Analysis of the Classification of Type-2 Area in the Zoning of Environmental Noise Function

<p>The zoning of environmental noise function is aimed to provide a good acoustic environment for people. With the acceleration of China’s urbanization, the zoning of environmental noise function has been widely conducted across the country, and many first-and second-tier cities have even made multiple adjustments on the original zoning. In addition, some third-and fourth-tier cities have also gradually implemented the zoning of environmental noise function. The method for determining the type-2 function area in the “Technical Specifications for Regionalizing Environmental Noise Function” (GB/T 15190-2014) is too general. In this paper, the classification of the type-2 area in different situations was discussed, so as to provide a certain reference for the zoning and adjustment of the environmental noise function areas in various places.</p>

Prime gradient noise

Procedural noise functions are fundamental tools in computer graphics used for synthesizing virtual geometry and texture patterns. Ideally, a procedural noise function should be compact, aperiodic, parameterized, and randomly accessible. Traditional lattice noise functions such as Perlin noise, however, exhibit periodicity due to the axial correlation induced while hashing the lattice vertices to the gradients. In this paper, we introduce a parameterized lattice noise called prime gradient noise (PGN) that minimizes discernible periodicity in the noise while enhancing the algorithmic efficiency. PGN utilizes prime gradients, a set of random unit vectors constructed from subsets of prime numbers plotted in polar coordinate system. To map axial indices of lattice vertices to prime gradients, PGN employs Szudzik pairing, a bijection F: ℕ2 → ℕ. Compositions of Szudzik pairing functions are used in higher dimensions. At the core of PGN is the ability to parameterize noise generation though prime sequence offsetting which facilitates the creation of fractal noise with varying levels of heterogeneity ranging from homogeneous to hybrid multifractals. A comparative spectral analysis of the proposed noise with other noises including lattice noises show that PGN significantly reduces axial correlation and hence, periodicity in the noise texture. We demonstrate the utility of the proposed noise function with several examples in procedural modeling, parameterized pattern synthesis, and solid texturing.

Parametric Optimization for the Design of Passenger Vehicle Suspension System with the Application of Genetic Algorithm

In this paper an improved suspension system of a four-wheel vehicle is designed to minimize the vehicle floor vibrations. A seven degree-of-freedom full car model of vehicle system is modelled using the linear approach and is excited under the uncertain road inputs approximated by the white noise function. Vehicle acceleration in bounce, pitch and roll along with suspension displacement are the multiple parameters blended into single objective function to be minimized through proper allocation of weightages to each sub-objective based on real implications. The modified suspension system with optimum parameters results in improvement in the dynamic characteristic. Computer simulation through MATLAB-Simulink is providing an approximate solution against expensive and time taking experimentation.

Intervention Modeling

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

Turing Bifurcation and Pattern Formation of Stochastic Reaction-Diffusion System

Noise is ubiquitous in a system and can induce some spontaneous pattern formations on a spatially homogeneous domain. In comparison to the Reaction-Diffusion System (RDS), Stochastic Reaction-Diffusion System (SRDS) is more complex and it is very difficult to deal with the noise function. In this paper, we have presented a method to solve it and obtained the conditions of how the Turing bifurcation and Hopf bifurcation arise through linear stability analysis of local equilibrium. In addition, we have developed the amplitude equation with a pair of wave vector by using Taylor series expansion, multiscaling, and further expansion in powers of small parameter. Our analysis facilitates finding regions of bifurcations and understanding the pattern formation mechanism of SRDS. Finally, the simulation shows that the analytical results agree with numerical simulation.

Research on the Inspection and Mark on the Defects of Printed Matter Based on Template Matching Algorithm

Defects inspection of printed matter that combines the image processing technology with printing principle is a kind of automatic detection method for high speed printing. In view of the displacement error, stains wrinkle, fuzzy words in the current printing industry, this paper has adopted template matching algorithm to compare the standard image and the detected image, followed by the neighborhood method to analyze, then extracted the defects and marked the defect location. This study has good anti-noise function and makes detection precision be controlled within 0.5mm×0.5mm.