A Health State Prediction Model Based on Belief Rule Base and LSTM for Complex Systems

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.

Formation of the mass density peak at the magnetospheric equator triggered by EMIC waves

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.

Impact of E×B flow shear stabilization on particle confinement and density peaking at JET

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.

Density and impurity profile behaviours in HL-2A tokamak with different gas fuelling methods

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.

Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

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.

ON DOUBLE PEAK PROBABILITY DENSITY FUNCTIONS OF DUFFING OSCILLATOR TO COMBINED DETERMINISTIC AND RANDOM EXCITATIONS

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.

几种经典聚类算法的比较研究

本文选取K-means、FCM、Spectral Cluster、Density Peak Cluster四种经典聚类算法作为研究对象,从理论和实验两个角度对它们进行比较研究。首先,本文介绍了聚类的含义、准则及应用;其次,本文分别阐述了四种算法的原理,并从理论角度分析它们的异同;再次,本文在UCI数据集上对四种算法执行了对比实验,比较它们的聚类准确率;最后,根据理论分析和对比实验的结果,得出四种算法适应不同类型数据集的结论。

Density Clustering Algorithm Based on KD-Tree and Voting Rules

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.

Effects on the Behavior and Neuroimmunity of Pulsed Microwaves with Different Peak Densities

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

Dynamic characteristics of bubbling fluidization through recurrence rate analysis of pressure fluctuations

Pressure fluctuations signals of a lab-scale fiuidized bed （15 cm inner diameter and 2 m height） at different superficial gas velocities were measured. Recurrence plot （RP） and recurrence rate （RR）, and the simplest variable of recurrence quantification analysis （RQA） were used to analyze the pressure signals. Different patterns observed in RP reflect different dynamic behavior of the system under study. It was also found that the variance of RR （a2R） Could reveal the peak dominant frequencies （PDF） of different dynamic systems： completely periodic, completely stochastic, Lorenz system, and fluidized bed. The results were compared with power spectral density. Additionally, the diagram of σ^2RR provides a new technique for prediction of transition velocity from bubbling to turbulent fluidization regime.