Aiming at the problems of the existing Graph Neural Network （GNN）-based collaborative filtering methods under sparse and noisy data conditions， such as the obscuring of true signals by static noise injection， the inability of fixed semantic prototypes to capture dynamic user interests， and the high computational overhead of complex augmentation， a graph diffusion generation and adaptive sampling-based contrastive collaborative filtering method was proposed. Firstly， a lightweight graph diffusion generation mechanism based on gradual denoising was designed， so as to optimize node representations through forward noise-adding and reverse denoising， thereby generating noise-resistant contrastive views. Then， random masking was integrated with Random Walk with Restart （RWR） to model local neighborhood features and global structural semantics collaboratively， thereby generating high-quality negative samples. Finally， an improved InfoNCE （Information Noise Contrastive Estimation） loss function was introduced to optimize the multi-view contrastive learning objective and enhance the discriminative power of representations. Experimental results on Gowalla， Yelp， and Amazon datasets show that compared to the best-performing baseline method， the proposed method improves the Top-20 Recall （Recall@20） by 0.63%， 1.36%， and 1.88%， respectively， and the Top-40 Normalized Discounted Cumulative Gain （NDCG@40） by 0.95%， 1.47%， and 1.24%， respectively， as well as improves the recommendation performance for long-tail users by 26.7%， increases the training efficiency by 90%， and accelerates the convergence speed by 32%. It can be seen that the proposed method enhances the noise resistance and dynamic adaptability of recommendation systems in open environments significantly.

To address the problems of low exploration efficiency and information exchange under limited communication bandwidth in the current Multiple Unmanned Aerial Vehicle （Multi-UAV） systems when exploring large-scale complex environments， a fast and fully autonomous exploration method for Multi-UAV in large-scale complex environments was proposed， including a fast and hierarchical exploration strategy and a lightweight large-scale environment modeling method. Firstly， closed viewpoints were generated in the front-end trajectory planning part to drive the Unmanned Aerial Vehicles （UAVs） to explore unknown environments. Then， the smooth， continuous， and time-optimal trajectory optimization problem was transformed into a convex optimization problem in the back-end， and this problem was modeled systematically. Meanwhile， in terms of environmental characterization， a random mapping method was used for lightweight mapping and map data interaction. Finally， in simulation， the proposed method was compared with fast exploration method using incremental boundary information and hierarchical planning — FUEL （Fast Unmanned aerial vehicle ExpLoration）， rapid exploration method based on frontiers — FBE （Frontier-Based Exploration）， and exploration method based on the next best viewpoint — NBVP （Next Best View Planner）. The results show that the proposed method improves the exploration time performance by 14.4%， 43.9% and 47.7%， respectively， and the lightweight mapping method reduces the data size by 28.3% and 22.4%， respectively， compared to the Bayesian method and the polyhedron method. It can be seen that the proposed method can perform fast and fully autonomous exploration in large-scale complex environments efficiently.

Industrial defect detection plays a crucial role in ensuring product quality and enhancing enterprise competitiveness. Traditional defect detection methods rely on manual inspection， which is costly and inefficient， making it difficult to meet large-scale quality inspection requirements. In recent years， vision-based industrial defect detection technologies have made significant progress and become an efficient solution for product appearance quality inspection. However， in many practical industrial scenarios， it is challenging to obtain large amounts of labeled data， and there are requirements for both the labor cost and real-time performance of product detection， making unsupervised learning become a research hotspot. Related work on task construction， current technologies， evaluation standards， and the commonalities and differences among various methods in this field were reviewed. Firstly， the definition of industrial defect problems was clarified， and the difficulties of the problem were analyzed from perspectives of data challenges and task difficulties. Secondly， unsupervised deep learning-based methods for industrial defect detection were comprehensively introduced and systematically categorized. Furthermore， commonly used public datasets and evaluation metrics were summarized. Finally， future work in industrial defect detection was discussed.

Aiming at the problem that the traditional biped robot model lacks the feet mass and the torso， a flexible biped robot model considering the influence of swing leg dynamics and torso was proposed， and its walking control and stability were studied. Firstly， the dynamics model of the system was established and the dynamics equation was deduced by the Euler-Lagrange method. At the same time， based on the Spring-Loaded Inverted Pendulum （SLIP） model， by adding rigid torso， foot mass， and adopting telescopic legs of variable length， the influence of the torso and the dynamics of swing legs on the gait of the robot was fully considered. Then， the feedback linearization controller based on variable length legs was designed to track the target trajectory and regulate the attitudes of the swing legs and the torso. Finally， the Newton-Raphson iteration method and Poincaré map were adopted to analyze the fixed point and orbital stability conditions of the robot. Simulation analysis was carried out based on theoretical analysis. Simulation results show that the proposed controller can realize the robot’s periodic walking and has good robustness to the external interference. And the moduli of all eigenvalues of the Jacobian matrix are less than 1， forming a stable limit cycle， which proves that the system has orbital stability.

