基于三电平中心削波自关分析的钢琴音准测量

论文介绍三电平中心削波自关分析法原理、这种方法应用于钢琴音准检测遇到的困难及改进的方法。由此实现的钢琴音准计算机检测系统,能够对经过人工粗调的钢琴进行音准的自动检测并以表格形式给出钢琴上各键的音准偏差值,为钢琴的音准指标提供了定量的依据。

Spatial Autocorrelation Analysis on Regional Economic Disparity of Northeast Economic Region in China

Popular regional inequality indexes such as variation coefficient and Gini coefficient can only reveal overall inequality, and have limited ability in revealing spatial dependence or spatial agglomeration. Recently some methods of exploratory spatial data analysis such as spatial autocorrelation have provided effective tools to analyze spatial agglomeration and cluster, which can reveal the pattern of regional inequality. This article attempts to use spatial autocorrelation at county level to get refined spatial pattern of regional disparity in Chinese northeast economic region over 2000-2006 (2001 absent). The result indicates that the basic trend of regional economy is an increasing concentration of growth among counties in northeast economic region, and there are two geographical clusters of poorer counties including the counties in western Liaoning Province and adjacent counties in Inner Mongolia, poorer counties of Heihe, Qiqihar and Suihua in Heilongjiang Province. This article also reveals that we can use the methods of exploratory spatial data analysis as the supplementary analysis methods in regional economic analysis.

Aggregate Characteristics During Natural Revegetation on the Loess Plateau

Field investigations and laboratory analysis were conducted to study the characteristics of soil water-stable aggregates during vegetation rehabilitation in typical grassland soils of the hilly-gullied loess area. The relationship between water- stable aggregates and other soil properties was analyzed using canonical correlation analysis and principal component analysis. The results show that during the natural revegetation, the aggregates 〉 5 mm dominated and constituted between 50% and 80% of the total soil water-stable aggregates in most of the soil layers. The 2-5 mm aggregate class was the second main component. The mean value of water-stable aggregates 〉 5 mm within the 0-2 m soil profile under different plant communities decreased in the following order： Stipa grandis 〉 Stipa bungeana Trin. 〉 Artemisia sacrorum Ledeb. 〉 Thymus mongolicus Ronn. 〉 Hierochloe odorata （L.） Beauv. Clay, organic matter, and total N were the key factors that influenced the water stability of the aggregates. Total N and organic matter were the main factors that affected the water stability of the aggregates 〉 5 mm and 0.5-1 mm in size. The contents of Fe2O3, Al2O3, and physical clay （〈 0.01 mm） were the main factors which affected the water stability of the 1-2 and 0.25-0.5 mm aggregates.

Application of Integration of Spatial Statistical Analysis with GIS to Regional Economic Analysis

This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of global Moran Coefficient, Local Moran and Local Geary. Furthermore, a user-friendly statistical module, combining spatial statistical analysis methods with GIS visual techniques, is developed in Arcview using Avenue. An example is also given to show the usefulness of this module in identifying and quantifying the underlying spatial association patterns between economic units.

Correlation analysis-based image segmentation approach for automatic agriculture vehicle

It is important to segment image correctly to extract guidance information for automatic agriculture vehicle. If we can make the computer know where the crops are, we can extract the guidance line easily. Images were divided into some rec-tangle small windows, then a pair of 1-D arrays was constructed in each small windows. The correlation coefficients of every small window constructed the features to segment images. The results showed that correlation analysis is a potential approach for processing complex farmland for guidance system, and more correlation analysis methods must be researched.

Analytical Hopf Bifurcation and Stability Analysis of T System

Complex dynamics are studied in the T system, a three-dimensional autonomous nonlinear system. In particular, we perform an extended Hopf bifurcation analysis of the system. The periodic orbit immediately following the Hopf bifurcation is constructed analytically for the T system using the method of multiple scales, and the stability of such orbits is analyzed. Such analytical results complement the numerical results present in the literature. The analytical results in the post-bifurcation regime are verified and extended via numerical simulations, as well as by the use of standard power spectra, autocorrelation functions, and fractal dimensions diagnostics. We find that the T system exhibits interesting behaviors in many parameter regimes.

