With the security requirement improvement of the image on the network, some typical image encryption methods can't meet the demands of encryption, such as Arnold cat map and Hilbert transformation. S-DES system can encrypt the input binary flow of image, but the fixed system structure and few keys will still bring some risks. However, the sensitivity of initial value that Logistic chaotic map can be well applied to the system of S-DES, which makes S-DES have larger random and key quantities. A dual image encryption algorithm based on S-DES and Logistic map is proposed. Through Matlab simulation experiments, the key quantities will attain 10 17 and the encryption speed of one image doesn't exceed one second. Compared to traditional methods, it has some merits such as easy to understand, rapid encryption speed, large keys and sensitivity to initial value.
The paper proposes a digital image extraction and segmentation algorithm based on color features. The traditional transformation from RGB model to HSI model is improved, meanwhile the leaf color information is extracted by similarity distance between pixels. The green component of leaf image in the RGB model is strengthened, and then the digital image is transformed to the HSI model by the improved method. Finally the image is divided by similarity distance of pixels’ H weight which determines whether the pixel belongs to the blade. The results of simulation experiment shows that this algorithm can achieve a good image segmentation effect, and it has a high degree of accuracy as well as a clearly distinguish degree and many other advantages such as good consistency with human visual system. It completely meets the effectiveness and clarity requirements of image segmentation.
Method based on vague optimization evaluation is vague pattern recognition. There are six detailed steps of application. The first, Set up Techno-economic indicator system. Secondly set up preparative optimization scheme sets. Thirdly set up optimal scheme in theory. It is made up of each Techno-economic indicator optimal data. Fourthly transform techno-economic input data into vague data. The fifth, Calculating similarly measures. Similarity measures will be evaluated between preparative optimization scheme vague sets and optimal scheme in theory. The last is vague optimization evaluation. The weight of each preparative optimization scheme is given. The data of weighted similarity measures by the weight factors are obtained. And applying them we obtain the good and bad sort of vague optimization scheme. The new similarity measures formula between vague sets is given. The formula is indispensable in the method of vague optimization evaluation. Application examples show that the Vague optimization evaluation method to the conclusion is reliable.
Wood drying is a complicated non-linear process that is lagged,time-variable and coupling. For them,it is difficult to build up ideal drying model,which is corresponding to reality.The paper put forward the drying method of vacuum dehumidification.This drying method is based on neural network and expert system. The test results showed that the control method were better than traditional PID control.The control system could meet requirement of precision and a fast convergence and small overshoot.
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