STRUCTURE OPTIMIZATION STRATEGY OF NORMALIZED RBF NETWORKS

Aimed at studying normali zed radial basis function network (NRBFN), this paper introduces the subtractiv e clustering based on a mountain function to construct the initial structure of NR BFN, adopts singular value decomposition (SVD) to analyze the relationship betwe en neural nodes of the hidden layer and singular values, cumulative contribution ratio, index vector, and optimizes the structure of NRBFN. Finally, simulation and performance comparison show that the algorithm is feasible and effective.

Chemical Constituents of Angelica sinensis

Aim To investigate the active constituents responsible for thepharmacological activities of Angelica sinensis (Oliv) Diels. Methods Chromatography was used toisolate chemical components, and spectroscopy was used to identify their structures. Results Sevencompounds were isolated and their structures were identified as ferulic acid (1), conife-rylferukte(2) , bis (2-ethylhexyl) phthalate (3), dibutyl phthalate (4), lignoceric acid (5), palmitic acid(6), and Z-6, 7-cis-dihydroxyligustilide (7) Conclusion Bis (2-ethylhexyl) phthalate and dibutylphthalate were obtained from Angelica sinensis for the first time.

Study on size-dependent bending behavior of axially functionally graded microbeams via nonlocal strain gradient theory

Based on the nonlocal strain gradient theory(NSGT),the static bending behaviors of an axially functionally graded(AFG)Bernoulli-Euler microbeam subjected to concentrated and distributed loads are studied.The material property of the AFG microbeam changes continuously along the longitudinal direction.On the basis of the minimum potential energy principle,the equations of motion and associated classical and non-classical boundary conditions are derived.Then,Galerkin’s weighted residual method in conjunction with the normalization technique are utilized to solve the governing differential equations.The transverse deformations of the AFG microbeam suffering the sinusoidal distributed load within the framework of NSGT,nonlocal elasticity theory(NET),strain gradient theory(SGT)and classical elasticity theory(CET)are compared.It is observed that the bending flexibility of the microbeam decreases with the increase in the ratio of the material length scale parameter to the beam height.However,the bending flexibility increases with the increase in the material nonlocal parameter.The functionally graded parameter plays an important role in controlling the transverse deformation.This study provides a theoretical basis and a technical reference for the design and analysis of AFG micro-beams in the related regions.

Application of well log normalization in coalfield seismic inversion

During the process of coal prospecting and exploration, different measurement time, different logging instruments and series can lead to systematic errors in well logs. Accordingly, all logging curves need to be normalized in the mining area. By studying well-logging normalization methods, and focusing on the characteristics of the coalfield, the frequency histogram method was used in accordance with the condition of the Guqiao Coal Mine. In this way, the density and sonic velocity at marker bed in the non-key well were made to close to those in the key well, and were eventually equal. Well log normalization was completed when this method was applied to the entire logging curves. The results show that the scales of logging data were unified by normalizing coal logging curves, and the logging data were consistent with wave impedance inversion data. A satisfactory inversion effect was obtained.

Determination of constituents of essential oil from Angelica sinensis by gas chromatographymass spectrometry

Gas chromatographymass spectrometry (GC-MS) coupled with chemometric resolution upon two- dimensional data was employed to analyze the constituents of essential oils of Angelica sinensis. Constituents in essential oils of Angelica sinensis root were identified by GC-MS with the help of subwindow factor analysis (SFA) method resolving two-dimensional original data into mass spectra and chromatograms. 76 of 97 separated constituents in essential oil of Angelica sinensis root were identified and quantified, and they account for about 91.36% of the total content. The results show that ligustilide, butylene phthalide, 2-methoxy-4-vinylphenol, carvacrol, allo-ocimene,2,6,6-trimethylbicyclo-[3,1,1]hept-2-ene are the main constituents in essential oil of Angelica sinensis root.

Normalization method for asphalt mixture fatigue equation under different loading frequencies

In order to analyze the effect of different loading frequencies on the fatigue performance for asphalt mixture,the changing law of asphalt mixture strengths with loading speed was revealed by strength tests under different loading speeds.Fatigue equations of asphalt mixtures based on the nominal stress ratio and real stress ratio were established using fatigue tests under different loading frequencies.It was revealed that the strength of the asphalt mixture is affected by the loading speed greatly.It was also discovered that the fatigue equation based on the nominal stress ratio will change with the change of the fatigue loading speed.There is no uniqueness.But the fatigue equation based on the real stress ratio doesn't change with the loading frequency.It has the uniqueness.The results indicate the fatigue equation based on the real stress ratio can realize the normalization of the asphalt mixture fatigue equation under different loading frequencies.It can greatly benefit the analysis of the fatigue characteristics under different vehicle speeds for asphalt pavement.

