Aiming at the problems of low accuracy and poor real-time performance of Noctiluca scintillans red tide extraction in the field of satellite remote sensing， a Noctiluca scintillans red tide extraction method from Unmanned Aerial Vehicle （UAV） images based on deep learning was proposed. Firstly， the high-resolution RGB （Red-Green-Blue） videos collected by UAV were used as the monitoring data， the backbone network was modified to VGG-16 （Visual Geometry Group-16） and the spatial dropout strategy was introduced on the basis of the original UNet++ network to enhance the feature extraction ability and prevent the overfitting respectively. Then， the VGG-16 network pre-trained by using ImageNet dataset was applied to perform transfer learning to increase the network convergence speed. Finally， in order to evaluate the performance of the proposed method， experiments were conducted on the self-built red tide dataset Redtide-DB. The Overall Accuracy （OA）， F1 score， and Kappa of the Noctiluca scintillans red tide extraction of the proposed method are up to 94.63%， 0.955 2， 0.949 6 respectively， which are better than those of three traditional machine learning methods — K-Nearest Neighbors （KNN）， Support Vector Machine （SVM） and Random Forest （RF） as well as three typical semantic segmentation networks （PSPNet （Pyramid Scene Parsing Network）， SegNet and U-Net）. Meanwhile， the red tide images of different shooting equipment and shooting environments were used to test the generalization ability of the proposed method， and the corresponding OA， F1 score and Kappa are 97.41%， 0.965 9 and 0.938 2， respectively， proving that the proposed method has a certain generalization ability. Experimental results show that the proposed method can realize the automatic accurate Noctiluca scintillans red tide extraction in complex environments， and provides a reference for Noctiluca scintillans red tide monitoring and research work.

In order to improve the driving efficiency of intersections with waiting areas, the effect of setting waiting area was firstly equal to the increase of lane green ratio. Then a signal timing optimization model for intersection was developed based on National Electronic Manufacturers Association (NEMA) standard dual-ring phase with the objective of minimizing the average vehicular delay. Next, a genetic algorithm for solving the model was designed by considering the ring-barrier constraint in phase structure. Finally, the model and algorithm were applied to the example intersection. The results show that compared to the signal timing scheme obtained by Synchro software, the model can obtain the scheme with shorter cycle and lower average vehicular delay. The delay reduction of the proposed model ranges from 12.9% to 17.4% when only left-turn waiting areas are provided at the intersections, and from 17.5% to 25.5% when both left-turn and through-movement waiting areas are provided. Besides, the model is not sensitive to the value of queue clearance rate, and can obtain almost the same signal timing scheme at the minimum and maximum vehicular speeds.

Focusing on the content distribution acceleration problem in Mobile Edge Computing (MEC), with the consideration of the influence of MEC server storage space limitation on content cache, with the object obtaining delays of the mobile users as optimization goal, an Interest-based Content Distribution Acceleration Strategy (ICDAS) was proposed. Considering the MEC server storage space, the interests of the mobile user groups on different objects and the file sizes of the objects, the objects were selectively cached on MEC servers, and the objects cached on MEC servers were timely updated in order to meet the content requirements of mobile user groups as more as possible. The experimental results show that the proposed strategy has good convergence performance, which cache hit ratio is relatively stable and significantly better than that of the existing strategies. When the system runs stably, compared with the existing strategies, this strategy can reduce the object data obtaining delay for users by 20%.

