Research of Web Documents Clustering Based on Dynamic Concept

Conceptual clustering is mainly used for solving the deficiency and incompleteness of domain knowledge. Based on conceptual clustering technology and aiming at the institutional framework and characteristic of Web theme information, this paper proposes and implements dynamic conceptual clustering algorithm and merging algorithm for Web documents, and also analyses the super performance of the clustering algorithm in efficiency and clustering accuracy. Key words conceptual clustering - clustering center - dynamic conceptual clustering - theme - web documents clustering CLC number TP 311 Foundation item: Supported by the National “863” Program of China (2002AA111010, 2003AA001032)Biography: WANG Yun-hua(1979-), male, Master candidate, research direction: knowledge engineering and data mining.

Study of acoustic bubble cluster dynamics using a lattice Boltzmann model

The search for the development of a reliable mathematical model for understanding bubble dynamics behavior is an ongoing endeavor.A long list of complex phenomena underlies the physics of this problem.In the past decades,the lattice Boltzmann method has emerged as a promising tool to address such complexities.In this regard,we have applied a 121-velocity multiphase lattice Boltzmann model to an asymmetric cluster of bubbles in an acoustic field.A problem as a benchmark is studied to check the consistency and applicability of the model.The problem of interest is to study the deformation and coalescence phenomena in bubble cluster dynamics,as well as the screening effect on an acoustic multibubble medium.It has been observed that the LB model is able to simulate the combination of the three aforementioned phenomena for a bubble cluster as a whole and for every individual bubble in the cluster.

The Accumulation of He on a W Surface During keV-He Irradiation:Cluster Dynamics Modeling

The accumulation of He on a W surface during keV-He ion irradiation has been simulated using cluster dynamics modeling. This is based mainly on rate theory and improved by involving different types of objects, adopting up-to-date parameters and complex reaction processes, as well as considering the diffusion process along with depth. These new features make the simulated results compare very well with the experimental ones. The accumulation and diffusion processes are analyzed, and the depth and size dependence of the He concentrations contributed by different types of He clusters is also discussed. The exploration of the trapping and diffusion effects of the He atoms is helpful in understanding the evolution of the damages in the near-surface of plasma-facing materials under He ion irradiation.

Cluster dynamics modeling of niobium and titanium carbide precipitates inα-Fe andγ-Fe

Kinetic behaviors of niobium and titanium carbide precipitates in iron are simulated with cluster dynamics.The simulations,carried out in austenite and ferrite for niobium carbides,and in austenite for titanium carbide,are analyzed for dependences on temperature,solute concentration,and initial cluster distribution.The results are presented for different temperatures and solute concentrations,compared to experimental data available.They show little impact of initial cluster distribution beyond a certain relaxation time and that highly dilute alloys with monomers only present a significantly different behavior from denser alloys or ones with different initial cluster distributions.

Line profile studies of hydrodynamical models of cometary compact H II regions

We simulate the evolution of cometary H II regions based on several champagne flow models and bow shock models, and calculate the profiles of the [Ne II] fine-structure line at 12.81 μm, the H30α recombination line and the [Ne III] finestructure line at 15.55 μm for these models at different inclinations of 0°, 30° and 60°. We find that the profiles in the bow shock models are generally different from those in the champagne flow models, but the profiles in the bow shock models with lower stellar velocity （≤ 5 km s^-1） are similar to those in the champagne flow models. In champagne flow models, both the velocity of peak flux and the flux weighted central velocities of all three lines point outward from molecular clouds. In bow shock models, the directions of these velocities depend on the speed of stars. The central velocities of these lines are consistent with the stellar motion in the high stellar speed cases, but they are opposite directions from the stellar motion in the low speed cases. We notice that the line profiles from the slit along the symmetrical axis of the projected 2D image of these models are useful for distinguishing bow shock models from champagne flow models. It is also confirmed by the calculation that the flux weighted central velocity and the line luminosity of the [Ne III] line can be estimated from the [Ne II] line and the H30α line.

Evolution of ion-irradiated point defect concentration by cluster dynamics simulation

The relationship between ions irradiation and the induced microstructures(point defects,dislocations,clusters,etc.)could be better analyzed and explained by simulation.The mean field rate theory and cluster dynamics are used to simulate the effect of implanted Fe on the point defects concentration quantitatively.It is found that the depth distribution of point defect concentration is relatively gentle than that of damage calculated by SRIM software.Specifically,the damage rate and point defect concentration increase by 1.5 times and 0.6 times from depth of 120 nm to 825 nm,respectively.With the consideration of implanted Fe ions,which effectively act as interstitial atoms at the depth of high ion implantation rate,the vacancy concentration Cv decreases significantly after reaching the peak value,while the interstitial atom concentration Ci increases significantly after decline of the previous stage.At the peak depth of ion implantation,Cv dropped by 86%,and Ci increased by 6.2 times.Therefore,the implanted ions should be considered into the point defects concentration under high dose of heavy ion irradiation,which may help predict the concentration distribution of defect clusters,further analyzing the evolution behavior of solute precipitation.

