Applied Mechanics and Materials Vols. 333-335

Abstract: In this paper, a novel combined leakage CMA and direct decision (DD) adaptive algorithm using automatic iteration step adaptive blind equalizer is presented. In contrast to known CMA algorithm, the novel algorithm can approach the performance of mean-square error and fast convergent rate. For adaption of filters, efficient automatic iteration step and DD algorithm is present. The novel algorithm is lower computational complexity with O(N) and N is the filter order. Furthermore, the relation of novel algorithm to CMA algorithm is investigated. Finally, it is shown that proposed combined LCMA-DD algorithm is robust against amplitude and phase offset with fast convergence and mean-square error.

Abstract: The best itinerary problem has been a heat topic for several decades. Among those sophisticated methods used for choosing paths, Dijkstra algorithm is a simple but powerful method. However, Dijkstra algorithm is restricted because of 3 reasons. Firstly, the weight of each path must be a constant value. Secondly, the algorithm only represents the best path, disregards the second and third best paths. Thirdly, the weights only represent a single variable, which cannot be used to represent two different variables simultaneously. In this paper, we use a Filtering Algorithm based on Lagrange relaxation method and ordinal selection to overcome these weaknesses. In our OLR Dijkstra algorithm, strategy set of choosing paths shows a strong stability and reliability facing different probabilities situations. Only 13.2% diversity degree was found when path efficiency varied from 100% to 11.1%. Smallest time complexity of the OLR Dijkstra algorithm is 2 times than normal Dijsktra, which is .

Abstract: In order to increase success rate and production of acid-fractured wells in Kazakhstan R oilfield, this paper compared various well-selection mathematic methods. Artificial neural network was chosen as a better way to predict production rate and finally choose target wells. Besides, some problems of this algorithm, such as normalization of input data, infection of order, were optimized to make it possible to be realized. With realistic data, sample database was set up and predictable model was also gotten. Its proven that the predicted results are very close to realistic data. Also, we analyzed the sensitivity of different parameters, and some useful conclusions were given: acid-fracturing is more effective for low and medium permeability reservoirs; if each layer could be treated separately, wells with large permeability differential will be better choice for acid-fracturing; bottomhole pressure is not the higher the better.

Abstract: A proper $k$-edge coloring of a graph $G$ is an assignment of $k$ colors, $1,2,\cdots,k$, to edges of $G$. For a proper edge coloring $f$ of $G$ and any vertex $x$ of $G$, we use $S(x)$ denote the set of thecolors assigned to the edges incident to $x$. If for any two adjacent vertices $u$ and $v$ of $G$, we have $S(u)\neq S(v)$,then $f$ is called the adjacent vertex distinguishing proper edge coloring of $G$ (or AVDPEC of $G$ in brief). The minimum number of colors required in an AVDPEC of $G$ is called the adjacent vertex distinguishing proper edge chromatic number of $G$, denoted by $\chi^{'}_{\mathrm{a}}(G)$. In this paper, adjacent vertex distinguishing proper edge chromatic numbers of several classes of complete 5-partite graphs are obtained.

Abstract: A new method which is least squares fitting combined with improved BP neural network based on LM algorithm was put forward. In order to overcome the weak points that easy to fall into local minimum, slow convergence of traditional BP neural network, we use LM algorithm to improve it. Least-squares curve fitting can be used to reflect the overall trend of the data changes, so we adopted least squares method firstly to make curve fitting for sample data firstly. Then, we corrected the fitting error by the improved BP Neural Network which has the advantages that reflecting external factors. Finally, the fitted values and error correction values were added to get oilfield production forecast. The results show that the oilfield production forecast error is significantly lower than the single curve fitting, BP Neural Network or LMBP.

Abstract: A data processing method based on a mesh search algorithm for elliptical profile error evaluation is proposed. First ellipse focuses of the least-square are set as reference points, according to a certain rules, a series of grid points are arranged and supposed as hypothetical ideal ellipse focuses respectively , then the distances of supposed ideal ellipse to the measuring points are calculated repeatedly. The minimum circumscribed ellipse and the maximum inscribed ellipse are constructed by comparison and judgment, and the corresponding value of ellipse profile error is obtained. Furthermore, the principle of the grid search algorithm and the process of ellipse profile error evaluation by using the minimum circumscribed ellipse method and the maximum inscribed ellipse method are stated . The results of examples show that the ellipse profile error can be evaluated accurately and effectively by this algorithm.

Abstract: A method of parabola error evaluation using Geometry Ergodic Searching Algorithm (GESA) was proposed according to geometric features and fitting characteristics of parabola error. First , the feature points of least-squared parabola are set as reference feature points to layout a group of auxiliary feature grid points. After that, a series of auxiliary parabolas as assumed ideal parabolas are reversed with the auxiliary feature points.The range distance from given points to these assumptions ideal parabolas are calculated successively.The minimum one is parabola profile error.The process of GESA was detailed discribed including the algorithm formula and contrastive results in this paper.Simulation experiment results show that the geometry ergodic searching algorithm is more accurate than the least-square method. The parabola profile error can be evaluated steadily and precisely with this algorithm based on the minimum zone.

Abstract: Automotive electronic control units (ECU) play an increasingly important role in modern cars. With the rapid development of ECU market, more and more ECU for vehicle appears, the ECU performance test is very important because automotive ECU closely related to the reliability and the safety of the vehicle. Starting from analyzing the characteristics of automotive electronic control unit, this article describes a generic automotive ECU testing platform, with the testing platform characteristics discussed. The proposed ECU testing platform can comply hardware testing, function testing, and communication testing, it can also reduce the test errors, and support assembly line test of ECU, which can greatly reduces the complexity of ECU functional test and test work.

Abstract: Analyzing the significance of macroscopically dynamic monitoring of newly increased construction land, and considering the influence of various factors, this paper selects central Shaanxi Plain in Northwestern region for a typical experimental zone, setting up knowledge base of remote sensing images interpretation, using multi-temporal remote sensing images, carrying through interactive interpretation of change patterns spots of newly increased construction land and field validation. Results of middle resolution remote sensing image interpretation are compared, analyzed. Additionally, interpretation accuracy of different scales are studied, especially between middle resolution 10 ms ALOS remote sensing image and panchromatic high resolution remote sensing, on newly increased construction land in northwestern plains, to find out the remote sensing images which can not only quickly extract new construction land change patterns spots, but also can satisfy precision requirement of the business.

Abstract: The paper describes the characteristics, operating principles and specific application methods of Microwave Detection Techniques. Taking Microwave as the information carrier, Microwave Detection detects and diagnoses various applicable material components and natural phenomena. Its measurement and detection on some non-electrical properties, such as material properties and process parameters, are non-contact, non-polluting and rapid. And it has broad application prospects in the field of engineering.