Identifying Scale-location Specific Control on Vegetation Distribution in Mountain-hill Region

vegetation continuous The scale-location specific control on distribution was investigated through wavelet transforms approaches in subtropical mountain-hill region, Fujian, China. The Normalized Difference Vegetation Index （NDVI） was calculated as an indicator of vegetation greenness using Chinese Environmental Disaster Reduction Satellite images along latitudinal and longitudinal transects. Four scales of variations were identified from the local wavelet spectrum of NDVI, with much stronger wavelet variances observed at larger scales. The characteristic scale of vegetation distribution within mountainous and hilly regions in Southeast China was around 20 km. Significantly strong wavelet coherency was generally examined in regions with very diverse topography, typically characterized as small mountains and hills fractured by rivers and residents. The continuous wavelet based approaches provided valuable insight on the hierarchical structure and its corresponding characteristic scales of ecosystems, which might be applied in defining proper levels in multilevel models and optimal bandwidths in Geographically Weighted Regression.

Dynamic collision avoidance for cooperative fixed-wing UAV swarm based on normalized artificial potential field optimization

Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.

Several boundary identification methods of gravity data and application in Vientiane of Laos area

The authors studied the potential field boundary identification of the new technology in order to find out the possible fractures or contact zones using the following methods such as tilt derivative,horizontal derivative of tilt derivative,normalized standard deviation and normalized differential method. Combined with Euler deconvolution and small subdomain filtering,the actual data processing results show that these methods are all able to identify wider range extending fractures and obtain abundant geological information. The horizontal derivative of tilt derivative and normalized differential method have a better resolution for the small cutting fractures and lacunae in the studied area. They provide a reliable basis for study of the cutting relationship between fractures.

3D prestack reverse time migration of ground penetrating radar data based on the normalized correlation imaging condition

The reverse time migration(RTM)of ground penetrating radar(GPR)is usually implemented in its two-dimensional(2D)form,due to huge computational cost.However,2D RTM algorithm is difficult to focus the scattering signal and produce a high precision subsurface image when the object is buried in a complicated subsurface environment.To better handle the multi-off set GPR data,we propose a three-dimensional(3D)prestack RTM algorithm.The high-order fi nite diff erence time domian(FDTD)method,with the accuracy of eighth-order in space and second-order in time,is applied to simulate the forward and backward extrapolation electromagnetic fi elds.In addition,we use the normalized correlation imaging condition to obtain pre-stack RTM result and the Laplace fi lter to suppress the low frequency noise generated during the correlation process.The numerical test of 3D simulated GPR data demonstrated that 3D RTM image shows excellent coincidence with the true model.Compared with 2D RTM image,the 3D RTM image can more clearly and accurately refl ect the 3D spatial distribution of the target,and the resolution of the imaging results is far better.Furthermore,the application of observed GPR data further validates the eff ectiveness of the proposed 3D GPR RTM algorithm,and its fi nal image can more reliably guide the subsequent interpretation.

Did Ecological Engineering Projects Have a Significant Effect on Large-scale Vegetation Restoration in Beijing-Tianjin Sand Source Region, China？ A Remote Sensing Approach

Aiming for the restoration of degraded ecosystems, many ecological engineering projects have been implemented around the world. This study investigates the ecological engineering project effectiveness on vegetation restoration in the Beijing-Tianjin Sand Source Region(BTSSR) from 2000 to 2010 based on the rain use efficiency(RUE) trend in relation to the land cover. More than half of the BTSSR experienced a vegetation productivity increase from 2000 to 2010, with the increasing intensity being sensitive to the indicators chosen. A clear tendency towards smaller increasing areas was shown when using the net primary productivity(NPP, 51.30%) instead of the accumulated normalized difference vegetation index(59.30%). The short-term variation in the precipitation and intra-seasonal precipitation distribution had a great impact on the remote sensing-based vegetation productivity. However, the residual trends method(RESTREND) effectively eliminated this correlation, while incorporating the variance and skewness of the precipitation distribution increased the models′ ability to explain the vegetation productivity variation. The RUE combined with land cover dynamics was valid for the effectiveness assessment of the ecological engineering projects on vegetation restoration. Particularly, the result based on growing season accumulated normalized difference vegetation index(ΣNDVI) residuals was the most effective, showing that 47.39% of the BTSSR experienced vegetation restoration from 2000 to 2010. The effectiveness of the ecological engineering projects differed for each subarea and was proportional to the strength of ecological engineering. The water erosion region dominated by woodland showed the best restoration, followed by the wind-water erosion crisscross regions, while the wind erosion regions dominated by grassland showed the worst effect. Seriously degraded regions still cover more area in the BTSSR than restored regions. Therefore, more future effort should be put in restoring degraded land.

Optimal control of cobalt crust seabedmining parameters based on simulated annealing genetic algorithm

Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting head, and the rotate speed） are chosen as the optimized parameters. According to the force on the cutting pick, the collecting size of the cobalt crust and bedrock and the optimized energy consumption of the collecting head, the optimized design model of collecting head is built. Taking two hundred groups seabed microtopography for grand in the range of depth displacement from 4.5 to 5.5 era, then making use of the improved simulated annealing genetic algorithm （SAGA）, the corresponding optimized result can be obtained. At the same time, in order to speed up the controlling of collecting head, the optimization results are analyzed using the regression analysis method, and the conclusion of the second parameter of the seabed microtopography is drawn.

