At present, the Proof-of-Work (PoW) mechanism of blockchain wastes a lot of computational power and electric power, and the Proof-of-Stake (PoS) mechanism is prone to bifurcation and makes the rich get richer due to the nothing-at-stake and unlimited growth of rights and interests, which cannot guarantee the stability of blockchain. In order to overcome the shortcomings of the two above mechanisms, an improved consensus mechanism of blockchain named Proof of Work and Stake (PoWaS) was proposed based on PoW and PoS. First, the difficulty of hashing calculation was reduced and the maximum value of difficulty was limited to reduce the computational power and electric power resources spent in searching for nonces. Second, the upper limits for the effective coin holding time and the coin age were set to prevent the problem of unlimited wealth of the rich caused by the infinite growth of the coin age. Third, with the concept of credit value adopted, the credit value was assigned to each node, and the credit value was increased or decreased based on node behaviors. Finally, the competition waiting time was added, and the time spent searching for nonces, coin age, and credit value were used to calculate a value named pStake. The node with the largest pStake gained the right to pack and account. A blockchain of PoWaS consensus mechanism with six nodes was built to carry out experiments. Experimental results show that the PoWaS can reduce the waste of computational power, speed up the block mining and balance the competition of accounting rights.

Focusing on the problem that the component contours taken by smart machine tool visual system always contain abnormal regions caused by the background interference,a component contour modification method based on image registration was proposed. Firstly,the component template feature point set and the matched feature point set were extracted from the component engineering drawing and the real image. Secondly,the parameters in the affine transformation model were decomposed and analyzed,and a criterion function was established based on area characteristics and edge structure characteristics of feature point sets of both images. Thirdly,an improved genetic algorithm was used to search for affine transformation parameters corresponding to the global maximum similarity between two images. After the image registration, the abnormal contour segments were detected and replaced by calculating the optimal migrated piecewise Hausdorff distance between the template contour point set and the matched contour point set. Experimental results show that the proposed method can detect the abnormal contour segments in matching contour point set with high accuracy and stability,its registration accuracy is 50% higher than that of Square Summation Joint Feature(SSJF)method,and the distance where the modified contour intersects is less than 3 pixels.

It has been found that the accuracy of particle filtering is much lower when the maneuvering target tracking process has been executed for a long time. The reason for this problem is that the diversity of the sampled particles is rapidly lost because of the excessive resampling. Therefore, the track of the maneuvering target estimated by the particle filtering will be widely wiggly from the true one. Through the research of the distribution of the sampled particles, a new algorithm was proposed. And a detected threshold was set to detect if the particle was dried up badly. When the particle was dried up badly, the particles of the state-space would be reset to relax the degree, so the new particles could contain more distribution information. The new algorithm has a high capability in the simulation of the 2-D maneuvering target tracking.

In older to solve the problem of the deviation accumulation caused by the restoring process of exemplar-based image inpainting algorithm, and to improve the accuracy of the algorithm, this paper focused on the revision of the priority computation formula, by leading an α factor to enhance the proportion of data. Therefore, the algorithm is more sensitive to the detail of image texture during the inpainting procedure. The average energy of texture feature was quantified by the wavelet coefficients. Thus the α factor was made to be self-adaptive so that the images with different energy could be inpainted adaptively through appropriate strategies. It is proved that the image quality can be enhanced effectively by this improved exemplar-based image inpainting algorithm, and the optical connectivity requirements of human beings can be satisfied by the inpainted effects.