A LOCAL DYNAMIC CLUSTER SELF-ORGANIZATION ALGORITHM IN WIRELESS SENSOR NETWORKS FOR RAINFALL MONITORING

Wireless Sensor Networks for Rainfall Monitoring (RM-WSNs) is a sensor network for the large-scale regional and moving rainfall monitoring,which could be controlled deployment. Delivery delay and cross-cluster calculation leads to information inaccuracy by the existing dynamic collabo-rative self-organization algorithm in WSNs. In this letter,a Local Dynamic Cluster Self-organization algorithm (LDCS) is proposed for the large-scale regional and moving target monitoring in RM-WSNs. The algorithm utilizes the resource-rich node in WSNs as the cluster head,which processes target information obtained by sensor nodes in cluster. The cluster head shifts with the target moving in chance and re-groups a new cluster. The target information acquisition is limited in the dynamic cluster,which can reduce information across-clusters transfer delay and improve the real-time of information acquisition. The simulation results show that,LDCS can not only relieve the problem of "too frequent leader switches" in IDSQ,also make full use of the history monitoring information of target and con-tinuous monitoring of sensor nodes that failed in DCS.

MOLECULAR DYNAMICS SIMULATIONS OF FILLED AND EMPTY CAGE-LIKE WATER CLUSTERS IN LIQUID WATER AND THEIR SIGNIFICANCE TO GAS HYDRATE FORMATION MECHANISMS

Molecular dynamics simulations are performed to observe the evolutions of 512 and 51262 cage-like water clusters filled with or without a methane molecule immersed in bulk liquid water at 250 K and 230 K. The lifetimes of these clusters are calculated according to their Lindemann index δ (t) using the criteria of δ≥0.07. For both the filled and empty clusters, we find the dynamics of bulk water determines the lifetimes of cage-like water clusters, and that the lifetime of 512 62 cage-like cluster is the same as that of 512 cage-like cluster. Although the methane molecule indeed makes the filled cage-like cluster more stable than the empty one, the empty cage-like cluster still has chance to be long-lived compared with the filled clusters. These observations support the labile cluster hypothesis on the formation mechanisms of gas hydrates.

Improvements to the fuzzy mathematics comprehensive quantitative method for evaluating fault sealing

Fuzzy mathematics is an important means to quantitatively evaluate the properties of fault sealing in petroleum reservoirs.To accurately study fault sealing,the comprehensive quantitative evaluation method of fuzzy mathematics is improved based on a previous study.First,the single-factor membership degree is determined using the dynamic clustering method,then a single-factor evaluation matrix is constructed using a continuous grading function,and finally,the probability distribution of the evaluation grade in a fuzzy evaluation matrix is analyzed.In this study,taking the F1 fault located in the northeastern Chepaizi Bulge as an example,the sealing properties of faults in different strata are quantitatively evaluated using both an improved and an un-improved comprehensive fuzzy mathematics quantitative evaluation method.Based on current oil and gas distribution,it is found that our evaluation results before and after improvement are significantly different.For faults in＂best＂and＂poorest＂intervals,our evaluation results are consistent with oil and gas distribution.However,for the faults in＂good＂or＂poor＂intervals,our evaluation is not completelyconsistent with oil and gas distribution.The improved evaluation results reflect the overall and local sealing properties of target zones and embody the nonuniformity of fault sealing,indicating the improved method is more suitable for evaluating fault sealing under complicated conditions.

Theoretical estimation of sonochemical yield in bubble cluster in acoustic field

In order to learn more about the physical phenomena occurring in cloud cavitation,the nonlinear dynamics of a spherical cluster of cavitation bubbles and cavitation bubbles in cluster in an acoustic field excited by a square pressure wave are numerically investigated by considering viscosity,surface tension,and the weak compressibility of the liquid.The theoretical prediction of the yield of oxidants produced inside bubbles during the strong collapse stage of cavitation bubbles is also investigated.The effects of acoustic frequency,acoustic pressure amplitude,and the number of bubbles in cluster on bubble temperature and the quantity of oxidants produced inside bubbles are analyzed.The results show that the change of acoustic frequency,acoustic pressure amplitude,and the number of bubbles in cluster have an effect not only on temperature and the quantity of oxidants inside the bubble,but also on the degradation types of pollutants,which provides a guidance in improving the sonochemical degradation of organic pollutants.