The traditional point-to-point saturation attack is not ideal choice facing high-density and multi-azimuth swarming intelligence targets. The maximum killing effect with weapon number less than target number can be achieved by selecting the appropriate types of weapons and the location of aiming points to realize the fire coverage. Considering the operational requirements of security targets, damage threshold and preference assignment, the Constrained Multi-objective Weapon-Target Assignment (CMWTA) mathematical model was established at first. Then, the calculation method of the constraint violation value was designed, and the individual coding, detection and repair as well as constraint domination were fused to deal with multiple constraints. Finally, the convergence metric for multi-objective weapon-target assignment model was designed, and the approaches were verified by the frameworks of Multi-Objective Evolutionary Algorithm (MOEA). In the comparison of three MOEA frameworks, the capacity of the Pareto sets of SPEA2 (Strength Pareto Evolutionary Algorithm Ⅱ) is mainly distributed in [21,25], that of NSGA-Ⅱ (Non-dominated Sorting Genetic Algorithm Ⅱ) is mainly distributed in [16,20], and that of MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition) is less than 16. In the verification of the repair algorithm, the algorithm makes the convergence metrics of three MOEA frameworks increased by 20 %, and the proportion of infeasible non-dominated solutions in Pareto solution set of 0%. The experimental results show that SPEA2 outperforms NSGA-Ⅱ and MOEA/D on distribution and convergence metric in solving CMWTA model, and the proposed repair algorithm improves the efficiency of solving feasible non-dominated solutions.

Considering the problems of low-enthusiasm workers and task expiration in the mobile crowd sensing system, a task assignment algorithm based on initial cost and soft time window was proposed. As the corresponding task assignment problem belongs to the category of NP-hard problems and the computationally efficient optimal algorithm cannot be found, thus, an algorithm was developed based on Discrete Cuckoo Search Algorithm (DCSA). Firstly, the corresponding global search process and local search process were designed respectively according to the problem characteristics. Secondly, to derive the better solution, the priorities of tasks with respect to the distance between tasks and workers' starting positions as well as the size of time windows were analyzed. Finally, feasible operations were executed to guarantee that the related constraints were satisfied by each task assignment. Compared with genetic algorithm and greedy algorithm, the simulation results show that DCSA-based task assignment algorithm can improve the enthusiasm of workers to participate, solve the problem of task expiration, and ultimately reduce the total system cost.

To solve the problem that Markov model has poor prediction accuracy and sparse matching in location prediction, a mobile user location prediction method based on Adaboost-Markov model was proposed. Firstly, the original trajectory data was preprocessed by a trajectory division method based on angle offset and distance offset to extract feature points, and density clustering algorithm was used to cluster the feature points into interest regions of the user, then the original trajectory data was discretized into a trajectory sequence composed of interest regions. Secondly, according to the matching degree of prefix trajectory sequence and historical trajectory pattern tree, the model order k was adaptively determined. Finally, Adaboost algorithm was used to assign the corresponding weight coefficients according to the importance degree of 1 to k order Markov models to form a multi-order fusion Markov model, realizing the prediction of future interest regions of the mobile user. The experimental results on a large-scale real user trajectory dataset show that the average prediction accuracy of Adaboost-Markov model is improved by 20.83%, 11.3%, and 5.38% respectively compared with the first-order Markov model, the second-order Markov model, and the multi-order fusion Markov model with average weight coefficient, and the proposed model has good universality and multi-step prediction performance.

Aiming at the safety verification problem of a class of stochastic continuous system equipped with both random initial state and stochastic differential equation, a new computation method based on stochastic barrier certificates and initial set selection was proposed. Firstly, the related knowledge and concepts of stochastic continuous system and its safety verification were introduced. Then, it was discussed that how to determine the initial state set for the initial variables obeying several different distributions. The safety verification problem was converted into the polynomial optimization problem by using the method of stochastic barrier certificates according to the selected initial state set. Finally, the sum of squares relaxation method was used to transform the problem into sum of squares programming problem, and the lower bound of safety probability was obtained by using the SOSTOOLS tool. The theoretical analysis and experimental results show that, the proposed method has the complexity of polynomial time and can effectively compute the lower bound of safety probability for stochastic continuous system in unbounded time.

