Neighbour field method for population initialization of TSP

It is sensitive to the initial population while the genetic algorithm (GA) is used to solve the traveling salesman problem (TSP). To overcome this problem, the neighbour field method is presented to create initial population. In this method the next city is not the nearest as yet unvisited location but randomly selected from the unvisited cities in neighbour field. Neighbour filed method can extract the local optimal information of adjacent cities, and the constructed population has the diversity character. Comparing to the random initial method, the mean value obtained by the neighbour field method in four standard test instances of TSPLIB improved by 46.3%. The simulation results show the effectiveness of the neighbour field method for creating the initial population.     

On line tool breakage monitoring method based on power information and cross-correlation algorithm

In order to reliably monitor unexpected tool failure and prevent workpiece or machine tool from possible damages in batch machining, a tool breakage on-line monitoring method based on power information and cross-correlation algorithm is proposed. In this method, wavelet coefficients of spindle-power signal are used as the characteristic vector of machining information, and then the vector sequence extracted from a normal machining process via Mallat wavelet is defined as the reference template for monitoring cutting tool condition. In batch machining, real-time characteristic vector of the workpiece in machining process is extracted via an improved real-time wavelet algorithm. The correlation of two vector sub-sequences within a sampling time window, which is described by generalized cross-correlation coefficient, decreases apparently when the tool is broken. The generalized cross-correlation coefficient is defined as tool condition index (TCI), and tool breakage can be detected by monitoring the TCI with a threshold value. Experiments show that the method can accurately identify tool breakage failures in normal machining condition, and thus it is practical.     

Feed-rate-controlled method of iterative algorithm for curve interpolation

In order to maintain a high machining accuracy and a constant speed feed-rate in CNC machining and improve the machining capability of CNC in handing complex part,the complicated interpolation algorithm needs to be used in CNC interpolation,which is time-consuming for large amount of computation,thus the machining speed is influenced. To solve this problem,based on the principle of parametric curve paths CNC interpolating,it is pointed out that Taylor series and iterative algorithm,given curve,using the chord length and the interpolation point,accurately calculate the next interpolation points. In the number of iterations and iterative error are less than the set value when the end of the iteration,next interpolation points can be calculated,and keep the current point and speed,otherwise continue iterative algorithm until they meet the requirements,feed-rate-controlled method based on iterative algorithm for curve real-time interpolation has also been given. The simulation results demonstrate that the proposed algorithm can satisfy the machining of a variety of different parametric curves. Compared with the conventional interpolation algorithm,it features are high university for machining,small computational a mount small feed-rate error and high computational accuracy,thereby greatly shorting the processing efficiency.     

OPTIMUM ARC APPROXIMATION OF COMPLEX PLANE CURVES

Arc approximation method is widely used in machining those parts contoured by various complex plane curves. In this paper, several arc approximation methods are reviewed, and the algorithm of the optimum arc approximation, which ensures the continuity of derivatives, is established. The algorithm presented can be used to precisely machine the parts with complex plane curves and the revolving body with a curved surace. In addition, it can also be used in part-programming of CNC machining.     

A Decision-Making Framework Model of Tool Selection for Green Manufacturing and Its Applications

Pursuing the green manufacturing of products is beneficial to the alleviation of environment burdens. In order to reap such benefits, green manufacturing is involved in every aspect of manufacturing processes.The optimization selection of tool is one of the important approach to improve environmentally performance of cutting machining. The objective factors of decision making problems for traditional tool selection are usually the following, quality and cost.Based on the main idea of environmentally conscious and decision making framework model of green manufacturing proposed by the authors, an multi object decision making model for tool selection is put forward. The objects includes Time(T),Quality (Q),Cost(C), Resources(R) and Environment impact(E), where T aims to minimize the produce time,Q means to maximize the quality,C means to minimize the cost, R means to minimize the consume the resources and E means to minimize the environment impact respectively. Each object is analyzed in detail and integrated the Fuzzy Clustering Analysis Algorithm together with the expert judging method apply to the model too. A case study in which a practical decision making problem for tool selection in green manufacturing machining is analyzed. Successful application of above model shows the model is practical .     