Quantum phase transitions of fermionic atoms in an anisotropic triangular optical lattice

The effect of anisotropy caused by a confining potential on the properties of fermionic cold atoms in a triangular optical lattice is systematically investigated by using the dynamical cluster approximation combined with the continuous time quantum Monte-Carlo algorithm. The quantum phase diagrams which reflect the temperature-interaction relation and the competition between the anisotropic parameter and the interaction are presented with full consideration of the anisotropy of the system. Our results show that the system undergoes a transition from Fermi liquid to Mott insulator when the repulsive interaction reaches a critical value. The Kondo effect also can be observed in this system and the pseudogap is suppressed at low temperatures due to the Kondo effect. A feasible experiment protocol to observe these phenomena in an anisotropic triangular optical lattice with cold atoms is proposed, in which the hopping terms are closely related to the lattice confining potential and the atomic interaction can be adjusted via the Feshbach resonance.

The relaxation of galaxy clusters at redshift z=0 in IllustrisTNG simulation

We study the dynamical states of the 30 most massive galaxy clusters in the TNG100 simulation at redshift z = 0 using three types of tracers: stars, dark matter particles and satellite galaxies. If the massive galaxy cluster is spherically symmetric and relaxed, we can obtain the underlying total mass distribution accurately from its dynamical tracers using the spherical Jeans equations. Although the three tracers of clusters have very different number densities, velocity dispersions and anisotropies, they still trace the same total mass profile. We obtain the total mass profiles of clusters using these tracers separately and compare them with the true mass distributions. We find that:(1) the kinematics of dark matter trace the total mass of all clusters well and the mass inferred from dark matter are generally consistent with the true mass profiles with relative deviations smaller than ~ 25% at all radii;(2) stars in ~ 60% massive clusters are approaching equilibrium and the total mass of these clusters inferred from stars have relative deviations smaller than ~50% at all radii. Stellar substructures are rich and the mass inferred from stars tend to be over-estimated in the inner region;and(3) satellite galaxies are unrelaxed in the inner region and become more relaxed as the radius increases. The total mass inferred from satellites are under-estimated in all regions.

Kinematics of the Open Cluster System in the Galaxy

Absolute proper motions and radial velocities of 202 open clusters in the solar neighborhood, which can be used as tracers of the Galactic disk, are used to investigate the kinematics of the Galaxy in the solar vicinity, including the mean heliocentric velocity components （u1, u2, u3） of the open cluster system, the characteristic velocity dispersions （σ1,σ2,σ3）, Oort constants （A, B） and the large-scale radial motion parameters （C, D） of the Galaxy. The results derived from the observational data of proper motions and radial velocities of a subgroup of 117 thin disk young open clusters by means of a maximum likelihood algorithm are： （u1,u2,u3） = （-16.1 ± 1.0,-7.9 ±1.4,-10.4±1.5） km·s^-1, （σ1,σ2,σ3） = （17.0±0.7, 12.2±0.9, 8.0±1.3） km·S^-1, （A, B） = （14.8±1.0, - 13.0±2.7） km·s^-1 kpc^-1, and （C, D） = （1.5 ± 0.7, -1.2 ±1.5） km·s^-1 kpc^-1. A discussion on the results and comparisons with what was obtained by other authors is given.

The Long-term Survival Chances of Young Massive Star Clusters

We review the long-term survival chances of young massive star clusters （YMCs）, hallmarks of intense starburst episodes often associated with Violent galaxy interactions. We address the key question as to whether at least some of these YMCs can be considered protoglobular clusters （GCs）, in which case these would be expected to evolve into counterparts of the ubiquitous old GCs believed to be among the oldest galactic building blocks. In the absence of significant external perturbations, the key factor determining a cluster＇s long-term survival chances is the shape of its stellar initial mass function （IMF）. It is, however, not straightforward to assess the IMF shape in unresolved extragalactic YMCs. We discuss in detail the promise of using high-resolution spectroscopy to make progress towards this goal, as well as the numerous pitfalls associated with this approach. We also discuss the latest progress in worldwide efforts to better understand the evolution of entire cluster systems, the disruption processes they are affected by, and whether we can use recently gained insights to determine the nature of at least some of the YMCs observed in extragalactic starbursts as proto-GCs. We conclude that there is an increasing body of evidence that GC formation appears to be continuing until today; their long-term evolution crucially depends on their environmental conditions, however.

A Projection Pursuit Dynamic Cluster Model Based on a Memetic Algorithm

A Projection Pursuit Dynamic Cluster(PPDC) model optimized by Memetic Algorithm(MA) was proposed to solve the practical problems of nonlinearity and high dimensions of sample data, which appear in the context of evaluation or prediction in complex systems. Projection pursuit theory was used to determine the optimal projection direction; then dynamic clusters and minimal total distance within clusters(min TDc) were used to build a PPDC model. 17 agronomic traits of 19 tomato varieties were evaluated by a PPDC model. The projection direction was optimized by Simulated Annealing(SA) algorithm, Particle Swarm Optimization(PSO), and MA. A PPDC model,based on an MA, avoids the problem of parameter calibration in Projection Pursuit Cluster(PPC) models. Its final results can be output directly, making the cluster results objective and definite. The calculation results show that a PPDC model based on an MA can solve the practical difficulties of nonlinearity and high dimensionality of sample data.

