Hu Zhikun

A Compression Approach of Power Quality Monitoring Data Based on Two-dimension DCT

A compression approach of power quality monitoring data based on two-dimension Discrete Cosine Transform (DCT) is presented to deal with huge data about power quality event detection. The monitoring data was truncated and recomposed in multiple cycles to transform the one-dimension data into the two-dimension data, which was a matrix in essence. The matrix was divided into some sub-blocks all of which were 8×8 matrices. These matrices were performed two-dimension DCT. The elements at the same location of all sub-matrices form a new matrix, and the elements were at the equivalent energy level. The energy levels of new matrices were measured by average energy, and quantitative matrix was obtained by a threshold of average energy. The new matrices and quantitative matrix were used to represent the monitoring data set.

A comprehensive evaluation scheme of the traffic congestion

A congestion evaluation strategy which contained multi-index real-time evaluation and non-real-time evaluation was proposed in order to tackle the problem that the convention strategy can not evaluate traffic congestion effectively, because of the single index, incomprehensive type. In the real-time evaluation of this scheme, if data was missing seriously, the traveling speed should be taken as the only index to evaluate the traffic congestion, else the traveling speed, saturation and delay should be taken as the index to evaluate the traffic congestion by using running state classification. In the non-real-time evaluation, the traveling speed throughout one day should be taken as the independent variable to establish Gaussian mixture distribution model and the evaluation result could be get by analyzing this model. Simulation results indicate that the real-time evaluation and the non-real-time evaluation can analyze the traffic flow objectively and evaluate the traffic congestion effectively.

Fast generation method of fuzzy rules and its application to flux optimization in process of matter converting

A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules with consequent real number were extracted from numerical data, and a linguistic representation method for deriving linguistic rules from fuzzy if-then rules with consequent real numbers was developed. The linguistic representation consisted of two linguistic variables with the degree of certainty and the storage structure of rule base was described. The simulation results show that the method involves neither the time-consuming iterative learning procedure nor the complicated rule generation mechanisms, and can approximate complex system. The method was applied to determine the flux amount of copper converting furnace in the process of matter converting. The real result shows that the mass fraction of Cu in slag is reduced by 0.5%.