Prediction and Output Estimation of Pattern Moving in Non-Newtonian Mechanical Systems Based on Probability Density Evolution

A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems,assuming that the system satisfies the generalized Lipschitz condition.As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics,the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables,which poses difficulties in predicting and estimating the system’s output.In this article,the temporal variation of the system is described by constructing pattern category variables,which are non-deterministic variables.Since pattern category variables have statistical attributes but not operational attributes,operational attributes are assigned to them by posterior probability density,and a method for analyzing their motion laws using probability density evolution is proposed.Furthermore,a data-driven form of pattern motion probabilistic density evolution prediction method is designed by combining pseudo partial derivative(PPD),achieving prediction of the probability density satisfying the system’s output uncertainty.Based on this,the final prediction estimation of the system’s output value is realized by minimum variance unbiased estimation.Finally,a corresponding PPD estimation algorithm is designed using an extended state observer(ESO)to estimate the parameters to be estimated in the proposed prediction method.The effectiveness of the parameter estimation algorithm and prediction method is demonstrated through theoretical analysis,and the accuracy of the algorithm is verified by two numerical simulation examples.

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.

An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams

The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.

Spatio-temporal Pattern and Spatial Heterogeneity of Ecotones Based on Land Use Types of Southeastern Da Hinggan Mountains in China

Ecotones have received great attention due to its critical function in energy flux, species harbor, global carbon sequestration, and land-atmosphere interaction. This study investigated land use pattern and spatial heterogeneity of the ecotones among agricultural land, forest land, and grassland of the southeastern Da Hinggan Mountains in the northeastern China. The change of these delineated ecotones under different slopes and aridity conditions was analyzed by two landscape indices, edge density(ED) and core area percentage of landscape(CPL), to explore the inter-linkage between spatial structure of ecotones and socioeconomic development and land management. Specifically, the ecotones such as agriculture-forest(AF) ecotone, forest-grassland(FG) ecotone, and agriculture-forestgrassland(AFG) ecotone moved from the arid southeast to the humid northwest. The flat area with small slope is more edge-fragmented than the steep area since the ED decreases as the slope increases. The AF ecotone mostly found in the humid region is moving to more humid areas while the agriculture-grassland(AG) ecotone mostly found in the dry region is moving towards the drier region.

Directional tunings independent of orientation in the primary visual cortex of the cat

A family of moving ‘random-line' patterns was developed and used to study the directional tuning of 91 single units in cat primary visual cortex (V1). The results suggest that, in addition to the well-known orientation-dependent mechanism, there is also some kind of orientation-independent mechanism underlying the direction selectivity. The directional tuning of the neurons varies in accordance with the increase of orientation or non-orientation element in the stimulus.