Modelling and Analysis of Multi-terminal Unified Power Flow Controller

To control the complex powers emanating from a bus in power system, there are two solutions. The obvious solution is to equip each of the n lines radiating from the node with its own UPFC. The next evolution of the idea is to equip with a multi-terminal UPFC. First, the configuration and the principle of the Two-terminal UPFC (when n=2) are presented. In consideration of the existing shortages of output model-constructing method and topological model-constructing method for T-UPFC, a switching function mathematical model of this device is constructed by introducing the concept of switching function. It has more generality than the output and the topological model with the consideration of the internal switching character and the physical course of the T-UPFC.     

Restoration algorithm with wavelength reservation collision avoidance in ASON

Aiming at the problem of link fault restoration in ASON, an improved equalizing routing algorithm (ERA) is introduced to proportion the load at the routing phase. In order to solve the easy blocking problem for the network without wavelength converter because of the wavelength continuity constraint, the algorithm WRCA is proposed at the wavelength assignment phase. This algorithm is realized by adding collision detection object (CDO) in the path message of the resource reservation protocol with traffic engineering extension (RSVP TE). Depending on the value of the CD flag, different wavelength selection strategies are applied at the destination node, and the blocks resulting from wavelength reservation collision are reduced. The simulation experiments compare four wavelength assignment algorithms with the same routing algorithm ERA. The results show that compared with the traditional first fit algorithm (FF) and random fit algorithm (RD), the FF and RD employing CD scheme can effectively reduce the blocking ratio of the whole network, and enhance the practical applicability of the restoration algorithms.     

AUTOMATIC CONTINGENCY SELECTION AND RANKING USING AN UNIFIED MODEL AND ALGORITHM FOR P - AND Q - SUBPROBLEM

A new approach of the automatic contingency selection and ranking with the network flow programming (NFP) is presented in this paper. NFP is adaptable to change the network topology as the transmission or generation branches arc in single or multiple outage. Thus the fast calculation of the contingency states by NFP cm provide the urgent information in real time N- 1 security analysis. In the paper, automatic contingency selection and ranking forP- and Q-type subproblem arc solved by an unified network flow model and algorithm. It is based on the existence of weak coupling between real and reactive quantities in power systems. The performance indices to assess the severity of contingencies are defined as the total real and reactive load required to be curtailed. The proposed ACS technique including the model and its algorithm are examined with IEEE 5-, 14-, 30-, 57- and 118- bus test systems on M-340. And the encouraging results are given in the paper.     

Application of dynamic time warping algorithm to partial discharge pattern recognition

This study uses the data sequences of apparent charge versus applied voltage （ΔQ-U) in the process of stepping-up/down the voltage as the characteristic features of partial discharge (PD). Based on Dynamic time warping (DTW) algorithm, a method is introduced to realize PD pattern recognition for insulation defect models. In the training process of DTW classifier, the train and test samples are processed by vector quantization (VQ). Moreover, the original vectors are substituted by the codeword to realize data reduction, and the DTW reference templates of various PD types are constructed by the corresponding train samples. In the testing process, the average DTW distances between test samples and each reference templates are calculated based on the accumulated distances. Recognition results are obtained by the recognition rule of nearest neighbor. The new algorithm is also supported by Fast Match (FM) technique to speed up the DTW matching process. The recognition results from five PD sources and 200 samples demonstrate the high classification rates and easy expansion of the proposed DTW algorithm. FM algorithm can save 56 percent computational time and improve the classification rates.     