A Cluster Dynamics Model for Accumulation of Helium in Tungsten under Helium Ions and Neutron Irradiation

A cluster dynamics model based on rate theory has been developed to describe the accumulation and diffusion processes of helium in tungsten under helium implantation alone or synergistic irradiationwith neutron,by involving different types of objects,adopting up-to-date parameters and complex reaction processes as well as considering the diffusion process along with depth.The calculated results under different conditions are in good agreement with experiments much well.The model describes the behavior of helium in tungsten within 2D space of defect type/size and depth on different ions incident conditions(energies and fluences)and material conditions(system temperature and existent sinks),by including the synergistic effect of helium-neutron irradiations and the influence of inherent sinks(dislocation lines and grain boundaries).The model,coded as IRadMat,would be universally applicable to the evolution of defects for ions/neutron irradiated on plasma-facing materials.

Fuzzy Genetic Sharing for Dynamic Optimization

Recently,genetic algorithms(GAs) have been applied to multi-modal dynamic optimization(MDO).In this kind of optimization,an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing optimum.Our fuzzy genetic sharing(FGS) approach is based on a novel genetic algorithm with dynamic niche sharing(GADNS).FGS finds the optimal solutions,while maintaining the diversity of the population.For this,FGS uses several strategies.First,an unsupervised fuzzy clustering method is used to track multiple optima and perform GADNS.Second,a modified tournament selection is used to control selection pressure.Third,a novel mutation with an adaptive mutation rate is used to locate unexplored search areas.The effectiveness of FGS in dynamic environments is demonstrated using the generalized dynamic benchmark generator(GDBG).

Systematic energy-balanced cooperative transmission scheme in wireless sensor networks

Energy efficiency is a critical issue in wireless sensor networks （WSNs）. In order to minimize energy consumption and balance energy dissipation throughout the whole network, a systematic energy-balanced cooperative transmission scheme in WSNs is proposed in this paper. This scheme studies energy efficiency in systematic view. For three main steps, namely nodes clustering, data aggregation and cooperative transmission, corresponding measures are put forward to save energy. These measures are well designed and tightly coupled to achieve optimal performance. A half-controlled dynamic clustering method is proposed to avoid concentrated distribution of cluster heads caused by selecting cluster heads randomly and to get high spatial correlation between cluster nodes. Based on clusters built, data aggregation, with the adoption of dynamic data compression, is performed by cluster heads to get better use of data correlation. Cooperative multiple input multiple output （CMIMO） with an energy-balanced cooperative cluster heads selection method is proposed to transmit data to sink node. System model of this scheme is also given in this paper. And simulation results show that, compared with other traditional schemes, the proposed scheme can efficiently distribute the energy dissipation evenly throughout the network and achieve higher energy efficiency, which leads to longer network lifetime span. By adopting orthogonal space time block code （STBC）, the optimal number of the cooperative transmission nodes varying with the percentage of cluster heads is also concluded, which can help to improve energy efficiency by choosing the optimal number of cooperative nodes and making the most use of CMIMO.

High-Order Two-Dimension Cluster Competitive Activation Mechanisms Used for Performing Symbolic Logic Algorithms of Problem Solving

This paper presents a neural network approach, based on high-order two-dimension temporal and dynamically clustering competitive activation mecha-nisms, to implement parallel searching algorithm and many other symbolic logicalgorithms. This approach is superior in many respects to both the commonsequential algorithms of symbolic logic and the common neura.l network usedfor optimization problems. Simulations of problem solving examples prove theeffectiveness of the approach.

Generalized labeled multi-Bernoulli filter with signal features of unknown emitters

A novel algorithm that combines the generalized labeled multi-Bernoulli(GLMB) filter with signal features of the unknown emitter is proposed in this paper. In complex electromagnetic environments, emitter features(EFs) are often unknown and time-varying. Aiming at the unknown feature problem, we propose a method for identifying EFs based on dynamic clustering of data fields. Because EFs are time-varying and the probability distribution is unknown, an improved fuzzy C-means algorithm is proposed to calculate the correlation coefficients between the target and measurements, to approximate the EF likelihood function. On this basis, the EF likelihood function is integrated into the recursive GLMB filter process to obtain the new prediction and update equations.Simulation results show that the proposed method can improve the tracking performance of multiple targets,especially in heavy clutter environments.