Aiming at the issues of isolated and fragmentation between quality control and production planning of enterprises,a quality control method and its technology hierarchy based on lean production was proposed.The methods and principles of setting key quality procedure were presented on the basis of analyzing production procedure and optimizing procedure flow.And quality kanban was also introduced to manage product quality of machining procedure so as to provide evidences for material requirement management.An example was given out to verify the validity of methods mentioned above in the end.The results shown that setting quality control procedure in machining procedure flow of product production properly could manage material requirement of enterprise's production effectively.

The incineration technology is used to dispose sludge from a WWTP(wastewater treatment plant) in Guangdong Province.Then the sludge incineration flue gas is purified by the secondary effluent from the WWTP.After treatment,88.5 percent of the sludge weight can be reduced,and the pollutants in emitted air are below the required emission limits of Standard for Pollution Control on Municipal Solid Waste Incineration(GB18485—2001),and the pollutants in absorption solution are below the required emission limits of Integrated wastewater discharge standard(GB8978—1996).

This paper studied the deference between ACD and SCD model used ultra-high-frequency data by MCMC.Two models' parameters and DIC values were getted,which can be used to analyze convergence,robustness,complexity and performance.The results indicate that two models are convergent.On one hand,ACD model is better in convergence and robustness,on the other hand,SCD model is better in performance and complexity.

Bid-price control is a popular method for controlling the sale of inventory in revenue management.It is well known that the network capacity control problem can be formulated as a dynamic programming model.In this paper,we approximate the optimal dynamic programming value function with an affine function of the state vector and develop our model based on the flight segment demands.We show that the resulting problem is the deterministic linear programming(DLP) for computing network bid-prices.The DLP yields tighter bounds than the classical DLP.We give a column generation procedure for solving the DLP within a desired optimality tolerance,and provide simulation experiments.The numerical results show the policy perform from our solution approach can outperform that from the classical DLP.

Under the assumption that the security's price follows the geometric Brown motion, We studied the optimal strategies which minimize the sum of the variance and expectation of hold position cost by using of variations.Using Euler equation,the analytical solution of optimal holding strategy was obtained.By a numerical analysis,the sensitivity of the strategy was discussed.The strategies were affected by temporary impact parameter,market volatility and rist adverse reference and are unrelated to permanent impact parameter.

Decision tree algorithm in univariate tests causes large-scale,complex rules that are difficult to understand.Multi-variable decision tree is effectively used in the classification of data mining.The key to build it lies in the reasonable choice of attributes combination based on the interconnection between attributes.Based on the rough set theory of attribute dependability and the concept of conditional attributes dispersion degree in information system,a new multi-variable decision tree algorithm called RD is proposed.The results of experiments on the UCI show that the decision tree built by the proposed method has better classification results than those of ID3 algorithm and multi-variate decision tree construction algorithm based on the relative core of attributes.

A classifiers ensemble approach based on Principal Component Analysis (PCA) was proposed. Lots of original classifiers were got from Random Subspace Method (RSM). According to their classification performance, their preservation scores were given, so the preferential ranks for classifiers preservation were ordered, by which a set of classifiers was selected from original classifiers. Theoretic analysis and experimental results in face database ORL show that this pattern classification method based on ensemble PCA is efficient for pattern recognition.

Based on stroke plane extraction and dynamic meshing partition,a new method for Chinese character feature extraction is proposed.This method combines stroke plane with fuzzy membership to improve the unstable problem,which is caused by the distortion of stroke.The basic idea is to divide the Chinese character image into four different stroke planes by dynamic meshing,allocate the fuzzy membership for each meshing,then compute the weighted accumulation histogram aimed for obtaining the feature for each meshing.The experiment results on NUST603HW handwritten Chinese character database of Nanjing university of science and technology verify the effectiveness of the proposed method.

A classifier ensemble method Cagging based on class information was proposed.Training sets of each classifier generated through selecting samples repeatedly based on class information enhanced the diverse of each classifier.The classify results of all classifiers were combined by voting with weight vector to use the diverse of each classifier better.This weight vector was set for each classifier according to its classification performance to each class.The experimental results on face database ORL verify Cagging's validity.Furthermore,the method to generate the classifier in Cagging can generate new ensemble classifier through incremental learning.So,a classifier ensemble method Cagging-I based on increment learning was designed by extending Cagging,and the experimental results verify its validity.