An improved Weighted One Class Support Vector Machine (WOCSVM) algorithm—Local Density WOCSVM (LD-WOCSVM) was proposed to solve the problems of poor classification performance and weak robustness of the data-based turboshaft engine fault detection algorithm. Firstly, for each training sample, k nearest neighbor samples contained in the body of the ball were selected, and the ball was centered on this sample with a radius of 2% of the Mahalanobis distance from the center of all training samples to the farthest samples. Secondly, the distance from this sample to the center of selected k training samples was used to evaluate the probability that this sample is a fault sample, and based on this, the normalized distance was used to calculate the weight of the corresponding sample. An algorithm of weight calculation based on rapid clustering namely FCLD-WOCSVM was proposed to deal with the problem that the present algorithms were not able to reflect the characteristics of sample distribution very well. In this algorithm, by obtaining two parameters of the local density of each training sample and the distance from the sample to the high local density, the distribution position of this sample was determined, and the weight of the sample was calculated by using the two obtained parameters. The classification performance of both algorithms was improved by assigning small weights to the possible fault samples. In order to verify the effectiveness of the two algorithms, simulation experiments were carried out on 4 UCI datasets and T700 turboshaft engines respectively. Experimental results show that, compared with Adaptive WOCSVM (A-WOCSVM) algorithm, LD-WOCSVM algorithm improves the AUC （Area Under the Curve） value by 0.5%, and FCLD-WOCSVM algorithm improves the G-mean （Geometric mean） by 12.1%. These two algorithms can be used as candidate algorithms for turboshaft engine fault detection.

Due to the complexity and dynamic behavior in virtual environment, the efficiency is low when adopting traditional methods to prove the secure state of virtual machines. Ring signature has high computational efficiency and strong anonymity, so the the key management can be solved by using the certificateless public key system. A trustworthiness attestation scheme which adopted certificateless ring signature scheme in Virtual Machine (VM) was put forward. After the trusted physical environment of virtual platform was validated by the Private Key Generator (PKG), the virtual Trusted Platform Module (vTPM) signature key was generated by PKG and vTPM manager using certificateless signature algorithm, and the ring signature was employed by VM to perform remote attestation and hide attestor's identity in ring members, which realized the attestation of VM's anonymous identity and state. After completion of the proof preparation, the VM does not need to generate virtual Attestation Identity Key (vAIK) certificates repeatedly in the process of attestation and migration, thus greatly improving the efficiency of attestation. Consequently, the proposed scheme has strong security and anonymity, and it is suitable for the cloud computing environment with huge numbers of VMs.

In real-time operation state analysis of power grid, in order to meet the requirements of real-time analysis and processing of large-scale real-time data, such as real-time electricity consumption data, and provide fast and accurate data analysis support for power grid operation decision, the system architecture for large-scale data analysis and processing based on stream computing and memory computing was proposed. The Discrete Fourier Transform (DFT) was used to construct abnormal electricity behavior evaluation index based on the real-time electricity consumption data of the users by time window. The K-Means clustering algorithm was used to classify the users' electricity behavior based on the characteristics of user electricity behavior constructed by sampling statistical analysis. The accuracy of the proposed evaluation indicators of abnormal behavior and user electricity behavior was verified by the experimental data extracted from the actual business system. At the same time, compared with the traditional data processing strategy, the system architecture combined with stream computing and memory computing has good performance in large-scale data analysis and processing.

Focusing on low counting accuracy caused by serious occlusions and abrupt illumination variations, a new real-time statistical method based on Gaussian Mixture Model (GMM) and Scale-Invariant Feature Transform (SIFT) features for video crowd counting was proposed. Firstly, the moving crowd were detected by using GMM-based motion segment method, and then the Gray Level Co Occurrence Matrix (GLCM) and morphological operations were applied to remove small moving objects of background and the dense noise in non-crowd foreground. Considering the high time-complexity of GMM algorithm, a novel parallel model with higher efficiency was proposed. Secondly, the SIFT feature points were acted as the basis of crowd statistics, and the execution time was reduced by using feature exaction based on binary image. Finally, a novel statistical analysis method based on crowd features and crowd number was proposed. The data sets with different level of crowd number were chosen to train and get the average feature number of a single person, and the pedestrians with different densities were counted in the experiment. The algorithm was accelerated by using multi-stream processors on Graphics Processing Unit (GPU) and the analysis about efficiently scheduling the tasks on Compute Unified Device Architecture (CUDA) streams in practical applications was conducted. The experimental results indicate that the speed is increased by 31.5% compared with single stream, by 71.8% compared with CPU.

