In the outdoor working environment of inspection robots, recognizing the number of meter was susceptible to illumination. An adaptive adjustment algorithm for meter images based on 2D-Gamma function was studied. Then an algorithm based on Maximally Stable Extremal Region (MSER) was proposed to extract the pointer. Firstly, the reflection component was extracted by three-scale Gaussian functions, 2D Gamma function was constructed to automatically adjust brightness of the reflected or overshadowed region of image. Secondly, the pointer region was extracted through two MSER detections. Thirdly, on the condition that the pointer passed through the axis of dial, the pointer was precisely positioned by thinning algorithm and Progressive Probabilistic Hough Transform (PPHT) to improve the accuracy of positioning lines. Finally, on basis of comparing the positions of two endpoints by PPHT with axis, the direction of pointer was directly determined, thus calculating the number was more convenient. The experimental results show that the proposed method can deal with different types of meters under different lighting conditions. Moreover, the correct rate of identification reaches over 94%.

Aiming at autonomous positioning of wheeled warehous robots, an indoor positioning method based on visual landmark and odometer data fusion was proposed. Firstly, by establishing a camera model, the rotation and translation relationship between the beacon and the camera was cleverly solved to obtain the positioning information. Then, based on the analysis of the characteristics of the angle difference between the gyroscope and the odometer, a method of angle fusion based on variance weight was proposed to deal with low update frequency and discontinuous positioning information problems. Finally, to compensate for a single sensor positioning defect, the odometer error model was designed to use a Kalman filter to integrate odometer and visual positioning information. The experiment was carried out on differential wheeled mobile robot. The results show that by using the proposed method the angle error and positioning error can be reduced obviously, and the positioning accuracy can be improved effectively. The repeat positioning error is less than 4 cm and the angle error is less than 2 degrees. This method is easy to operate and has strong practicability.

The forecasted concentration of PM2.5 forecasting model greatly deviate from the measured concentration. In order to solve this problem, the data (from February 2015 to July 2015), consisting of measured PM2.5 concentration, PM2.5 model (WRF-Chem) forecasted concentration and model forecasted data of 5 main meteorological factors, were provided by Shanghai Pudong Meteorological Bureau. Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) algorithm were combined to build rolling forecasting model of hourly PM2.5 concentration in 24 hours in advance. Meanwhile, the nighttime average concentration, daytime average concentration and daily average concentration during the upcoming day were forecasted by rolling model. Compared with Radical Basis Function Neural Network (RBFNN), Multiple Linear Regression (MLR) and WRF-Chem, the experimental results show that the proposed SVM model improves the forecasting accuracy of PM2.5 concentration one hour in advance (according with the results concluded from finished research), and can comparatively well forecast PM2.5 concentration in 24 hours in advance, and effectively forecast the nighttime average concentration, daytime average concentration and daily average concentration during the upcoming day. In addition, the proposed model has comparatively high forecasting accuracies of hourly PM2.5 concentration in 12 hours in advance and nighttime average concentration during the upcoming day.

Focusing on the state estimation problem of a Wireless Sensor Network (WSN) without a fusion center, a Quantized Distributed Kalman Filtering (QDKF) algorithm was proposed. Firstly, based on the weighting criterion of node estimation accuracy, a weight matrix was dynamically chosen in the Distributed Kalman Filtering (DKF) algorithm to minimize the global estimation Error Covariance Matrix (ECM). And then, considering the bandwidth constraint of the network, a uniform quantizer was added into the DKF algorithm. The requirement of the network bandwidth was reduced by using the quantized information during the communication. Simulations were conducted by using the proposed QDKF algorithm with an 8-bit quantizer. In the comparison experiments with the Metropolis weighting and the maximum degree weighting, the estimation Root Mean Square Error (RMSE) of the mentioned dynamic weighting method decreased by 25% and 27.33% respectively. The simulation results show that the QDKF algorithm using dynamic weighting can improve the estimation accuracy and reduce the requirement of network bandwidth, and it is suitable for network communications limited applications.