An improved FCM algorithm and its application to EEG signal processing

Most of the popular EEG classifiers need to be supervised and their parameters have to be trained by a number of train data in advance.That’s the reason why they cannot be used in the real-time circumstances.In this paper,a new FCM unsupervised classification algorithm is proposed which is based on the density size of data dot and mahalanobis distance.Then,the algorithm is used to classify the EEG signals from the database of the second session of 2003 BCI competition.The EMD algorithm is used to decompose the EEG and extract the characteristic values,and then these values are classified by the proposed FCM algorithm.The experimental results show the algorithm’s feasibility and validity in the EEG classification field.     

Algorithm and Its Implementation for the Network Maximal Flow

On the algorithm of the network maximal flow, the paper provides a method of achieving it. The concrete procedure is to achieve the algorithm by using stack and structural array. First of all, an adjacency list should be established and its composition chiefly includes orientation, capacity, flux and so on. Afterwards the labeling method is adopted to find the augmenting chain according to the adjacency list. In the process, some spots are stored in the stack by means of the depth superior traverse and range superior traverse and the course way is also conserved in the array. Keep on doinh this till the maximum flow and the flow of each arc are all found.     

Hierarchical self-localization of underwater wireless sensor network nodes

The follow-up application of underwater wireless sensor network is influenced by accuracy of self-localization of nodes. The self-localization of nodes is discussed in this paper. First of all, nodes of underwater wireless sensor network are classified into several levels according to the accuracy of position of nodes and the levels are from the first to the fifth in accordance with accuracy of nodes from high to low respectively. Secondly, the level of anchor nodes can be known by those unknown nodes from the information given by the anchor nodes themselves, At the same time the unknown nodes are able to be located in the area controlled by the first level of anchor nodes that are as the aggregation. Then the positioning algorithm is designed correspondingly in accordance with the accuracy level of nodes. Finally, the positioning algorithm is simulated and analyzed. The result shows that the unknown nodes can be located effectively by hierarchical control.     

Improvement and performances of DV hop localization algorithm in wireless sensor networks

In order to improve the node localization precision of Range Free based DV Hop localization algorithm in wireless sensor networks (WSNs) with randomly distributed nodes and dynamic topology, the improved algorithm is proposed. After analyzing the DV Hop algorithm, considering the obvious errors of the estimated node coordinates calculated by Polygon based method in traditional DV Hop algorithm, the numerical iterative algorithm is constructed by employing Taylor series expansion, and simulation studies for the improved DV Hop algorithm are conducted. The selection criteria for the convergent threshold of iterative step is determined, the localization performance of the improved localization algorithm is analyzed by comparing with the traditional DV Hop algorithm under the same condition of selected convergent threshold and simulation parameters, while the calculation amount and convergence rate of the improved algorithm are also measured by the statistic iterations. The simulation results show that by selecting reasonable iterative threshold values and appropriately increasing calculation amount for node localization, the improved DV Hop localization algorithm greatly improves the localization precision and the error stability, which is feasible for node localization in WSNs with both randomly distributed nodes and dynamic topology.     

An improved back projection algorithm for electrical impedance tomography

An improved mean back projection algorithm based on an isopotential back projection algorithm of electrical impedance tomography (EIT) is presented by adopting the mean back projection theory of computer tomography. The distance from the midpoint of an injected electrode pair to the midpoint of a measured electrode pair is taken as approximately the length of the projection routine which is introduced into the back projection process. The positive and negative mean values of relative conductivities obtained by back projection are computed. The final relative conductivities are the differences between the conductivity values obtained by back projection and the corresponding sign mean value. If the results change sign, they are set to zero. Simulations and phantom experiments show that fewer artifacts are present in images reconstructed by mean back projection algorithm than those reconstructed using the back projection algorithm. The improved mean back projection algorithm is more practical and effective than the back projection algorithm.     

