The Analysis of Bases of Market Segmentation

This paper analyses the characters of different bases of market segmentation and classes the bases into four kinds： observable general bases, observable product-specific bases, unobservable general bases and unobservable product-specific bases. The result can be used in the future research of market segmentation.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

Rainfall Variability and Trends in West Africa

Rainfall variability associated with climate change has enormous impacts on ecosystems, agriculture and people in West Africa but few studies have been devoted to it. Monthly rainfall data from 1901 to 2013, provided by the Global Precipitation Climatology Center dataset, were analyzed using segmentation and empirical modal decomposition (EMD) methods to increase our knowledge on past and recent spatio-temporal rainfall trends and their impacts on the West African region. The results obtained showed that the peak of rainfall during the short rainy season is observed in September in Côte d’Ivoire, Ghana and Liberia. The temporal variability of this rainfall is marked by several breakpoints whose durations range from 2 to 70 years. The periods of change in the rainfall regime, characterized by the appearance of breakpoints, vary from one country to another and are of unequal duration. The main breakpoint appears after 1960. Periods of relative or normal increase or decrease in precipitation are observed before and after 1960. The long-term variability of this rainfall is characterized by a decrease in the amount of rainfall over all West African countries. The results of this study can be used as a tool to help raise awareness among populations for sustainable management of water resources in response to climate change and its adverse effects.

Predictive cruise control for heavy trucks based on slope information under cloud control system

With the advantage of fast calculation and map resources on cloud control system(CCS), cloud-based predictive cruise control(CPCC) for heavy trucks has great potential to improve energy efficiency, which is significant to achieve the goal of national carbon neutrality. However, most investigations focus on the on-board predictive cruise control(PCC) system,lack of research on CPCC architecture under CCS. Besides, the current PCC algorithms have the problems of a single control target and high computational complexity, which hinders the improvement of the control effect. In this paper, a layered architecture based on CCS is proposed to effectively address the realtime computing of CPCC system and the deployment of its algorithm on vehicle-cloud. In addition, based on the dynamic programming principle and the proposed road point segmentation method(RPSM), a PCC algorithm is designed to optimize the speed and gear of heavy trucks with slope information. Simulation results show that the CPCC system can adaptively control vehicle driving through the slope prediction, with fuel-saving rate of 6.17% in comparison with the constant cruise control. Also,compared with other similar algorithms, the PCC algorithm can make the engine operate more in the efficient zone by cooperatively optimizing the gear and speed. Moreover, the RPSM algorithm can reconfigure the road in advance, with a 91% roadpoint reduction rate, significantly reducing algorithm complexity.Therefore, this study has essential research significance for the economic driving of heavy trucks and the promotion of the CPCC system.

Three Dimensional Laser Point Cloud Slicing Method for Calculating Irregular Volume

Volume parameter is the basic content of a spatial body object morphology analysis.However,the challenge lies in the volume calculation of irregular objects.The point cloud slicing method proposed in this study effectively works in calculating the volume of the point cloud of the spatial object obtained through three-dimensional laser scanning(3DLS).In this method,a uniformly spaced sequent slicing process is first conducted in a specific direction on the point cloud of the spatial object obtained through 3DLS.A series of discrete point cloud slices corresponding to the point cloud bodies are then obtained.Subsequently,the outline boundary polygon of the point cloud slicing is searched one by one in accordance with the slicing sequence and areas of the polygon.The point cloud slice is also calculated.Finally,the individual point cloud section volume is calculated through the slicing areas and the adjacent slicing gap.Thus,the total volume of the scanned spatial object can be calculated by summing up the individual volumes.According to the results and analysis of the calculated examples,the slice-based volume-calculating method for the point cloud of irregular objects obtained through 3DLS is correct,concise in process,reliable in results,efficient in calculation methods,and controllable on accuracy.This method comes as a good solution to the volume calculation of irregular objects.

ANALYSIS OF DYNAMICAL BUCKLING AND POST BUCKLING FOR BEAMS BY FINITE SEGMENT METHOD

Based on the multi-rigid body discretization model, namely, finite segment model,a chain multi-rigid-body-hinge-spring system model of a beam was presented, then a nonlinear parametrically exacted vibration equation of multi-degrees of freedom system was established using the coordination transformation method, and its resonance fields were derived by the restriction parameter method, that is, the dynamical buckling analysis of the beam. Because the deformation of a beam is not restricted by the discrete model and dynamic equation, the post buckling analysis can be done in above math model. The numerical solutions of a few examples were obtained by direct integrated method, which shows that the mechanical and math model gotten is correct.

Analysis of normal human retinal vascular network architecture using multifractal geometry