Aiming at the specific application scenario of aircraft detection in large-scale satellite images of military airports, a real-time target detection and recognition framework was proposed. The deep Convolutional Neural Network (CNN) was applied to the target detection task and recognition task of aircraft in large-scale satellite images. Firstly, the task of aircraft detection was regarded as a regression problem of the spatially independent bounding-box, and a 24-layer convolutional neural network model was used to complete the bounding-box prediction. Then, an image classification network was used to complete the classification task of the target slices. The traditional target detection and recognition algorithm on large-scale images is usually difficult to make a breakthrough in time efficiency. The proposed target detection and recognition framework of aircraft based on CNN makes full use of the advantages of computing hardware greatly and shortens the executing time. The proposed framework was tested on a self-collected data set consistent with application scenarios. The average time of the proposed framework is 5.765 s for processing each input image, meanwhile, the precision is 79.2% at the operating point with the recall of 65.1%. The average time of the classification network is 0.972 s for each image and the Top-1 error rate is 13%. The proposed framework provides a new solution for application problem of aircraft detection in large-scale satellite images of military airports with relatively high efficiency and precision.

Focused on the issues that circular domain extraction is not accurate and effective correction field angle can not reach 180 degrees in the vertical direction, Variable Angle Line Scan (VALS) method and Longitudinal Compression Cylindrical Projection (LCCP) method were proposed respectively. By changing the inclination angle of the scan line, the VALS method got coordinates of those cut points, then it filtered out invalid cut points coordinates and further got the parameters of the circular domain by using the Kasa circle fitting method. As for the LCCP method, it artificially bended the optical path of traditional cylindrical projection so that the light projected onto the infinity point could be projected back on the cylindrical surface, thus preserved the image information effectively. The comparison with two known methods named longitude-latitude mapping and Mercator mapping proves the effectiveness of the proposed algorithm in weakening the blurring effect due to stretching caused by the edge of image correction. The result looks more nature.

Group key agreement is one of the important stages to carry out secure multicast communication. A group controller node switch method was given pointing to the problem of satellite network topology changed dynamically. It could adjust controlling nodes in a dynamic way. Then, both authentication and integrality mechanism were used to attest agreement messages and group keys, a group key generation and renewing method was proposed, which could improve security of agreement messages. The results of simulation and analysis show that this group key agreement protocol leads to high efficiency and security.

Mobility model is the basis of protocol design and evaluation for mobile Ad Hoc network. A multiple states entity random mobility model was proposed according to the requirements of entity mobility modeling, which could reflect the realistic node movement and had more controllable parameters. Several familiar mobility models were derived from it by adjusting some parameters. The mobility model could be applied to simulation of mobile Ad Hoc network for its flexibility and versatility.

Peer-to-peer (P2P) applications generate a large volume of traffic and seriously affect quality of normal network services. Accurate and real-time identification of P2P traffic is important for network management. A hybrid approach consists of three sub-methods was proposed to identify BitTorrent (BT) traffic. It applied application signatures to identify unencrypted traffic. And for those encrypted flows, message-based method according to the features of the message stream encryption (MSE) protocol was proposed. And a pre-identification method based on signaling analysis was applied to predict BT flows and distinguish them even at the first packet with SYN flag. And some modified Vuze clients were used to label BT traffic in real traffic traces, which made high accuracy benchmark datasets to evaluate the hybrid approach. The results illustrate its effectiveness, especially for those un- or semi- established flows, which have no obvious signatures or flow statistics.

In the hierarchy model of ontology conceptual model, the semantic similarity between two ontology concepts is in inverse proportion to the semantic distance in between. At the same time, the closer the concepts are to the bottom in the hierarchy model, the more information they describe. Therefore, if the deeper the nearest shared ancestor is, the more similar the two concepts are. Considering these two factors, two heuristic rules and the calculating formula were constructed. And the formula was proved reasonable by analyzing an ontology instance. In the formula, there were two experimental parameters, that were decided by the depth of the hierarchy model of ontology. According to the depth at present, the reasonable values of two experimental parameters were given.

According to the E-Government documents characters,the weight of the terms in documents and queries were calculated based on E-Government thesaurus.Similarity between query and documents was obtained through computing the similarity between weigh terms of documents and query,and most matching documents were provided.At the same time E-Government document metadata is organized in semantic web.E-Government document can also be retrieved as searching for metadata in semantic web.The documents retrieved is approached in semantic web with metadata to benefit logic reasoning.