Characterization of a novel developmentally retarded mutant (drm1) associated with the autonomous flowering pathway in Arabidopsis

A developmentally retarded mutant (drm1) was identified from ethyl methanesulfonate (EMS)-mutagenized M2 seedsin Columbia (Col-0) genetic background. The drm1 flowers 109 d after sowing, with a whole life cycle of about 160 d.It also shows a pleiotropic phenotype, e.g., slow germination and lower germination rate, lower growth rate, curlingleaves and abnormal floral organs. The drm1 mutation was a single recessive nuclear mutation, which was mapped tothe bottom of chromosome 5 and located within a region of 20-30 kb around MXK3.1. There have been no mutantswith similar phenotypes reported in the literature, suggesting that DRM1 is a novel flowering promoting locus. Thefindings that the drm1 flowered lately under all photoperiod conditions and its late flowering phenotype was significantlyrestored by vernalization treatment suggest that the drm1 is a typical late flowering mutant and most likely associatedwith the autonomous flowering pathway. The conclusion was further confirmed by the revelation that the transcriptlevel of FLC was constantly upregulated in the drm1 at all the developmental phases examined, except for a very earlystage. Moreover, the transcript levels of two other important repressors, EMF and TFL1, were also upregulated in thedrm1, implying that the two repressors, along with FLC, seems to act in parallel pathways in the drm1 to regulateflowering as well as other aspects of floral development in a negatively additive way. This helps to explain why the drm1exhibits a much more severe late-flowering phenotype than most late-flowering mutants reported. It also implies that theDRM1 might act upstream of these repressors.

What Is the Role of FDI in Environmental Pollution？---Evidence ti＇om China

This paper analyzes the distribution patterns and spatial dynamic transitions of foreign direct investment （FDI） and pollution from 2000 to 2009 in China＇s provinces by using the comprehensive pollution index （CEPI） and exploratory spatial data analysis. Findings suggest that FDI as well as environmental pollution in our provinces exists an obvious spatial autocorrelation, both of them have remarkable characteristics of path dependence and form different accumulation areas. Currently, the accumulations of highlevel FDI correspond to low-level environmental pollution, while the accumulations of low-level FDI are associated with high-level environmental pollution. Furthermore, the authors have empirically analyzed the impact of FDI on China＇s environmental pollution by spatial error model （SEM） and spatial lag model （SLM） respeetively. Findings suggest that the geographical clustering of FDI has a positive impact on China＇s environment, in general, ＂Pollution Haven Hypothesis＂ is invalid in China. In addition, there are remarkable differences in the impact of FDI on environmental pollution due to different sources, the foreign capital from offshore financial centers has significantly alleviated pollution in China while that from developed countries in East Asia and the West has played an insignificant role in environmental pollution.

Estimation of Number Of Small Cattle Through ARIMA Models in Turkey

In this study, the number of sheep and goats in Turkey were analysed by time series analysis method, and the number of great cattle for next years predicted through the most appropriate time series model.Time series was formed using the data on the number of sheep and goats belonging to the period between 1930 and 2014 in Turkey It was determined through autocorrelation function graphic that the series weren＇t stationary at first, but they became stationary after their first difference were calculated. A stagnancy test was performed through extended Dickey-Fuller test. So as to determine the suitability of the model, it was reviewed if autocorrelation and partial autocorrelation graphs were white noise series and also the results of Box-Ljung test were reviwed. Through the ＂tested models, the model estimations, of which parameter estimates were significant and Akaike information criterion （AIC） was the smallest, were performed. The most appropriate model in terms of both the number of sheep and goats is first-level integrated moving average model stated as ARIMA（0,1,1）. In this model, it was estimated that there would be an increase in the number of sheep and goats in Turkey between the years of 2015 and 2020, however, the increase in the number of sheep would be more than the increase in the number of goats.

Spatio-temporal evolution of population and urbanization in the countries along the Belt and Road 1950–2050

This paper uses data for the period 1950-2050 compiled by the United Nations Population Division together with methods including spatial autocorrelation analysis, hie- rarchical cluster analysis and the standard deviational ellipse, to analyze the spatio-temporal evolution of population and urbanization in the 75 countries located along the routes of the Silk Road Economic Belt and the 21st-century Maritime Silk Road, to identify future popula- tion growth and urbanization hotspots. The results reveal the following： First, in 2015, the majority of Belt and Road countries in Europe, South Asia and Southeast Asia had high population densities, whereas most countries in Central Asia, North Africa and West Asia, as well as Russia and Mongolia, had low population densities; the majority of countries in South Asia, Southeast Asia, Central Asia, West Asia and North Africa had rapid population growth, whereas many countries in Europe had negative population growth; and five Belt and Road countries are in the initial stage of urbanization, 44 countries are in the acceleration stage of urbanization, and 26 are in the terminal stage of urbanization. Second, in the century from 1950 to 2050, the mean center of the study area＇s population is consistently located in the border region between India and China. Prior to 2000, the trajectory of the mean center was from northwest to southeast, but from 2000 it is on a southward trajectory, as the population of the study area becomes more concentrated. Future population growth hotspots are predicted to be in South Asia, West Asia and Southeast Asia, and hotspot countries for the period 2015-2030 include India, China, Pakistan and Indonesia, though China will move into nega- tive population growth after 2030. Third, the overall urban population of Belt and Road coun- tries increased from 22% in 1950 to 49% in 2015, and it is expected to gradually catch up with the world average, reaching 64% in 2050. The different levels of urbanization in different countries display significant spatial dependency, and in the hundred-year period under con-sideration, this dependency increases before eventually weakening. Fourth, between 2015 and 2030, urban population hotspots will include Thailand, China, Laos and Albania, while Kuwait, Cyprus, Qatar and Estonia will be urban ＂coldspots.＂ Fifth, there were 293 cities with populations over 1 million located along the Belt and Road in 2015, but that number Js ex- pected to increase to 377 by 2030. Of those, 43 will be in China, with many of the others located in India, Indonesia and the eastern Mediterranean.

