Disassembly Process Planning of Products Based on Matrix Operation

Based on fuzzy Petri nets, a fuzzy reasoning Petri nets (FRPN) model is defined. And then, combining fuzzy Petri nets with matrix operation, an algorithm of formalized fuzzy reasoning process is proposed. Based on the fuzziness and uncertainness of the knowledge in the disassembly process of products, a decision making model in disassembly process is established. Furthermore, taking realistic disassembly as an example, an algorithm of decision-making in disassembly sequence is discussed. The result shows that the model of decision-making has strong parallel operation ability in the disassembly process sequence. It also can make intelligent decisions based on the product information originating from each disassembly step.     

Fuzzy Transportation Problem and Its Hybrid Intelligent Algorithm

The authors investigate a more practical transportation problem under fuzzy environment, that is , capacities of supplies and demands in the transportation problem are fuzzy variables. To obtain a directive decision, the authors construct a mathematical model for the fuzzy transportation problem based chance constrained programming and dependent chance programming in fuzzy environment. In addition, since there are many complex fuzzy variables in the mathematical model, the authors design the genetic algorithm to solve the model based on fuzzy simulation. Finally, they give a numerical example to show the efficiency of the algorithm.     

An edge detection method based on a good point set genetic algorithm

In order to improve the convergence rate of genetic algorithms based on edge detection, a novel edge detection method based on a good point set genetic algorithm (GGA) was proposed. The proposed method designed the crossover operation with the theory of good point set in which the progeny inherits the common genes of the parents which represent its family so as to improve the convergence rate of the genetic algorithm. Furthermore, before the algorithm was used for edge detection, the feature space of the image grey level was transformed into the feature space of the fuzzy entropy. Dissimilarity enhancement processing next was applied to the image by using a fuzzy entropy theory to filter the non edge pixels so as to reduce the scale of the solution domain. This approach offered another efficient way to improve the convergence rate. Experimental results show the proposed algorithm performs very well in terms of convergence rate. The detected edge image is well localized, thin, and robustly resistant to noise.     

Hyper-spectral estimation of wheat biomass after alleviating of soil effects on spectra by non-negative matrix factorization

Hyper-spectral technology has been proven to be an effective method for the fast and non-destructive monitoring of crop biomass. However, the biomass estimation accuracy of this method is limited due to the effects of background factors, such as soils and water. In this study, a spectral separation method, non-negative matrix factorization (NMF), was proposed to alleviate the effects of soil on spectra. With the application of the NMF method, pure vegetation spectra were extracted from the field-observed spectra of wheat canopy, which were collected in four growing seasons from the tillering to the booting stages of wheat. Then, prediction models of wheat biomass (WB) were established and validated using the extracted spectra with the partial least squares regression (PLSR) method. The results showed that the NMF method could effectively separate the vegetation spectra from the mixed canopy spectra. Based on the extracted vegetation spectra, the WB prediction accuracy could be greatly improved with an increase of 31.7% for the R²_p and an increase of 46.6% for the ratio of performance to deviation (RPD) as compared to the original spectra, indicating that the NMF method could significantly improve the performance of the WB prediction model. This method has potential application in the estimation of biomass using remote sensing technology.     

An ALgorithm of the Second Kind Fuzzy Reliability

Based on L.A.Zadeh's idea of fuzzy probability operation in his language probability which space is fuzzy probability,the concrete algorithm of the second kind fuzzy reliability of engineering structure/system is researched.The calculating equation of the fuzzy probabiliy of the second kind fuzzy reliability is given out by using the extension principle for fuzzy sets.The fuzzy set decompose theorem,which satesfies the closed probability operation,is given out and proved.The method calculating fuzzy reliability of that structure/system is studied,where the resistance and load effect of the structure/system have probability.The analysis indicates that the calculation of the second kind fuzzy reliability for a structure/system can be transformed into a series of solving optimum programming.The result of the example indicates the rationality of above algorithm.     

