Aiming at the problem of excessive time overhead of the Extended Isolation Forest (EIF) algorithm, a new algorithm named Extended Isolation Forest based on Random Subspace (RS-EIF) was proposed. Firstly, multiple random subspaces were determined in the original data space. Then, in each random subspace, the extended isolated tree was constructed by calculating the intercept vector and slope of each node, and multiple extended isolated trees were integrated into a subspace extended isolation forest. Finally, the average traversal depth of data point in the extended isolation forest was calculated to determine whether the data point was abnormal. Experimental results on 9 real datasets in Outliter Detection DataSet (ODDS) and 7 synthetic datasets with multivariate distribution show that, the RS-EIF algorithm is sensitive to local anomalies and reduces the time overhead by about 60% compared with the EIF algorithm; on the ODDS datasets with many samples, its recognition accuracy is 2 percentage points to 12 percentage points higher than those of the isolation Forest (iForest) algorithm, Lightweight On-line Detection of Anomalies (LODA) algorithm and COPula-based Outlier Detection (COPOD) algorithm. The RS-EIF algorithm has the higher recognition efficiency in the dataset with a large number of samples.

Aiming at the difference between the clustering effects of wavelet clustering algorithm under different grid division scales, an improved method based on peak grid was proposed. The algorithm mainly aimed at improving the detection method of connected regions in wavelet clustering. First, the spatial grids after wavelet transform were sorted according to the grid values; then, the breadth-first-search method was used to traverse each spatial grid to detect the peak connected regions in the data after wavelet transform; finally, the connected regions were marked and mapped to the original data space to obtain the clustering result. Experimental results of 8 synthetic datasets(4 convex datasets and 4 non-convex datasets) and 2 real datasets in the UCI database showed that the improved algorithm had good performance at low grid division scales, and compared with the original wavelet clustering algorithm, this algorithm had the requirement for grid division scale reduced by 25% to 60%, and the clustering time reduced by 14% under the same clustering effect.

For Diabetic Retinopathy (DR), the image resolution is too high, the lesion features are too scattered to obtain, and the positive, negative, hard and easy samples are imbalanced, thus the DR staging accuracy cannot be effectively improved. Therefore, a DR staging method based on the combination of improved Faster Region-based Convolutional Neural Network (Faster R-CNN) and subgraph segmentation was proposed. First, subgraph segmentation was used to solve the interference problem of the optic disc region to lesion recognition. Second, a deep residual network was used in the feature extraction process to solve the problem of difficulty of obtaining features due to the small proportion of the lesions in the high-resolution fundus image. Finally, the Online Hard Example Mining (OHEM) method was used to solve the problem of imbalance between positive, negative, hard and easy samples during the generation of Region of Interest (ROI). In the DR staging experiments on EyePACS, an internationally open dataset, the accuracy of the proposed method in DR staging reached 94.83% in stage 0, 86.84% in stage 1, 94.00% in stage 2, 87.21% in stage 3 and 82.96% in phase 4. Experimental results show that the improved Faster R-CNN can efficiently stage DR images and automatically label the lesions.

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.

As the built-in scheduling strategy of Docker swarm cannot implement load balance of cluster very well and the utilization rate of cluster resource is not very high, a dynamic weighted scheduling algorithm was proposed. The weight coefficient was set on the resource, and the parameter bias was introduced to dynamically adjust the resource weight for different services. According to the actual resource utilization of each node, the node weight was calculated to reflect node load, and was used for scheduling. Compared with the original Docker scheduling strategy and the weighted scheduling strategy without parameter adjustment, the proposed algorithm makes all the resource utilization of each node in the cluster more balanced. At the same time, the proposed algorithm can achieve faster service running speed under the condition of high cluster load.

A large amount of alarms considerably complicate the root-cause analysis in telecom networks, thus a new alarm filtering algorithm was proposed to minimize the interference on the analysis. Firstly, a quantitative analysis for the alarm data, e.g., the quantity distribution and the average duration, was conducted, and the concepts of alarm impact and high-frequency transient alarm were defined. Subsequently, the importance of each alarm instance was evaluated from four perspectives:the amount of the alarms, the average duration of the alarms, the alarm impact, and the average duration of the alarm instance. Accordingly, an alarm filtering algorithm with O ( n) computation complexity in principle was proposed, where n is the number of alarms under analysis. Single-factor experimental analysis show that the compression ratio of the alarm data has a positive correlation with the alarm amount of a specific alarm element, the average duration of the alarms, the alarm impact, and the duration of the alarm instance; further, the accuracy of the proposed algorithm is improved by 18 percentage points at most compared with Flexible Transient Flapping Determination (FTD) algorithm. The proposed algorithm can be used both for off-line analysis of historical alarm data and for on-line alarm filtering.

The Automated Trust Negotiation (ATN) Model based on Interleaved Spiral Matrix Encryption (ISME) was proposed for the protection of sensitive information in the automated trust negotiation. The interleaved spiral matrix encryption and policy migration were used in the model to protect three kinds of sensitive information of negotiation. Compared with the traditional spiral matrix encryption algorithm, the concept of odd-even bit and triple were added into the interleaved spiral matrix encryption algorithm. In order to make the model adapt the application better, the concept of key attributes flag was introduced in the certification of negotiations, and thus it recorded the sensitive information which corresponded to the encrypted key effectively. Meanwhile, how to represent the negotiation rules through encryption function was listed in the negotiation model. To increase efficiency and success rate of the model, the 0-1 graph policy parity algorithm was proposed. The decomposition rules of six basic propositions were constructed by directed graph of graph theory in the 0-1 graph policy parity algorithm. The propositions abstracted by the access control policies could be determined effectively and the reliability and completeness was testified to prove the equivalence of semantics concept and syntax concept in logistic system. Finally, the simulation results demonstrate that the model of the average number of disclosure strategy is 15.2 less than the traditional model in 20 negotiations. The successful rate of the negotiation is increased by 21.7% and the efficiency of the negotiation is increased by 3.6%.

The existing passenger flow counting sensors with PolyVinyliDene Fluoride (PVDF) piezoelectric material exist the lack of accuracy caused by erroneous counting and missing counting, which has characteristics of low cost and resistance to wear and tear. In order to solve the problem, a passenger counting system based on PVDF gait intelligent detection technology was proposed. The ANSYS software was applied to carry out stress analysis of passengers' gait stepping on and off the bus and observe the distribution of the PVDF piezoelectric signal. The multi-input signal conditioning circuit was designed to acquire multi-channel plantar signal. Combined with signal processing algorithm, the sensor mechanical structure and people-counting system on buses were introduced by Laboratory Virtual Instrument Engineering Workbench (LabVIEW).The experimental results indicate that the proposed system improves the precision in comparison with the existing PVDF passenger flow counting sensors, reduces the cost in comparison with video image counting and human body infrared detection technology, and the average counting error is 5.3%.The proposed system has high practicality and can be widely used in Chinese public transport buses.

The authentication and key agreement protocol adopted by 3rd Generation Partnership Project (3GPP) System Architecture Evolution (SAE) Release 8 standard was analyzed in contrast with 3G, and several security defects in SAE protocol were pointed out, then an improved 3GPP SAE authentication and key agreement protocol was put forward based on public key cryptosystem. In the new protocol, user’s identity information and authentication vector in network domain were encrypted based on public key cryptosystem, public parent key adopted in local authentication was generated by random data. The security and efficiency of the proposed new scheme was analyzed at last. The analysis results show that the proposal can effectively solve the problems mentioned above and improve the security of protocol with less cost.