To improve the safety of vehicles while driving, a computer vision-based inter-vehicle distance estimation and warning method was proposed in this paper. First, shadow detection method was applied to detect shadow of cars ahead, and inter-vehicle distance estimation function was built based on the distance between shadow and vision center of a frame. Then, estimation equations for non-threatened background optical flow was built, and by judging optical flow with the estimation equations, the abnormal objects could be separated from others, thus the overtaking event could be recognized. Based on the inter-vehicle distance and detection of overtaking event, the driver could be timely warned of the potential safety hazard. The experimental results prove that the proposed method can estimate inter-vehicle distance and detect overtaking event accurately. Finally, NVIDIA GeForce GTX680 GPU (Graphic Processing Unit) was used to accelerate the algorithm on Compute Unified Device Architecture (CUDA) platform and achieve the processing speed of 48.9 ms per frame which basically meets the real-time processing demand.

Concerning the output error model, order identification and parameter estimation were integrated and extended, and the determinant ratio method based on the auxiliary model was put forward. The system order and parameter estimation were got simultaneously with this method, so it could reduce calculation time in the process of system identification. Considering the inaccuracy of the determinant ratio method, a model validation method was proposed to enhance the identification ability. At last, the simulation indicates that the expended determinant ratio method effectively estimates the system order and parameters.

Vehicular network has the following characteristics such as nodes moving fast, topology changing rapidly. The direct use of Global Positioning System (GPS) devices causes large positioning error and low routing connectivity rate. Therefore, the packet delivery rate of the existing location-based routing algorithm is not high enough to provide reliable routing. A geographic routing algorithm based on anchor node in vehicle networks named Geographic Routing based on Anchor Nodes (GRAN) was proposed. Using street lamps as anchor nodes, a vehicle could locate itself through the anchor nodes. Combined with the road gateway and the central data, GRAN established a hierarchical routing structure, thus removing the steps of route discovery and the whole network broadcast. Thus, the routing overhead was reduced and the routing efficiency and the packet delivery rate were improved. By using the NS-2 software and selecting a realistic urban scene, a simulation was conducted on Greedy Perimeter Stateless Routing (GPSR), Graphic Source Routing (GSR) and GRAN. The experimental results show that GRAN can provide a lower average delay, higher packet delivery ratio and throughput at a lower load, compared with several typical location-based routing protocols.

In order to improve the spectral efficiency and outage performance of the cooperative communication system, a Two-Path Relaying (TPR) cooperative communications model based on the Decode-and-Forward (DF) protocol was proposed. Meanwhile, two data transmission modes were presented for the proposed system. The closed-form expressions for outage probability and the relations between diversity gain and multiplexing gain were derived. The simulation results show that, compared with the conventional two-relay DF protocol, the outage probability performance of the two proposed transmission models is greatly improved.

Dynamic trusted measurement is a hot and difficult research topic in trusted computing. Concerning the measurement difficulty invoked by the dynamic nature of operating system kernel, a Dynamic Trusted Kernel Measurement (DTKM) model was proposed. Dynamic Measurement Variable (DMV) was presented to describe and construct dynamic data objects and their relations, and the method of semantic constraint was proposed to measure the dynamic integrity of kernel components. In DTKM, the collection of memory data was implemented in real-time, and the dynamic integrity was verified by checking whether the constructed DMV was consistent with semantic constraints which were defined based on the security semantics. The nature analysis and application examples show that DTKM can effectively implement dynamic measurement of the kernel and detect the illegal modification of the kernel dynamic data.