The method for removing the spurious modes of eigensystem realization algorithm

Due to the influence of spurious modes on the eigensystem realization algorithm results,singular value decomposition(SVD) and model energy level are introduced to remove the spurious modes of eigensystem realization algorithm,reduce part of the noise modes and improve the accuracy by reducing measurement noise by SVD. The energy matrix of each mode can be calculated by the selection matrices,the eigenvalues and eigenvectors of the state matrix and the input distribution matrix. The largest singular value of the energy matrix obtained by SVD is a measure for the energy contribution of each mode,which is named mode energy level. Spurious modes resulting from noise or model redundancy are indicated according their mode energy level. A numerical example and an experimental example are presented to demonstrate the efficacy of the method.     

Weighted sparse principal component analysis and it’s application

The principal component analysis(PCA) is one of the important methods for feature extraction,but it can’t provided more classification information by itself. In order to pick up feature information in favor of recognition from PCA eigenvector,a weight sparse principal component analysis is proposed in the paper. It achieves image de-noising function by using primitive PCA algorithm,acquires the group of weight values which are able to maximize within-class distance and minimize between-class distance in PCA feature space by utilizing Lagrange multiplier,and finishes dimension reduction by using sparse PCA(SPCA) to retain effectively some classification information of eigenvectors with little eigenvalue. In the end,the proposed algorithm is tested on an all-known public face database. The experiment results indicate the proposed algorithm has not only faster running speed but also better rate of recognition.     

The filtered back-projection algorithm of magnetic induction tomography with uniform magnetic excitation

Helmholtz coils produce uniform sinusoidal magnetic field in the center region, and the direction of magnetic field is approximate straight line, to help simplify the complexity of inverse problems. The simulation models of an 8-channel magnetic induction tomography measurement system are built, reconstructing conductivity distribution with filtered back projection algorithm. In the filtered back projection algorithm, the detected data is supplemented by the linear interpolation first, and then filtered by the Hamming filter, while adding a window filter to reduce the impact of around the coil from the divergence of the magnetic field. The different noise ratio of noise is added in the detected data to test noise suppression ability of the algorithm. The experiment results show that this filtered back projection algorithm can reconstruct the conductivity distribution under this model.     

Fault diagnosis of rotating machinery based on fuzzy clusteringoptimized by chaos embedded particle swarm optimization

A method of weighted fuzzy clustering optimized by chaos embedded particle swarm algorithm(CPSO) is put forward and applied in vibration fault diagnosis of rotating machinery. In the method, CPSO is used to displace the traditional stochastic-gradient algorithm to optimize parameters of weighted fuzzy C-means (WFCM). The best clustering num and clustering centers are automatically attained according to clustering validity function. The experimental results show that the method effectively increases the convergence velocity and precision of WFCM and so does the correctness rate of fault diagnosis for rotating machinery.     

Organically Combinatorial Inference and Optimization in Intelligence System

The inference algorithm is the most important part in intelligence system because the level of intelligence in the system is decided by it. By means of the mutual benefit for inference algorithm of expert system ,fuzzy logic and neural network ,the combinatorial inference technology which is organically composed of these three parts is put forward for inference mechanism in intelligence system. The optimization decision model is also set up. In order to bring all the advantage of every inference algorithm into play and overcome the disadvantage of single inference algorithm the common expert knowledge base is applied to organic combination and concurrent operation of all inference algorithm. In order to realize knowledge share the inference results are optimized by decision technology optimization. The research results show that the organized combinatorial inference and optimization can be applied in engineering practice effectively and is benefit for raising the inference level.     

Decision Making for Vendor Selection With AHP and Random Disposal of DEA

The vendor selection directly relatives to the procurement quality and influences the competition of the corporation in the supply chains. It is important and practical to study the vendor selection method. The measuring criteria and the method for vendor selection are analyzed. The authors put forward an new method which is combined with both the advantage of analytic hierarchy process (AHP ) and random disposal of data envelopment analysis ( DEA) for vendor selection. It introduces a random variable which solves the disadvantage of the weigh value in DEA which convert subjective estimation to objective opinion in the course of the vendor selecting. The method can improve the reliability of the vendor selection.