AIM：To apply the multifractal analysis method as a quantitative approach to a comprehensive description of the microvascular network architecture of the normal human retina.METHODS：Fifty volunteers were enrolled in this study in the Ophthalmological Clinic of Cluj-Napoca,Romania,between January 2012 and January 2014. A set of 100 segmented and skeletonised human retinal images,corresponding to normal states of the retina were studied. An automatic unsupervised method for retinal vessel segmentation was applied before multifractal analysis. The multifractal analysis of digital retinal images was made with computer algorithms,applying the standard boxcounting method. Statistical analyses were performed using the Graph Pad In Stat software.RESULTS：The architecture of normal human retinal microvascular network was able to be described using the multifractal geometry. The average of generalized dimensions（D_q）for q=0,1,2,the width of the multifractal spectrum（Δα=α_（max）-α_（min））and the spectrum arms＇ heights difference（│Δf│）of the normal images were expressed as mean±standard deviation（SD）：for segmented versions,D_0=1.7014±0.0057; D_1=1.6507±0.0058; D_2=1.5772±0.0059; Δα=0.92441±0.0085; │Δf│= 0.1453±0.0051; for skeletonised versions,D_0=1.6303±0.0051; D_1=1.6012±0.0059; D_2=1.5531± 0.0058; Δα=0.65032±0.0162; │Δf│= 0.0238±0.0161. The average of generalized dimensions（D_q）for q=0,1,2,the width of the multifractal spectrum（Δα）and the spectrum arms＇ heights difference（│Δf│）of the segmented versions was slightly greater than the skeletonised versions.CONCLUSION：The multifractal analysis of fundus photographs may be used as a quantitative parameter for the evaluation of the complex three-dimensional structure of the retinal microvasculature as a potential marker for early detection of topological changes associated with retinal diseases.

Plotting the Sequential Rock Remote Sensing Information by Optimization Dichotomy Method

The sequential rock remote sensing information is a group of rocks that are correlative in space or in space and time. For the sake of plottiug them, someone had brought forward the optimization segn.entotion metkod. We have ased this method to plot the sequential rock remote sensing information at tbe remote sensing hyperspetral test field of Daqing mountain, Inner Mongolia Autonomous Region, China, and found some disadvantages of this method. Therefore, we put forward the optimization dichotomy to plot them, and get better results. Finally we make a conclusion.

THE BENDING OF THIN RECTANGULAR PLATES WITH MIXED SUPPORTED SEGMENTS OF STRAIGHT EDGES

In this paper, the exact analytical solution of the rectangular plate having simplysupported segments mixed with free segments of straight edges are first given by means of the method of reciprocal theorem.By comparison .we calculate the same question by finite element method.Thecomparison shows that the analytical solution is correct.

FLEXIBLE MULTIBODY SYSTEM DYNAMICS-FINITE SEGMENT METHOD

The principle and method of flexible multibody system dynamics is presented. The dynamic equation have been developed by means of Huston's method based on Kane's equation. In which the flexible members with general cross-section characters were divided into finite segment models under the assumption of small strain. In order to decrease the degrees of freedom of the system and increase the efficiency of numerical calculation. the mode transformation has been introduced. A typical example is presented. and the foregoing method has been perfectly verified.

Multiscale Hessian filter-based segmentation and quantification method for photoacoustic microangiography

The appearanee of blood vessels is an important biomarker to distinguish diseased from healthy tissues in several fields of medical applications. Photoacoustie microangiography has the advantage of directly visualizing blood vessel networks within mierocireulatory tissue. Usually these images are interpreted qualitatively. However, a quantitative analysis is needed to better describe the characteristics of the blood vessels. This Letter addresses this problem by leveraging an efficient multiscale Hessian filter-based segmentation method, and four measure- ment parameters are acquired. The feasibility of our approach is demonstrated on experimental data and we expect the proposed method to be beneficial for several microcireulatory disease studies.

Recognition results classification and post-processing methods for painted characters on billet surface

Automatic identification of characters marked on billets is very important for steelworks to achieve manu- facturing and logistics informatization management. Due to the presence of adhesions, fractures, blurs, and other problems in characters painted on billets, character recognition accuracy with machine vision is relatively low, and hardly meets practical application requirements. To make the character recognition results more reliable and accu- rate, an identification results classification and post-pro- cessing method has been proposed in this paper. By analyzing issues in the image segmentation and recognition stage, the recognition result classification model, based on character encoding rules and recognition confidence, is built, and the character recognition results can be classified as correct, suspect, or wrong. In the post-processing stage, a human-machine-cooperation mechanism with a post- processing interface is designed to eliminate error infor- mation in suspect and wrong types. The system was developed and experiments conducted with images acquired in an iron and steel factory. The results show the character recognition accuracy to be approximately 89% using the character recognizer. However, this result cannot be directly applied in information management systems. With the proposed post-processing method, a human worker will query the suspect and wrong results classified by the system, determine whether the result is correct or wrong, and then, correct the wrong result through the post-processing interface. Using this method, the character recognition accuracy ultimately improves to 99.4%. Thus, the results will be more reliable applied in a practical system.

Geometric modeling of underground ferromagnetic pipelines for magnetic dipole reconstruction-based magnetic anomaly detection

To aid the magnetic anomaly detection(MAD)of underground ferromagnetic pipelines,this paper proposes a geometric modeling method based on the magnetic dipole reconstruction method(MDRM).First,the numerical modeling of basic pipe components such as straight sections,bends and elbows,and tee joints are discussed and the relevant mathematical formulations for these components are derived.Next,after analyzing the function of MDRM and various element division strategies,the sectional division and blocked division methods are introduced and applied to the appropriate pipeline components to determine the volume and center coordinates of each element,establishing the general models for the three typical pipeline components considered.The resulting volume and center coordinates of each component are the fundamental parameters for determining the MAD forwarding of underground ferromagnetic pipelines using the MDRM.Finally,based on the combination and transformation of the basic pipeline components considered,the visualized geometric models of typical pipeline layouts including parallel pipelines,pipelines with elbows,and a pipeline with a tee joint are constructed.The results demonstrate the feasibility of the proposed method of geometric modeling for the MDRM,which can be further applied to the finite element modeling of these and other components when analyzing MAD data.Furthermore,the models with output parameters proposed in this paper establish a foundation for the inversion of MAD.