模糊控制系统的垂直切换判决算法分析

针对模糊控制系统中切换时延较长和切换次数较多的问题,研究了一种基于模糊控制系统的垂直切换判决算法。在基于信号强度判决时将RSS作为门限值,提高系统判决能力;同时,将网络参数和服务类型作为判决因素,结合层次分析法引入并行的模糊控制系统,缩短了切换判决的时间、选择适合用户的最佳网络,做出垂直切换。仿真结果表明,该算法减少了切换次数,降低了切换时延,增强了系统的性能。与传统的切换算法相比较,该算法对切换的判决因素考虑的更全面,兼顾了用户终端的使用环境及成本问题,有效地保证了网络的服务质量。     

Corner Detection Algorithm Based on Fuzzy Subjection Degree

Corner detection in planar curves is a basic problem in image processing domain. The authors start from concluding corner's intuitive characters, introduce fuzzy set theory, and propose a multi-feature based algorithm which contains fuzzy subjection degree to achieve the goal. First, two simple fuzzy characters are addressed which compile with human perception. Second, three formulations corresponding to corner features are presented, and they are synthesized and summarized into a principle used to detect and locate corners. At last, by comparing computed results with existed results, robustness and effectiveness of our algorithm are proved.     

基于粒子群算法和支持向量机的黄花菜叶部病害识别

使用数字图像处理技术,以黄花菜叶部病害图像为识别对象,基于Lab空间和K-means聚类算法分割病害区域,提取目标区域的颜色特征、方向梯度直方图(histogram of oriented gradient,HOG)特征和形状特征,分别建立单一特征模型和特征融合模型,采用粒子群(particle swarm optimization,PSO)算法通过交叉验证优化支持向量机(support vector machine,SVM)模型的惩罚因子和核参数,建立基于PSO-SVM的多特征融合分类模型识别黄花菜病害。基于SVM的多特征融合分类模型识别率高于单一特征分类模型,识别率可达为81.67%;基于PSO-SVM多特征融合分类模型识别率高达92.39%。基于PSO-SVM的多特征分类模型识别率高,可以及时、便捷、高效地识别黄花菜病害。     

Drift error compensation algorithm for fiber optic gyro based on T-S fuzzy modeling

A drift error nonlinear compensation algorithm for Fiber Optic Gyro (FOG) is presented based on T-S fuzzy model with the antecedent parameters identified by G-K clustering algorithm and the error model of T-S fuzzy model with the consequent parameters identified by least square algorithm. The computed results show that this model can compensate the original data effectively, while the error principles of FOG do not need to be understood well. Comparing with the original data, compensation with linear fitting and compensation with neural network, the absolute error of the proposed model reduces by 99%, 96% and 10%, respectively. The error variance reduces by 99%, 98% and 20%, respectively. The results indicate that this proposed algorithm can be simply operated with high precision and easy to realize in engineering.     

THE CHARACTERISTICS OF FUZZY AND ADAPTIVE PI COORDINATE DECISION CONTROLLER

On the basis of analysing problems of conventional fuzzy controller in the design of the structure and algorithm, this paper puts forward a kind of control algorithm that is fuzzy and Adaptive PI coordinate decision controller (FACDC) . It adopts the hierarchial control structure imitating intelligent control and combines fuzzy controller and adaptive PI controller. It takes full advantage of two controllers. Since the algorithm of FACDC in model reference fuzzy adaptive algorithm, it avoids the complexity of conventional adaptive algorithm.     

多邻域保持嵌入的人脸识别方法

现有流形学习算法在学习人脸数据时,假设所有数据点位于单一低维嵌入流形之上,当数据点实际分布在不同的流形上时,单流形假设就会影响数据真实空间结构。为此提出一种基于多邻域保持嵌入（multiple neighborhood preserving embedding, M-NPE）的学习算法来发现不同类别数据在不同维度的低维嵌入空间中分布的多流形结构。首先,单独学习不同类别数据的流形,得到反映其本质特征的流形;再通过遗传算法搜索每个流形的最优维数;最后依据最小重构误差分类器对样本分类。在Extended Yale B和CMU PIE这2个大型人脸库上实验结果验证了该算法的有效性。     

Fuzzy Identification on Mathematical Model of Dynamic Process of Evaporator in Refrigeration System

Founding the mathematic model of refrigeration system's dynamic process is the basis of realizing the optimal control of refrigeration machines.Refrigeration evaporator is a kind of two-phase flow and heat exchange with complex process.For its obvious nonlinearity and uncertainty,it is difficult to describe by accurate theoretical model.This paper partitions the input data into some clusters by entropy method and competitive learning algorithm,then the on-line fuzzy identification of dynamic process mathematical model of evaporator is achieved by utilizing ultimate parameter which is ascertained by the recursive least-square(RLS).The simulation results show that fuzzy identification method is effective for on-line model process of evaporator in refrigeration system.The model has not only superior identification precise,but also quite perfect generalizable performance and traceable ability.     

