To solve the security problems caused by the disclosure of blockchain ledgers， the key lies in the hiding of private information. An attribute-based encryption scheme with multiple authorities was proposed for privacy protection of blockchain data. Compared to single authority， multiple authorities are decentralized and avoid any single point of failure. First， the key component generation algorithm was modified， where each authority used the user identity as a parameter to generate private key components， preventing collusion between nodes to access unauthorized data. Then， identity-based signature technology was modified to establish a connection between user identities and wallet addresses， making the blockchain policeable and the illegal users traceable. Finally， based on the DBDH （Decisional Bilinear Diffie-Hellman） hypothesis， the safety of the proposed scheme was proved in random oracle model. The experimental results show that， compared with the blockchain privacy protection scheme based on the ring signature based on the elliptic curve and the blockchain privacy protection scheme supporting keyword forgetting search， the proposed scheme takes the least amount of time and is more feasible， when generating the same number of blocks.

To meet the needs of data sharing in the context of digitalization currently， and take into account the necessity of protecting private data security at the same time， a blockchain smart contract private data authorization method based on TrustZone was proposed. The blockchain system is able to realize data sharing in different application scenarios and meet regulatory requirements， and a secure isolation environment was provided by TrustZone Trusted Execution Environment （TEE） technology for private computing. In the integrated system， the uploading of private data was completed by the regulatory agency， the plaintext information of the private data was obtained by other business nodes only after obtaining the authorization of the user. In this way， the privacy and security of the user were able to be protected. Aiming at the problem of limited memory space in the TrustZone architecture during technology fusion， a privacy set intersection algorithm for small memory conditions was proposed. In the proposed algorithm， the intersection operation for large-scale datasets was completed on the basis of the ??grouping computing idea. The proposed algorithm was tested with datasets of different orders of magnitude. The results show that the time and space consumption of the proposed algorithm fluctuates in a very small range and is relatively stable. The variances are 1.0 s² and 0.01 MB² respectively. When the order of magnitudes of the dataset is increased， the time consumption is predictable. Furthermore， using a pre-sorted dataset can greatly improve the algorithm performance.

Deep learning based speech enhancement algorithms typically perform better than the traditional noise suppression based speech enhancement algorithms. However， deep learning based speech enhancement algorithms usually do not work well when there exists mismatch between training data and test data. Aiming at the above problem， a novel Progressive Ratio Mask （PRM）-based Adaptive Noise Estimation （PRM-ANE） method was proposed， and this method was used for the preprocessing of the speech recognition system. In the method， Improved Minima Controlled Recursive Averaging （IMCRA） algorithm with frame-level noise tracking capability and utterance-level deep progressive learning algorithm nonlinear interactions between speech and noise were used comprehensively. Firstly， two Dimensional-Convolutional Neural Network （2D-CNN） was adopted to learn PRM， which increased with the increase of Signal-to-Noise Ratio （SNR）. Then， the PRMs at sentence level were combined by the conventional frame-level speech enhancement algorithm to perform speech enhancement. Finally， the enhanced speech based on the multi-level information fusion was directly fed into speech recognition system to improve the performance of the system. Experimental results on the CHiME-4 real test set show that the proposed method can achieve a relative Word Error Rate （WER） of 7.42%， which is 51.41% lower than that of IMCRA speech enhancement method. Experimental results show that the proposed enhancement method can effectively improve the performance of downstream recognition tasks.

Focusing on the challenge task for mining complementary information in different levels of features in the deep subspace clustering problem， based on the deep autoencoder， by exploring complementary information between the low-level and high-level features obtained by the encoder， a Diversity Represented Deep Subspace Clustering （DRDSC） algorithm was proposed. Firstly， based on Hilbert-Schmidt Independence Criterion （HSIC）， a diversity representation measurement model was established for different levels of features. Secondly， a feature diversity representation module was introduced into the deep autoencoder network structure， which explored image features beneficial to enhance the clustering effect. Furthermore， the form of loss function was updated to effectively fuse the underlying subspaces of multi-level representation. Finally， several experiments were conducted on commonly used clustering datasets. Experimental results show that on the datasets Extended Yale B， ORL， COIL20 and Umist， the clustering error rates of DRDSC reach 1.23%， 10.50%， 1.74% and 17.71%， respectively， which are reduced by 10.41， 16.75， 13.12 and 12.92 percentage points， respectively compared with those of Efficient Dense Subspace Clustering （EDSC）， and are reduced by 1.44， 3.50， 3.68 and 9.17 percentage points， respectively compared with Deep Subspace Clustering （DSC）， which indicates that the proposed DRDSC algorithm has better clustering effect.

