To address the issues of difficult prediction of landslide displacement and difficulty in selecting influencing factors， a model combining Double Moving Average （DMA）， Variational Modal Decomposition （VMD）， Improved Gray Wolf Optimizer （IGWO） algorithm and Support Vector Regression （SVR） was proposed for landslide displacement prediction. Firstly， DMA was used to extract the trend and periodic terms of landslide displacement， and polynomial fitting was used to predict the trend term. Secondly， the influencing factors of the landslide periodic term were classified， and VMD was used to decompose the original factor sequence to obtain the optimal sequence. Then， a grey wolf optimizer algorithm combining SVR with an improved Circle-based multi-tactic， called CTGWO-SVR （Circle Tactics Grey Wolf Optimizer with SVR）， was proposed to predict the landslide periodic term. Finally， the cumulative displacement prediction sequence was obtained using a time series additive model， and the model was evaluated using post validation difference verification and small probability error in grey prediction. Experimental results show that compared with GA （Genetic Algorithm）-SVR and GWO-SVR models， CTGWO-SVR has higher prediction accuracy with a fitting degree of 0.979， and the Root Mean Square Error （RMSE） reduces by 51.47% and 59.25%， respectively. The model evaluation accuracy is level one， which can meet the real-time and accuracy requirements of landslide prediction.

Concerning the secondary attack problem of virus in cloud computing， data center and other virtual network-based environments， the virus propagation and immune mechanism under the background of dynamic platform defense was studied， and a heterogeneous backup based network virus defense method was proposed. Firstly， the process of secondary attack of redundant backup was analyzed， and the law of virus action was summarized. At the same time， combined with the idea of dynamic platform defense， the heterogeneous platform state node was introduced， and a Susceptible-Escaped-Infected-Removed-Heterogeneous-Susceptible （SEIRHS） virus propagation model was proposed. Secondly， the local stability at the equilibrium point of the model was proved by using the Routh-Hurwitz stability criterion， and the basic reproductive number was solved. Finally， the proposed model was compared with the traditional Susceptible-Infected-Removed （SIR） and Susceptible-Escaped-Infected-Removed （SEIR） models through simulation analysis， the stability of the model was verified， and the effect of virus propagation influencing factors on virus spread scale was discussed. The simulation results show that the proposed model can objectively reflect the propagation law of virus in the network， and effectively improve the network’s defense effect against the virus by reducing the node degree， increasing the Infected-Heterogeneous （I-H） state transition probability， and reducing the probability of being hidden by the virus during backup， etc.

Aiming at the problems of slow detection speed， low precision， missed detection and false detection of current forest pest detection methods， a forest pest detection method based on attention model and lightweight YOLOv4 was proposed. Firstly， a dataset was constructed and preprocessed by using geometric transformation， random color dithering and mosaic data augmentation techniques. Secondly， the backbone network of YOLOv4 was replaced with a lightweight network MobileNetV3， and the Convolutional Block Attention Module （CBAM） was added to the improved Path Aggregation Network （PANet） to build the improved lightweight YOLOv4 network. Thirdly， Focal Loss was introduced to optimize the loss function of the YOLOv4 network model. Finally， the preprocessed dataset was input into the improved network model， and the detection results containing pest species and location information were output. Experimental results show that all the improvements of the network contribute to the performance improvement of the model； compared with the original YOLOv4 model， the proposed model has faster detection speed and higher detection mean Average Precision （mAP）， and effectively solves the problem of missed detection and false detection. The proposed new model is superior to the existing mainstream network models and can meet the precision and speed requirements of real?time detection of forest pests.

Aiming at the problems of large model parameters， high computational complexity and low accuracy of traditional violence detection methods， a method of violence detection in video based on temporal attention mechanism and EfficientNet was proposed. Firstly， the foreground image obtained by preprocessing the dataset was input to the network model to extract the video features， meanwhile， the frame-level spatial features of violence were extracted by using the lightweight EfficientNet， and the global spatial-temporal features of the video sequence were further extracted by using the Convolutional Long Short-Term Memory （ConvLSTM） network. Then， combined with temporal attention mechanism， the video-level feature representations were obtained. Finally， the video-level feature representations were mapped to the classification space， and the Softmax classifier was used to classify the video violence and output the detection results， realizing the violence detection of video. Experimental results show that the proposed method can decrease the number of model parameters， reduce the computational complexity， increase the accuracy of violence detection and improve the comprehensive performance of the model with limited resources.

Current research on Person Re-Identification （Re-ID） mainly concentrates on short-term situations with person’s clothing usually unchanged. However， more common practical cases are long-term situations， in which a person has higher possibility to change his clothes， which should be considered by Re-ID models. Therefore， a method of person re-identification with cloth changing based on joint loss capsule network was proposed. The proposed method was based on ReIDCaps， a capsule network for cloth-changing person re-identification. In the method， vector-neuron capsules that contain more information than traditional scalar neurons were used. The length of the vector-neuron capsule was used to represent the identity information of the person， and the direction of the capsule was used to represent the clothing information of the person. Soft Embedding Attention （SEA） was used to avoid the model over-fitting. Feature Sparse Representation （FSR） mechanism was adopted to extract discriminative features. The joint loss of label smoothing regularization cross-entropy loss and Circle Loss was added to improve the generalization ability and robustness of the model. Experimental results on three datasets including Celeb-reID， Celeb-reID-light and NKUP prove that the proposed method has certain advantages compared with the existing person re-identification methods.

