Compared with the other classical algorithms,the improved algorithm based on CFSFDP has the good basic performance and more room of improvement.