Biological Mass Spectrometry for Macromolecules:Approaches and Applications

According to the indiscernibility relation, in this paper, the concept of indiscernibility matrix is proposed and the relation between discernibility matrix and indiscernibility matrix is shown. The advantage of indiscernibility matrix is pointed out. Then, an attribute reduction algorithm based on indiscernibility matrix is introduced. Compared with discernibility matrix algorithm, this algorithm greatly reduces running time and memory space.     

Orthogonal Back-Propagation Algorithm of Generalized Inverse of a Matrix

Calculating generalized inverse of a matrix is a basic algorithm for inverstigating linear equations and optimization problems. In this paper we investigate the algorithm of generalized inversion of a matrix by using Orthogonalized Back-Propagation Algorithm (OBPA). There is no problem for convergence in OBPA. It is shown how OBPA avoids some of the difficulties posed by the Back-Propagation(BP)algorithm.     

FUZZY OPTIMAL MODEL OF ROCK CLASSIFICATION

This paper proposed a fuzzy optimal model of rock classification, according to a number of controlling factors of rock classification. Satisfactory results are obtained by applying the model to the real rock mass classification.     

Application of fuzzy comprehensive evaluation in mine geological environment

Based on the complex geological environment and hydro-geological conditions in mountainous areas the fuzzy comprehensive evaluation index system of the mine geological environment is constructed. It uses the weak fuzzy consistent matrix and analytic hierarchy process to calculate the weight, which solves the question that traditional AHP is difficult to meet the consistency test. According to the special geological conditions of the mine, it divides the impact of the geological environment into three levels, builts the fuzzy comprehensive evaluation table and determines the membership degree of the influencing factors. Combined with fuzzy comprehensive evaluation method, it developes a second-order fuzzy comprehensive evaluation model of the impact of the mine geological environment. By means of the application of this model to the actual mining, it shows that the evaluation results of the model are highly similar to the actual results of the impacts of landslides, land subsidence and other geological disasters.     

Using BP Neural Network to Determine the Membership Function of the Input of Fuzzy Control System

While design the fuzzy controller, it is very important to determine the membership function of fuzzy variables.The data can be broadly classified as fuzzy sets by using the classification property of the BP neural network. The author selects a BP neural network with one hide layer and uses S function to the input and hide layer, and linear function to the output layer.Advanced BP algorithm isused to train the BP neural network in the environment of MATLAB . The nearer to the target values is the better the last output is.With the trained BP network , the membership values of the inputs can be got ten. This method has high rate and low error.     

A blind channel estimation algorithm for UltraWide band channel based on compressive sensing

The sampling speed for the ultra wide band (UWB) channel is too high to realize with the existing sampling technology. To solve the problem, a novel blind channel estimation algorithm was presented based on the theory of compressive sensing. Firstly, some measurements are obtained which are linear combinations of the received signals multiplied by a random incoherent measurement matrix. Then, the mathematical model is established by exploiting the first statistics of the measurements. Finally, the orthogonal matching pursuit (OMP) algorithm is utilized to get the estimating channel parameters. With the proposed algorithm, the number of the measurements need for channel estimation is much smaller than that of the samples needed for the existing algorithms, which reduces the ADC resources greatly. The simulation result shows that the estimation performance of the algorithm is good, while the bit error rate (BER) is only 2~3dB higher than that of the exact channel.     

Segmentation of Color Image Based on Feature Divergence and Fuzzy Theory

In order to improve general adaptive capability of algorithm,the new color image segmentation algorithm based on feature divergence and fuzzy theory(FDCIS) is proposed.The algorithm introduces feature divergence and fuzzy dissimilarity function into calculation in order to measure the dissimilarity of feature vector,clusters data by means of feature divergence,and accomplishes the merge of image region.The experimental results demonstrate that the color image segmentation result of the proposed approach reduce calculation on large sample of color image,simply and effectively solve over-segmentation of color image,avoid the dependence of the algorithm on initial condition,and hold favorable consistency in terms of human perception.