Due to low error checking rate of Web application test, a method of test case generation for Web applications based on state transition was proposed. By constructing state transition diagram of pages, event transition table and navigation transition table, the link relationship of Web applications was shown. This approach generated test path from state transition tree of pages got from state transition diagram of pages. Based on equivalence partitioning principles, a coverage criteria was proposed, then a test case set was reported as result combined with information from event transition table and navigation transition table. The result shows that the proposed method can represent link relationship of Web applications effectively, and improve error checking rate of test case.

In order to support the distributed transmission of a lot of tasks on the data exchange platform for civil aviation information, it needs to establish the efficient task scheduling algorithms and models. Based on the infrastructure and needs of the platform, after analyzing the existing task scheduling models and scheduling algorithms, a new task scheduling model was proposed to fulfill the data exchange on this platform. This model mapped the point-to-multipoint data transmission network to a Steiner tree problem with delay and bandwidth constraints, and an improved Genetic Algorithm (GA) was also proposed to solve the constrained Steiner tree problem. The results of comparative experiment with the maximum bandwidth allocation algorithm prove the validity and feasibility of the proposed model.

Concerning the issue of route-transition by helicopter towing arming of mine-hunting and mine-sweeping, three patterns of route-transition were put forward. Through analyzing the swerve characteristics of helicopter and the real movement of underwater towed cable system, the kinematic model of swerving process was established, the complexity of fluid dynamics was avoided and the calculation rate was advanced. Finally, the correctness was testified by examples of three flight states by helicopter towing arming of mine-hunting and mine-sweeping. Through increasing velocity of helicopter, the underwater towed cable system was fast close to the beeline sea-lane, the result was in accordance with the real movement. The trajectory of swerving process of route-transition by helicopter towing arming of mine-hunting and mine-sweeping was truly reflected, and the result can be used for decision-making of route optimization and operation scheme.

Concerning the time-consuming and computational complexity in extracting face features of traditional Gabor filters, the face features were extracted by using three different local Gabor filters adaptively chosen by the Gabor images' energy from different directions, scales and overall situation. Firstly, the Gabor features of some images in the face database were extracted and analyzed, and the local Gabor filters were built by choosing the filters corresponding to the images with larger energy. And then, the Fisher features were extracted using Linear Discriminate Analysis (LDA) further. Finally, face recognition was realized using the nearest neighbor method. The experimental results based on ORL and YALE face database show that the proposed approach has better face recognition performance with less feature dimension and calculation time.

This paper studied the spectrum sensing algorithms mainly from the perspective of multiple cognitive radio users. At present, most decision rules do not take the influence of the trust of spectrum sensing results into consideration. This paper proposed a new sending scheme based on index belief degree function. This scheme is a method of low complexity to improve the performance of spectrum sensing. Considering the security in cognitive radio network, this paper presented a cognitive radio spectrum algorithm based on outlier. This algorithm introduced outlier sensing to the fusion rules and this scheme could improve the robustness of spectrum sensing when some sensing nodes were malfunctioned or malicious. Considering the speed and precision of spectrum sensing in cognitive radio, this paper considered the sensing information to the spectrum sensing algorithm. This algorithm can improve the detection performance of spectrum sensing while accelerating the speed of spectrum sensing.

To solve the problem of the test data set generation in combinatorial test, if the software under test has a great many factors and values, the traditional Particle Swarm Optimization (PSO)will have large iteration times and slow convergence velocity. A test data set generation method based on K-means clustering algorithm and PSO has been proposed. The polymorphism of the test data set has been enhanced, though clustering and partitioning the test data set. And it makes PSO has been improved. The compact between the particles in each area has been promoted. Several typical cases show that this method has some merits while ensuring the coverage.

The existing methods of malware feature selection and reduction methods were studied. Current attribute reduction methods of malware do not take advantage of the information of feature selection evaluation function. So a method was proposed to order all features based on their value of information gain and their size, and used attributes order reduction method to get a reduction. An analysis of spatial and temporal complexity was given, and the overall design was given. Test results show that the application of attributes order reduction can obtain fewer reduction results in less time, and get higher classification accuracy using the reduction result.

According to the urgent characteristics of casualty evacuation, this paper established a casualty evacuation mathematical model of multidisaster places, multihospital and multipatients with the goal of the shortest time. Considering the hospital resource competition of patients, Operation Table Method (OTM) was used to solve the model and realize the goal of the shortest time. The method has been realized on the provincial level information demonstration platform of emergency management, and can deal with different injury types and the patient priorities. With this platform and Geographic Information System (GIS) technology, the method is simulated and the effect is good.

Now, Internet Explorer is the most popular client software which malwares often use. In allusion to such threats to Internet Explorer, the characters of Web attack were analyzed, and a malwebpage detection system based on honeyclient was designed, in which spider was combined with honeypot. In the system, spider was used to collect source of urls, then clientengine automatically created Internet Explorer processes, and devicedriven detector was used to detect malwares coming through Internet Explorer. In the end, the malicious webpages url was added to the black list and the malware database was enlarged.