Intrusion detection is one of the important means to ensure network security. To address the problem that it is difficult to balance detection accuracy and computational efficiency in network intrusion detection, based on the idea of deep metric learning, a network intrusion detection model combining improved Triplet Network (imTN) and K-Nearest Neighbor (KNN) was proposed, namely imTN-KNN. Firstly, a triplet network structure suitable for solving intrusion detection problems was designed to obtain the distance features that are more conducive to the subsequent classification. Secondly, due to the overfitting problem caused by removing the Batch Normalization (BN) layer from the traditional model which affected the detection precision, a Dropout layer and a Sigmoid activation layer were introduced to replace the BN layer, thus improving the model performance. Finally, the loss function of the traditional triplet network model was replaced with the multi-similarity loss function. In addition, the distance feature output of the imTN was used as the input of the KNN algorithm for retraining. Comparison experiments on the benchmark dataset IDS2018 show that compared with the Deep Neural Network based Intrusion Detection System (IDS-DNN) and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) based detection model, the detection accuracy of imTN-KNN is improved by 2.76% and 4.68% on Sub_DS3, and the computational efficiency is improved by 69.56% and 74.31%.

User association is the problem that a wireless terminal chooses to access one serving base station. User association can be seen as the first step in wireless resource management, which has an important impact on network performance, and plays a very important role in achieving load balance, interference control, improvement of spectrum and energy efficiency. Aiming at the characteristics of multi-layer heterogeneous network including macro base stations and full-duplex small base stations, a separate multiple access mechanism was considered, which means allowing a terminal access different and multiple base stations in the uplink and downlink, so as to realize the performance improvement. On this basis, the user association problem with separation of uplink and downlink multi-access in heterogeneous network was modeled into an evolutionary game problem, in which the users act as the players to perform the resource competition with each other, the access choices of base stations are strategies in the game, and every user wants to obtain the maximum of own effectiveness by the choice of strategy. Besides, a low-complex self-organized user association algorithm was designed based on evolutionary game and reinforcement learning. In the algorithm, the user was able to adjust the strategy according to the revenue of current strategy choice, and reached an equilibrium state in the end, realizing user fairness. Finally, extensive simulations were performed to verify the effectiveness of the proposed method.

To realize automatic crack detection for aircraft skin, skin image processing and parameter estimation methods were studied based on scanning images obtained by pan-and-tilt long-focus camera. Firstly, considering the characteristics of aircraft skin images, light compensation, adaptive grayscale stretching, and local OTSU segmentation were carried out to obtain the binary images of cracks. Then, the characteristics like area and rectangularity of the connected domains were calculated to remove block noises in the images. After that, thinning and deburring were operated on cracks presented in the denoised binary images, and all branches of crack were separated by deleting the nodes of cracks. Finally, using the branch pixels as indexes, information of each crack branch such as the length, average width, maximum width, starting point, end point, midpoint, orientation, and number of branches were calculated by tracing pixels and the report was output by the crack detection software. The experimental results demonstrate that cracks wider than 1 mm can be detected effectively by the proposed method, which provides a feasible means for automatic detection of aircraft skin cracks in fuselage and wings.

Almost all of existing works on structure-preserving texture smoothing utilize the statistical features of pixels within local rectangular patches to distinguish structures from textures.However, the patch sizes of the rectangular regions are single-scale, which may lead to texture over-smoothed or non-smoothed for images with sharp structures or structures at different scales. Thus, an adaptive scale bilateral texture filtering method was proposed. Firstly, the patch size of rectangular region for each pixel was chosen adaptively from some given candidate sizes based on statistical analysis of local patches, where larger patch sizes were chosen for the homogeneous texture regions and smaller ones for regions near the structure edges. Secondly, guided image were computed via the adaptive patch sizes. Finally, the guided bilateral filtering was operated on the original image. The experimental results demonstrate that the proposed method can better preserve image structures and smooth textures.

Aiming at the problem of low efficiency and lack of adaptive ability for the existing multi-keyword ciphertext search methods in cloud storage environment, a Multi-keyword Ranked Search over Encrypted cloud data based on Improved Quality Hierarchical Clustering (MRSE-IQHC) method was proposed. Firstly, the document vectors were constructed by Term Frequency-Inverse Document Frequency (TF-IDF) method and Vector Space Model (VSM). Secondly, the Improved Quality Hierarchical Clustering (IQHC) algorithm was proposed to cluster the document vectors, the document index and cluster index were constructed. Thirdly, the K-Nearest Neighbor (KNN) query algorithm was used to encrypt the indexes. Finally, the user-defined keyword weight was used to construct the search request and search for the top k relevant documents in ciphertext state. The experimental results show that compared with the Multi-keyword Ranked Search over Encrypted cloud data (MRSE) method and the Multi-keyword Ranked Search over Encrypted data based on Hierarchical Clustering Index (MRSE-HCI) method, the search time was shortened by 44.3% and 34.2%, 32.4% and 13.2%, 36.9% and 19.4% in the same number of search documents, retrieved documents and search keywords conditions, and the accuracy rate was increased by 10.8% and 8.6%. The proposed method MRSE-IQHC has high search efficiency and accuracy for multi-keyword ciphertext search in cloud storage environment.