For the difficulty of complex non-linear system modeling, a new system modeling algorithm based on the Takagi-Sugeno (T-S) Fuzzy Radial Basis Function (RBF) neural network optimized by improved Particle Swarm Optimization (PSO) algorithm was proposed. In this algorithm, the good interpretability of T-S fuzzy model and the self-learning ability of RBF neural network were combined together to form a T-S fuzzy RBF neural network for system modeling, and the network parameters were optimized by the improved PSO algorithm with dynamic adjustment of the inertia weight combined with recursive least square method. Firstly, the proposed algorithm was used to do the approximation simulation of a non-linear multi-dimensional function, the Mean Square Error (MSE) of the approximation model was 0.00017, the absolute error was not greater than 0.04, which shows higher approximation precision; the proposed algorithm was also used to build a dynamic flow soft measurement model and to finish related experimental study, the average absolute error of the dynamic flow measurement results was less than 0.15L/min, the relative error is 1.97%, these results meet measurement requirements well and are better than the results of the existing algorithms. The above simulation results and experimental results show that the proposed algorithm is of high modeling precision and good adaptability for complex non-linear system.

Considering the high computation complexity and storage requirement of Naive Bayes (NB) based on Parzen Window Estimation (PWE), especially for classification on interval uncertain data, an improved method named IU-PNBC was proposed for classifying the interval uncertain data. Firstly, Class-Conditional Probability Density Function (CCPDF) was estimated by using PWE. Secondly, an approximate function for CCPDF was obtained by using algebraic interpolation. Finally, the posterior probability was computed and used for classification by using the approximate interpolation function. Artificial simulation data and UCI standard dataset were used to assume the rationality of the proposed algorithm and the affection of the interpolation points to classification accuracy of IU-PNBC. The experimental results show that: when the interpolation points are more than 15, the accuracy of IU-PNBC tends to be stable, and the accuracy increases with the increase of the interpolation points; IU-PNBC can avoid the dependence on the training samples and improve the computation efficiency effectively. Thus, IU-PNBC is suitable for classification on large interval uncertain data with lower computation complexity and storage requirement than NB based on Parzen window estimation.

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 automatic measurement of the radio spectrum parameters, the traditional threshold-based method is difficult to obtain satisfactory result when the spectrum has undulating background noise. To solve this problem, this paper proposed a signal noise separation algorithm based on morphological gradient to separate signals from background noise by detecting edges of signals. The experimental results show that this algorithm can deal with the spectrum with undulating background noise, and get more accurate separation result than the traditional method.

Least Square Support Vector Regression (LS-SVR) is sensitive to noise and outliers. By setting the upper bound of the loss function, a non-convex Ramp loss function was proposed, which had strong ability of suppressing the impact of outliers. Since the Ramp loss function was neither convex nor differentiable and the corresponding non-convex optimization problem was difficult to implement, the Concave-Convex Procedure (CCCP) was employed to transform the non-convex optimization problem into a convex one. Finally, a Newton algorithm was introduced to solve the robust model and the computational complexity was analyzed. The numerical experimental results on artificial and Benchmark data sets show that, in comparison with LS-SVR, the proposed approach has significant robustness to noise and outliers, and it also simultaneously reduces the training time.

A large number of data gathered in many projects are visualized in the form of curves by graphical software. For the convenience in recognition and interpretation of curves, by means of the introduction of the concept of graphical label set, GLSTool(graphical label set tool) was designed based on VC++ to create and edit graphical label set, and mark the curves with the graphical label set which has existed. GLSTool is successfully applied to the well logging curves, and turns out very efficient and convenient.