The information interaction between operation anomalies and treatments is usually completed by operators to ensure the stable operation of distribution networks， but it is vulnerable to the subjectivity of operators， resulting in the mismatch between treatments and operation anomalies， and the lack of guarantee of information security for the interaction process. Therefore， a blockchain-based network model for distribution network exception management， Exception Management Blockchain Network （EMBN）， was proposed， as well as an improved three-line defense model for distribution network. Firstly， according to the tamper-proof and traceable characteristics of blockchain， an Anomaly Index Blockchain （AIB） was constructed， and appropriate treatments were found to solve operation anomalies based on the latest information in the block. Secondly， an Exception Interact Blockchain （EIB） was constructed to monitor the interaction process of operation anomalies and treatments， and ensure the implementation of treatments. Finally， the EMBN was applied to the three-lines of defense in traditional distribution network， and the intelligent contract was combined to realize adaptive detection and anomaly response of the distribution network. Simulation results show that， facing the complicated distribution network environment， EMBN can match treatments and operation anomalies without the influence by subjectivity of operators； compared with the traditional distribution network， EMBN has the advantage in the information security of information interaction.

To enhance the integrity and security of cloud data storage， a data storage scheme based on hybrid algorithm blockchain and a decentralized framework integrating identity authentication and privacy protection were proposed in Wireless Sensor Network （WSN）. Firstly， the collected information was transmitted to the base station by the cluster heads， and all the key parameters were recorded on the distributed blockchain and transmitted to the cloud storage by the base station. Then， in order to obtain a higher security level， the 160-bit key of Elliptic Curve Cryptography （ECC） and the 128-bit key of Advanced Encryption Standard （AES） were combined， and the key pairs were exchanged between the cloud storage layers. The proposed blockchain is based on a hybrid algorithm and combined with an identity verification scheme， which can well ensure the secure storage of cloud data， thus achieving excellent security. In addition， malicious nodes were able to be directly removed from the blockchain and also their authentication was able to be revoked through the base stations. And this operation is convenient and fast. Simulation results show that compared with schemes of decentralized Blockchain Information Management （BIM） scheme， secure localization algorithm based on trust and Decentralized Blockchain Evaluation （DBE） and Key Derivation Encryption and Data Analysis （KDE-DA） management scheme， the proposed scheme has some advantages in delay， throughput and computational overhead.

Aiming at the problem that Protein-Protein Interaction (PPI) network functional module mining method based on spectral clustering and Fuzzy C-Means (FCM) clustering has low accuracy and low running efficiency, and is susceptible to false positive, a method for Functional Module mining in uncertain PPI network based on Fuzzy Spectral Clustering (FSC-FM) was proposed. Firstly, in order to overcome the effect of false positives, an uncertain PPI network was constructed, in which every protein-protein interaction was endowed with a existence probability measure by using edge aggregation coefficient. Secondly, based on edge aggregation coefficient and flow distance, the similarity calculation of spectral clustering was modified using Flow distance of Edge Clustering coefficient (FEC) strategy to overcome the sensitivity problem of the spectral clustering to the scaling parameters. Then the spectral clustering algorithm was used to preprocess the uncertain PPI network data, reducing the dimension of the data and improving the accuracy of clustering. Thirdly, Density-based Probability Center Selection (DPCS) strategy was designed to solve the problem that FCM algorithm was sensitive to the initial cluster center and clustering numbers, and the processed PPI data was clustered by using FCM algorithm to improve the running efficiency and sensitivity of the clustering. Finally, the mined functional module was filtered by Edge-Expected Density (EED) strategy. Experiments on yeast DIP dataset show that, compared with Detecting protein Complexes based on Uncertain graph model (DCU) algorithm, FSC-FM has F-measure increased by 27.92%, running efficiency increased by 27.92%; compared with an uncertain model-based approach for identifying Dynamic protein Complexes in Uncertain protein-protein interaction Networks (CDUN), Evolutionary Algorithm (EA) and Medical Gene or Protein Prediction Algorithm (MGPPA), FSC-FM also has higher F-measure and running efficiency. The experimental results show that FSC-FM is suitable for the functional module mining in the uncertain PPI network.

A control law based on Function Sliding Mode Controller (FSMC) was proposed for trajectory tracking control of manipulator. The uncertainties of the system were achieved from dynamic model and sliding mode function. Then RBF neural network was used to approach uncertainties of the system. Because of the approximation error of neural network, especially at the initial phase, the function sliding mode controller and robust compensator were designed for error compensation of neural network. The function sliding mode controller was capable of overcoming chattering problem of common Sliding Mode Control (SMC), and improved the tracking ability of the system. The global stability of closed loop system was proved based on Lyapunov theory, the effectiveness of proposed control approach was also demonstrated by simulation results.

Concerning the deficiency that the study on elliptic curve cryptosystem over Optimal Extension Fields (OEF) mainly focuses on the operation about addition, subtraction, multiplication and inverse of field's element, the preparative base point method was presented and a simple algorithm of computing the order on elliptic curve was designed. Making use of the methods and the algorithm, a fast generating algorithm of secure elliptic curve over optimal extension fields was implemented on general PC. Experiments show that the efficient is preferable.