Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network

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.

K-means Find Density Peaks in Molecular Conformation Clustering

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.

Density peaks clustering based integrate framework for multi-document summarization

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].

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.

Efficient parallel implementation of a density peaks clustering algorithm on graphics processing unit

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.

A DBRTD with a High PVCR and a Peak Current Density at Room Temperature

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.

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。

Coarse-Grained Molecular Dynamics Study based on TorchMD

The coarse grained(CG)model implements the molecular dynamics simulation by simplifying atom properties and interaction between them.Despite losing certain detailed information,the CG model is still the first-thought option to study the large molecule in long time scale with less computing resource.The deep learning model mainly mimics the human studying process to handle the network input as the image to achieve a good classification and regression result.In this work,the TorchMD,a MD framework combining the CG model and deep learning model,is applied to study the protein folding process.In 3D collective variable(CV)space,the modified find density peaks algorithm is applied to cluster the conformations from the TorchMD CG simulation.The center conformation in different states is searched.And the boundary conformations between clusters are assigned.The string algorithm is applied to study the path between two states,which are compared with the end conformations from all atoms simulations.The result shows that the main phenomenon of protein folding with TorchMD CG model is the same as the all-atom simulations,but with a less simulating time scale.The workflow in this work provides another option to study the protein folding and other relative processes with the deep learning CG model.

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

A study of the strong pulses detected from PSR B0656+14 using the Urumqi 25-m radio telescope at 1540 MHz

We report on the properties of strong pulses from PSR B0656＋14 by analyzing the data obtained using the Urumqi 25-m radio telescope at 1540 MHz from August 2007 to September 2010.In 44 h of observational data,a total of 67 pulses with signal-to-noise ratios above a 5σthreshold were detected.The peak flux densities of these pulses are 58 to 194 times that of the average profile,and their pulse energies are 3 to 68 times that of the average pulse.These pulses are clustered around phases about 5-ahead of the peak of the average profile.Compared with the width of the average profile,they are relatively narrow,with the full widths at half-maximum ranging from 0.28 ° to 1.78 °.The distribution of pulse-energies follows a lognormal distribution.These sporadic strong pulses detected from PSR B0656＋14 have different characteristics from both typical giant pulses and its regular pulses.

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.