A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression

A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der normal condition, then kernel regression is further used for quality prediction and estimation. If faults have oc-curred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can ef-fectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.

Modified Recursive Least Squares Algorithm with Variable Parameters and Resetting for Time-Varying System

Based on the idea of the set-membership identification, a modified recursive least squares algorithm with variable gain, variable forgetting factor and resetting is presented. The concept of the error tolerance level is proposed. The selection criteria of the error tolerance level are also given according to the min-max principle. The algorithm is particularly suitable for tracing time-varying systems and is similar in computational complexity to the standard recursive least squares algorithm. The superior performance of the algorithm is verified ma simulation studies on a dynamic fermentation process.

Statistical Reconstruction of the Antarctic Oscillation Index Based on Multiple Proxies

Based on multiple proxies from the Southern Hemisphere, an austral summer (December-January-February: DJF) Antarctic Oscillation Index (AAO) since 1500 A.D. was reconstructed with a focus on interannual to interdecadal variability (<50 a). By applying a multivariate regression method, the observational AAO-proxy relations were calibrated and cross-validated for the period of 1957 89. The regressions were employed to compute the DJF-AAO index for 1500 1956. To verify the results, the authors checked the explained variance (r 2 ), the reduction of error (RE), and the standard error (SE). Cross-validation was performed by applying a leave-one-out validation method. Over the entire reconstruction period, the mean values of r 2 , RE, and SE are 59.9%, 0.47, and 0.67, respectively. These statistics indicate that the DJF-AAO reconstruction is relatively skillful and reliable for the last ～460 years. The reconstructed AAO variations on the interannual and interdecadal timescales compare favorably with those of several shorter sea level pressure (SLP)-based AAO indices. The leading periods of the DJF-AAO index over the last 500 years are ～2.4, ～2.6, ～6.3, ～24.1, and ～37.6 years, all of which are significant at the 95% level as estimated by power spectral analysis.

Water bodies extraction from TM images

Aiming at the problems of high time-consuming, low accuracy and weak versatility of the existing methods of wa- ter extraction based on TM image, this paper combines principal component analysis （PCA） with the modified normalized difference water index （MNDWI） which was improved by XU Han-qiu to construct a false color composite image that could separate water from others easily. This method can realize the water extraction based on TM image by analyzing the spectral characteristics of water in this false color image and establishing a water extraction model. This paper also compares the effi- ciency of this method with MNDWI, （TM2 ＋ TM3） - （TM4 ＋ TM5） and new water index （NWI）, which were applied in the city and mountain of Taiyuan, respectively. The results show that the proposed method can extract water body from TM im- age more rapidly and efficiently and its accuracy is up to 94.03 %. In addition, this method does not require a manual selec- tion threshold, which meets the research reuuirement of high automaticm.

Fitting Generalized Additive Logistic Regression Model with GAM Procedure

In dealing with nonparametric regression the GAM procedure is the most versatile of several new procedures. The terminology behind this procedure is more flexible than traditional parametric modeling tools. It relaxes the usual assumptions of parametric model and enables us to uncover structure to establish the relationship between independent variables and dependent variable in exponential family that may not be obvious otherwise. In this paper, we discussed two methods of fitting generalized additive logistic regression model, one based on Newton Raphson method and another based on iterative weighted least square method for first and second order Taylor series expansion. The use of the GAM procedure with the specified set of weights, using local scoring algorithm, was applied to real life data sets. The cubic spline smoother is applied to the independent variables. Based on nonparametric regression and smoothing techniques, this procedure provides powerful tools for data analysis.

An imaging condition for reverse time migration based on wavefield decomposition

Reverse Time Migration(RTM) is a high precision imaging method of seismic wavefield at present,but low-frequency noises severely affect its imaging results.Thus one of most important aspect of RTM is to select the proper noise suppression method.The wavefield characteristics of the Poynting vector are analyzed and the upgoing,downgoing,leftgoing and rightgoing waves are decomposed using the Poynting vector of the acoustic wave equation.The normalized wavefield decomposition cross-correlation imaging condition is used to suppress low-frequency noises in RTM and improve the imaging precision.Numerical experiments using the Mamousi velocity model are performed and the results demonstrate that the upgoing,downgoing,leftgoing and rightgoing waves are well decomposed using the Poynting vector.Compared with the normalized cross-correlation imaging and Laplacian filtering method,the results indicate that the low-frequency noises are well suppressed by using the normalized wavefield decomposition cross-correlation imaging condition.

Self-normalized moderate deviations for independent random variables

Let X1,X2,... be a sequence of independent random variables （r.v.s） belonging to the domain of attraction of a normal or stable law. In this paper, we study moderate deviations for the self-normalized sum n X ∑^n_i=1Xi/Vm,p ,where Vn,p （∑^n_i=1｜Xi｜p）^1/p （P 〉 1）.Applications to the self-normalized law of the iteratedlogarithm, Studentized increments of partial sums, t-statistic, and weighted sum of independent and identically distributed （i.i.d.） r.v.s are considered.