The haze removal of remote sensing image may easily result in ground object distortion. In order to solve the problem, an improved haze removal algorithm was proposed on the basis of the traditional additive haze pollution model, which was called high fidelity haze removal method based on estimation for Haze Thickness Map (HTM). Firstly, the HTM was obtained by using the traditional additive haze removal algorithm, and the mean value of the cloudless areas was subtracted from the whole HTM to ensure the haze thickness of the cloudless areas closed to zero. Then, the haze thickness of blue ground objects was estimated alone in degraded images. Finally, the cloudless image was obtained by subtracting the finally optimized haze thickness map of different bands from the degraded image. The experiments were carried out for multiple optical remote sensing images with different resolution. The experimental results show that, the proposed method can effectively solve the serious distortion problem of blue ground objects, improve the haze removal effect of degrade images, and promote the data fidelity ability of cloudless areas.

The manual processing method is mainly used for the detection, tracking and information editing of key objects in indoor scene video, which has the problems of low efficiency and low precision. In order to solve the problems, a new learning method of indoor scene semantic annotation based on texture information was proposed. Firstly, the optical flow method was used to obtain the motion information between video frames, and the key frame annotation and interframe motion information were used to initialize the annotation of non-key frames. Then, the image texture information constraint of non-key frames and its initialized annotation were used to construct an energy equation. Finally, the graph-cuts method was used for optimizing to obtain the solution of the energy equation, which was the non-key frame semantic annotation. The experimental results of the annotation accuracy and visual effects show that, compared with the motion estimation method and the model-based learning method, the proposed learning method of indoor scene semantic annotation based on texture information has the better effect. The proposed method can provide the reference for low-latency decision-making systems such as service robots, smart home and emergency response.

To solve the problems existing in the off-line construction method of location fingerprint database for location fingerprint positioning based on Received Signal Strength Indication (RSSI), including large workload of collecting all the fingerprint information in the location, low construction efficiency of the location fingerprint database, and the limited precision of interpolation, a high efficient off-line construction method of the location fingerprint database based on the Singular Value Thresholding (SVT) Matrix Completion (MC) algorithm improved by the Backtracking Search optimization Algorithm (BSA) was proposed. Firstly, using the collected location fingerprint data of some reference nodes, a low-rank matrix completion model was established. Then the model was solved by the low rank MC algorithm based on the SVT. Finally, the complete location fingerprint database could be reconstructed in the location area. At the same time, the BSA was introduced to improve the optimization process of MC algorithm with the minimum kernel norm as the fitness function to solve the problem of the fuzzy optimal solution and the poor smoothness of the traditional MC theory, which could further improve the accuracy of the solution. The experimental results show that the average error between the location fingerprint database constructed by the proposed method and the actual collected location fingerprint database is only 2.7054 dB, and the average positioning error is only 0.0863 m, but nearly 50% of the off-line collection workload can be saved. The above results show that the proposed off-line construction method of the location fingerprint database can effectively reduce the workload of off-line collection stage while ensuring the accuracy, significantly improve the construction efficiency of location fingerprint database, and improve the practicability of fingerprint positioning method to a certain extent.

In the field of big data real-time analysis and computing, the importance of stream computing is constantly improved while the energy consumption of dealing with data on stream computing platform rises constantly. In order to solve the problems, an Energy-efficient Strategy for Threshold Control (ESTC) was proposed by changing the processing mode of node to data in stream computing. First of all, according to system load difference, the threshold of the work node was determined. Secondly, according to the threshold of the work node, the system data stream was randomly selected to determine the physical voltage of the adjustment system in different data processing situation. Finally, the system power was determined according to the different physical voltage. The experimental results and theoretical analysis show that in stream computing cluster consisting of 20 normal PCs, the system based on ESTC saves about 35.2% more energy than the original system. In addition, the ratio of performance and energy consumption under ESTC is 0.0803 tuple/(s·J), while the original system is 0.0698 tuple/(s·J). Therefore, the proposed ESTC can effectively reduce the energy consumption without affecting the system performance.