To solve the problem of Fine Particulate Matter (PM2.5) concentration prediction, a PM2.5 concentration prediction model was proposed. First, through introducing the comprehensive meteorological index, the factors of wind, humidity, temperature were comprehensively considered; then the feature vector was conducted by combining the actual concentration of SO2, NO2, CO and PM10; finally the Least Squares Support Vector Machine (LS-SVM) prediction model was built based on feature vector and PM2.5 concentration data. The experimental results using the data from the city A and city B environmental monitoring centers in 2013 show that, the forecast accuracy is improved after the introduction of a comprehensive weather index, error is reduced by nearly 30%. The proposed model can more accurately predict the PM2.5 concentration and it has a high generalization ability. Furthermore, the author analyzed the relationship between PM2.5 concentration and the rate of hospitalization, hospital outpatient service amount, and found a high correlation between them.

Multiple Sclerosis (MS) is a chronic disease that affects the central nervous system and MS lesions are visible in conventional Magnetic Resonance Imaging (cMRI). A new method for the automatic segmentation of MS White Matter Lesions (WML) on cMRI was presented, which enabled the efficient processing of images. Firstly the Kernel Fuzzy C-Means (KFCM) clustering was applied to the preprocessed T1-weight (T1-w) image for extracting the white matter image. Then region growing algorithm was applied to the white matter image to make a binary mask. This binary mask was then superimposed on the corresponding T2-weight (T2-w) image to yield a masked image only containing white matter, lesions and background. The KFCM was reapplied to the masked image to obtain WML. The testing results show that the proposed method is able to segment WML on simulated images of low noise quickly and effectively. The average Dice similarity coefficient of segmentation result is above 80%.

In view of complicated and inefficient calculation of system matrix, a simple length weighted algorithm was proposed. Compared with the traditional length weighted algorithm, the proposed algorithm reduced situations of the intercepted photon rays with the grid and the grid index of the proposed approach was determined in the two-dimensional coordinate. The computational process of the system matrix was improved based on the proposed algorithm. The image reconstructed with the system matrix was constructed through the new process, and the quality of the reconstructed image was assessed. The experimental results show that the operation speed of the proposed algorithm is more than three times faster than Siddon improved algorithm, and the more lengths in the length weighted algorithm get considered, the better quality of the reconstructed image has.

Concerning the accurate positioning of Optical Mark Recognition (OMR) images without any position information, an image segmentation approach of mutation signal detection based on wavelet transformation was proposed. Firstly, the horizontal and vertical projective operations were processed, and then these functions were transformed by wavelet to detect mutation points, which can better reflect the boundary of OMR information. This algorithms adaptability is based on limited times of wavelet transform and mutation signal detection. The experimental results demonstrate that the method possesses high accuracy of segmentation and stability, and the mean square error of segmentation accuracy can be 0.4167 pixels. The processing of this method is efficient because the segmentation only used the horizontal and vertical information. This algorithm is not sensitive to noise because of the statistic characteristic of projection functions and multi-resolution characteristic of wavelet tranformation.

Mining association rules is an important research field in data mining.The traditional algorithm mining association rules,or slowly produces association rules,or produces too many redundant rules,or it is probable to find an association rule,which posses high support and confidence,but is uninteresting,and even is false.Furthermore,a rule with negative-item can’t be produced.This paper put forwards a new algorithm MVNR(Mining Valid and non-Redundant Association Rules Algorithm),which primely solved above problems by using the minimal subset of frequent itemset.

Mining association rule is an important matter in data mining, in which mining maximum frequent itemsets is a key problem in mining association rule. Many of the previous algorithms mine maximum frequent itemsets by producing candidate itemsets firstly, then pruning. But the cost of producing candidate itemsets is very high, especially when there exist long patterns. In this paper, the structure of a FP-tree was improved, a fast algorithm DMFIA-1 based on FP-tree for mining maximum frequent itemsets was proposed, which did not produce maximum frequent candidate itemsets and was more effective than DMFIA. The new FP-tree is a one-way tree and there is no pointer pointing its children in each node, so at least one third of memory is saved.