Semi-urbanization and evolving patterns of urbanization in China: Insights from the 2000 to 2010 national censuses

Based on the prefecture-level data of the 2000 and 2010 national censuses, the spatial evolution of China's semi-urbanization is analyzed in this study. The stages of urbanization are re-examined by considering semi-urbanization. Nine types of urban development are presented according to the relations between semi-urbanization and urbanization, and China's urbanization is divided into five stages, namely, high incoordination, incoordination, low coordination, coordination, and high coordination. Results show that China's semi-urbanization rate varies significantly from one area to another; its order in 2010 from the highest to the lowest value was as follows: east, middle, west, and northeast. Urbanization and semi-urbanization rates in inland cities increase much faster than those in coastal cities. In addition, semi-urbanization displays a spatial pattern similar to that of urbanization across China, with the sole exception of the northeastern region. Through a spatial autocorrelation analysis, the spatial concentration of semi-urbanization is determined to be increasing. High-value concentration areas are expanding in the coastal east, whereas low-value concentration areas are growing in the northeast. Lastly, the evolution of China's urbanization model suggests a weakening trend of coordination between urbanization and semi-urbanization over the studied decade. Semi-urbanization can be viewed as a special production of China's hukou system, which restricts the permanent settlement of migrants in cities. As such, China's semi-urbanization trend is expected to exhibit a reversed U-shaped pattern as urbanization and citizenization develop.

COMPLEX SYSTEM ANALYSIS OF MARKET RETURN PERCOLATION MODEL ON SIERPINSKI CARPET LATTICE FRACTAL

This paper investigates the statistical behaviors of fluctuations of price changes in a stock market.The Sierpinski carpet lattice fractal and the percolation system are applied to develop a new random stock price for the financial market.The Sierpinski carpet is an infinitely ramified fractal and the percolation theory is usually used to describe the behavior of connected clusters in a random graph.The authors investigate and analyze the statistical behaviors of returns of the price model by some analysis methods,including multifractal analysis,autocorrelation analysis,scaled return interval analysis.Moreover,the authors consider the daily returns of Shanghai Stock Exchange Composite Index,and the comparisons of return behaviors between the actual data and the simulation data are exhibited.

A novel posture alignment system for aircraft wing assembly

A novel 6-degree of freedom (DOF) posture alignment system, based on 3-DOF positioners, is presented for the assembly of aircraft wings. Each positioner is connected with the wing through a rotational and adsorptive half-ball shaped end-effector, and the positioners together with the wing are considered as a 3-PPPS (P denotes a prismatic joint and S denotes a spherical joint) redundantly actuated parallel mechanism. The kinematic model of this system is established and a trajectory planning method is introduced. A complete analysis of inverse dynamics is carried out with the Newton-Euler algorithm, which is used to find the desired actuating torque in the design and path planning phase. Simulation analysis of the displacement and actuating torque of each joint of the positioners based on inverse kinematics and dynamics is conducted, and the results show that the system is feasible for the posture alignment of aircraft wings.

Spatial Linear Combination Forecasting Model and Its Application

The accuracy of spatial forecasting is close relation to the selection of spatial forecasting model. Each model from special aspects using special spatial data has its own advantage or disadvantage. A more accurate spatial forecasting model can be obtained by a linear combination of some models. In this study, first-order spatial autoregressive （SAR（1）） model, Kriging algorithm interpolation （KAI） model and back-propagation neural network （BPNN） model are established by using cross-section data or time series data. A spatial linear combination forecasting （SLCF） model is obtained by the combination models mentioned above. An empirical research by these models is carried out with forecasting some areas＇ GDP per capita in Fujian, 2003. It is found that the best one is the SLCF model.

DEPENDENCE ANALYSIS OF REGRESSION MODELS IN TIME SERIES

In this paper, the relative dependence of a linear regression model is studied. In particular, the dependence of autoregressive models in time series are investigated. It is shown that for the first-order non-stationary autoregressive model and the random walk with trend and drift model, the dependence between two states decreases with lag. Some numerical examples are presented as well.