With the rapid development of mobile Internet, how to extract effective description information from a large number of mobile applications and then provide effective and accurate recommendation strategies for mobile users becomes urgent. At present, recommendation strategies are relatively traditional, and mostly recommend applications according to the single attribute, such as downloads, application name and application classification. In order to resolve the problem that the granularity of recommended applications is too coarse and the recommendation is not accurate, a mobile application similarity matrix construction method based on Latent Dirichlet Allocation (LDA) was proposed. Started from the application labels, a topic model distribution matrix of mobile applications was constructed, which was utilized to construct mobile application similarity matrix. Meanwhile, a method for converting the mobile application similarity matrix to the viable storage structure was also proposed. Extensive experiments demonstrate the feasibility of the proposed method, and the application similarity achieves 130 percent increasement by the proposed method compared with that by the existing 360 application market. The proposed method solves the problem that the recommended granularity is too coarse in the mobile application recommendation process, so that the recommendation result is more accurate.

Concerning the problem of data sparseness, cold start and low user experience of recommendation system, a video recommendation algorithm based on clustering and hierarchical model was proposed to improve the performance of recommendation system and user experience. Focusing on the user, similar users were obtained by analyzing Affiliation Propagation (AP) cluster, then historical data of online video of similar users was collected and a recommendation set of videos was geberated. Secondly, the user preference degree of a video was calculated and mapped into the tag weight of the video. Finally, a recommendation list of videos was generated by using analytic hierarchy model to calculate the ranking of user preference with videos. The experimental results on MovieLens Latest Dataset and YouTube video review text dataset show that the proposed algorithm has good performance in terms of Root-Mean-Square Error (RMSE) and the recommendation accuracy.

Concerning the problem of unbalanced load and incomplete comprehensive evaluation of nodes in big data stream processing platform, a dynamic load balancing strategy based on load awareness algorithm was proposed and applied to a data stream processing platform named Apache Flink. Firstly, the computational delay time of the nodes was obtained by using the depth-first search algorithm for the Directed Acyclic Graph (DAG) and regarded as the basis for evaluating the performance of the nodes, and the load balancing strategy was created. Secondly, the load migration technology for data stream was implemented based on the data block management strategy, and both the global and local load optimization was implemented through feedback. Finally, the feasibility of the algorithm was proved by evaluating its time-space complexity, meanwhile the influence of important parameters on the algorithm execution was discussed. The experimental results show that the proposed algorithm increases the efficiency of the task execution by optimizing the load sharing between nodes, and the task execution time is shortened by 6.51% averagely compared with the traditional load balancing strategy of Apache Flink.

Focusing on the issue that ZooKeeper cannot meet the requirement of low latency and quick recovery of RAMCloud, a Coordinator Election Strategy (CES) based on RAMCloud was proposed. First of all, according to the network environment of RAMCloud and factors of the coordinator itself, the performance indexes of coordinator were divided into two categories including individual indexes and coordinator indexes, and models for them were built separately. Next, the operation of RAMCloud was divided into error-free running period and data recovery period, their fitness functions were built separately, and then the two fitness functions were merged into a total fitness function according to time ratio. Lastly, on the basis of fitness value of RAMCloud Backup Coordinator (RBC), a new operator was proposed with randomness and the capacity of selecting an ideal target: CES would firstly eliminate poor-performing RBC by screening, as the range of choice was narrowed, CES would select the ultimate RBC from the collection of ideal coordinators by means of roulette. The experimental results showed that compared with other RBCs in the NS2 simulation environment, the coordinator selected by CES decreased latency by 19.35%; compared with ZooKeeper in the RAMCloud environment, the coordinator selected by CES reduced recovery time by 10.02%. In practical application of RAMCloud, the proposed CES can choose the coordinator with better performance, ensure the demand of low latency and quick recovery.

RAMCloud only supports the small object storage which is not larger than 1 MB. When the object which is larger than 1 MB needs to be stored in the RAMCloud cluster, it will be constrained by the object's size. So the big data objects can not be stored in the RAMCloud cluster. In order to resolve the storage limitation problem in RAMCloud, a parallel access strategy for big data objects based on RAMCloud was proposed. Firstly, the big data object was divided into several small data objects within 1 MB. Then the data summary was created in the client. The small data objects which were divided in the client were stored in RAMCloud cluster by the parallel access strategy. On the stage of reading, the data summary was firstly read, and then the small data objects were read in parallel from the RAMCloud cluster according to the data summary. Then the small data objects were merged into the big data object. The experimental results show that, the storage time of the proposed parallel access strategy for big data objects can reach 16 to 18 μs and the reading time can reach 6 to 7 μs without destroying the architecture of RAMCloud cluster. Under the InfiniBand network framework, the speedup of the proposed paralled strategy almost increases linearly, which can make the big data objects access rapidly and efficiently in microsecond level just like small data objects.

In order to solve the problem of low using rate, RAMCloud would change the positions of objects, which would cause the failure for Hash to localize the object, and the low efficiency of data search. On the other hand, since the needed data could not be positioned rapidly in the recovery process of the data, the returned segments from every single backup could not be organized perfectly. Due to such problems, RAMCloud Global Key (RGK) and binary index tree, as solutions, were proposed. RGK can be divided into three parts:positioned on master, on segment, and on object. The first two parts constituted Coordinator Index Key (CIK), which means in the recovery process, Coordinator Index Tree (CIT) could position the master of segments. The last two parts constituted Master Index Key (MIK), and Master Index Tree (MIT) could obtain objects quickly, even though the data was shifted the position in the memory. Compared with the traditional RAMCloud cluster, the time of obtaining objects can obviously reduce when the data throughput is increasing. Also, the idle time of coordinator and recombined time of log are both declining. The experimental results show that the global key with the support of the binary index tree can reduce the time of obtaining objects and recovering.

It is difficult to maintain a balance between accuracy and real-time performance of lane line recognition, thus a new lane line recognition method was proposed based on region division. Firstly, an improved OTSU algorithm was applied to segment the edge image; then, feature points in that edge image were extracted by using Progressive Probabilistic Hough Transform (PPHT) algorithm and fitted as a line by using Least Square Method (LSM). Finally, all fitted lines were judged and the possible lines were chosen by using an anti-interference algorithm. Comparative experiments were conducted with three other algorithms mentioned in the references. In addition, an evaluation model was put forward to assess the performance of the algorithms when dealing with 500 typical lane images. Meanwhile, by calculating the average overhead time on processing each frame of a 1 min 26 s video, the response time of the algorithm was evaluated. The experimental results show that three indexes including precision, recall rate and F value of the proposed algorithm are better than the comparison algorithm, and the proposed algorithm also meets the requirement of real-time processing.

It began with the requirement of Sudoku to establish the system of equations. The solution of equation system was equivalent to the original Sudoku. And many mathematical properties were derived from equation system, including candidates elimination techniques, unique determination property, contradiction property and invariance property. It also illustrated that the artificial deductive rules included these properties. Finally this paper proposed a algorithm which calculated the solution of equation system from these properties. The algorithm used a three-dimensional matrix to express the candidate matrix of an unsolved Sudoku, and according to the above properties the candidate matrix could be deleted until Sudoku was solved. The algorithm can resolve many difficult Sudoku puzzles. Two difficult Sudoku puzzles were demonstrated and solved easily with the method in this paper, which shows the method is very effective.

Command protection engineering is the important component of national protection engineering system. To raise the level of construction of command protection engineering, the Back Propagation (BP) neural network was improved to give research on self-stability evaluation of its surrounding rock. Firstly, the network topology was devised，based on the characteristics of surrounding rock. Secondly, the model was improved according to its disadvantages, by introducing the momentum, self-adaptive adjusting learn rate, variable hidden nodes and steep factor; furthermore, Genetic Algorithm(GA) was imported to seek its best initial weight and threshold value. Finally, an instance was given to validate the algorithm. The results show that the model is scientifically reliable and of better value in engineering.

It has been a new approach that flash memory is used as storage media to improve database system performance. In the current flash database system, to solve the problems of low search performance, improper log region allocation, and high index update cost in storage management, a forecasting algorithm based on the latest version of Bloom Filter was proposed, record locator and log summary structure were introduced, and a self-adaptive mechanism based on flash search and update cost estimate model were given. The experimental results prove that it can make a proper log region allocation, efficiently improve search performance, and reduce non-clustered index update cost.

In the distributed data fusion architecture, the general batches should be organized to make local tracks be correspondent with systemic tracks, after local tracks are associated in fusion center. Thus, following tracks' fusion can be dealt conveniently. In the practical engineering systems, the association mistakes can make the following automatic algorithm for organizing batches invalid, and cause systemic tracks intermittent and fail to work. An automatic algorithm for organizing batches of associated local tracks was proposed. In this algorithm, the local tracks would be distributed with general batches according to the desired degree between association of tracks and fusion of tracks. It could make the association tracks be correspondent with the systemic tracks. Besides reflecting real associated relationship, the proposed algorithm also guaranteed the stability of fused tracks batches and made the batch represent the same object in different time. The proposed algorithm has been applied into the practical engineering system, and it has good stability.

The static resource allocation algorithm based on the theory of effective bandwidth was introduced firstly. When the real behavior of Internet traffic is taken into account, this algorithm is inefficient. So here a dynamic resource management algorithm based on Internet traffic prediction was proposed to take the place of it. This algorithm was applied to a Differentiated Service network, and implemented on the boundary node. The basic idea under this algorithm was to allocate resources (bandwidth/buffer size) between different kinds of flows dynamically, according to the result of prediction. At last, ns-2 was used to run the simulation and find out the lost packets rate and output link utilization of this algorithm, which were superior to those of the static resource allocation algorithm.