Concerning the problem that the interpretable mapping relationship between data features and data categories do not be revealed by general feature selection algorithms. on the basis of Gene Expression Programming (GEP),by introducing the initialization methods, mutation strategies and fitness evaluation methods,an improved Feature Selection classification algorithm based on Layer Distance for GEP(FSLDGEP) was proposed. Firstly,the selection probability was defined to initialize the individuals in the population directionally, so as to increase the number of effective individuals in the population. Secondly, the layer neighborhood of the individual was proposed, so that each individual in the population would mutate based on its layer neighborhood, and the blind and unguided problem in the process of mutation was solved。Finally, the dimension reduction rate and classification accuracy were combined as the fitness value of the individual, which changed the population evolutionary mode of single optimization goal and balanced the relationship between the above two. The 5-fold and 10-fold verifications were performed on 7 datasets, the functional mapping relationship between data features and their categories was given by the proposed algorithm, and the obtained mapping function was used for data classification. Compared with Feature Selection based on Forest Optimization Algorithm (FSFOA), feature evaluation and selection based on Neighborhood Soft Margin (NSM), Feature Selection based on Neighborhood Effective Information Ratio (FS-NEIR)and other comparison algorithms, the proposed algorithm has obtained the best results of the dimension reduction rate on Hepatitis, Wisconsin Prognostic Breast Cancer (WPBC), Sonar and Wisconsin Diagnostic Breast Cancer (WDBC) datasets, and has the best average classification accuracy on Hepatitis, Ionosphere, Musk1, WPBC, Heart-Statlog and WDBC datasets. Experimental results shows that the feasibility, effectiveness and superiority of the proposed algorithm in feature selection and classification are verified.

It is difficult for traditional algorithms to accurately find out the first direct path in indoor Ultra Wide Band (UWB) Time Of Arrival (TOA) positioning system, resulting in low positioning accuracy. In order to solve the problem, a TOA indoor UWB positioning algorithm based on Time Reversal (TR) was proposed. Firstly, the spatial-temporal focusing characteristic of TR processing was used to determine the first direct path, so as to estimate the TOA of this path. Then, the Weighted Least Squares (WLS) positioning algorithm was used to assign the corresponding weights to different estimation components for improving the positioning accuracy. The simulation results show that, compared with the traditional TOA positioning, the proposed scheme has the Root Mean Square Error (RMSE) reduced by 28.6% under the low signal-to-noise ratio condition. It can be seen that the proposed scheme improves the system positioning accuracy significantly.

To solve the Split Delivery Vehicle Routing Problem (SDVRP) more reasonably, overcome the shortcoming that the traditional two-stage solution method of first route and then optimization is easy to fall into local optimization, and handle the problem that the intelligent optimization algorithm fails to integrate competition and cooperation organically in the optimization stage, an Improved Pyramid Evolution Strategy (IPES) was proposed with the shortest delivery path and the least delivery vehicles as the optimization objectives. Firstly, based on the pyramid, the encoding and decoding methods and hierarchical cooperation strategy were proposed to solve SDVRP. Secondly, according to the characteristics such as the random of genetic algorithm, high parallelism of "survival of the fittest" and self-adaption, as well as the different labor division of different layers of pyramid structure, an adaptive neighborhood operator suitable for SDVRP was designed to make the algorithm converge fast to the optimum. Finally, the optimal solution was obtained. Compared with the piecewise solving algorithm, clustering algorithm, particle swarm algorithm, artificial bee colony algorithm, taboo search algorithm,the results of four simulation experiments show that, when solving the optimal path of each case, the proposed IPES has the solution accuracy improved by at least 0.92%, 0.35%, 3.07%, 9.40% respectively, which verifies the good performance of IPES in solving SDVRP.

Entity relation extraction is a critical basic step of question answering, knowledge graph construction and information extraction in the medical field. In view of the fact that there is no open dataset available in the process of building knowledge graph specialized for cardiovascular disease, a professional training set for entity relation extraction of specialized cardiovascular disease knowledge graph was constructed by collecting some medical guidelines for cardiovascular disease and performing the corresponding professional labeling of the categories of entities and relations. Based on this dataset, firstly, Bidirectional Encoder Representation from Transformers and Convolutional Neural Network (BERT-CNN) model was proposed to realize the relation extraction in Chinese corpus. Then, the improved Bidirectional Encoder Representation from Transformers and Convolutional Neural Networks based on whole word mask (BERT(wwm)-CNN) model was proposed to improve the performance of relation extraction in Chinese corpus, according to the fact that word instead of character is the fundamental unit in Chinese. Experimental results show that, the improved BERT(wwm)-CNN model has the accuracy of 0.85, the recall of 0.80 and the F ₁ value of 0.83 on the constructed relation extraction dataset, which are better than those of the comparison models, Bidirectional Encoder Representation from Transformers and Long Short Term Memory (BERT-LSTM) and BERT-CNN, verifying the superiority of the improved BERT(wwm)-CNN.

To solve the problem that the reconstructed 3D model of a single image has high uncertainty, a network model based on depth image estimation, spherical projection mapping and 3D generative adversarial network was proposed. Firstly, the depth image of the input image was obtained by the depth estimator, which was helpful for the further analysis of the image. Secondly, the obtained depth image was converted into a 3D model by spherical projection mapping. Finally, 3D generative adversarial network was utilized to judge the authenticity of the reconstructed 3D model, so as to obtain 3D model closer to reality. In the comparison experiments with LVP algorithm which learning view priors for 3D reconstruction, the proposed model has the Intersection-over-Union (IoU) increased by 20.1% and the Charmfer Distance (CD) decreased by 13.2%. Theoretical analysis and simulation results show that the proposed model has good generalization ability in the 3D model reconstruction of a single image.

The cubic cryptosystem is the improvement of the classical multivariable cryptosystem Square. By increasing the degree of central mapping from square mapping to cubic mapping, the public key polynomial was promoted from quadratic to cubic in order to resist the MinRank attack against the quadratic multivariable public key cryptosystem. Aiming at this system, a MinRank attack combining with difference was proposed to recover its private key. Firstly, the central mapping difference of the system was analyzed, and its rank was determined according to the structure after difference. Then, the difference of the public key was solved and the coefficient matrices of the quadratic term were extracted. After that, a MinRank problem was constructed by the coefficient matrix and the determined rank. Finally, the extended Kipnis-Shamir method was combined to solve the problem. The experimental results show that the private key of cubic cryptosystem can be recovered by using MinRank attack.

In order to solve the Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows (VRPSPDTW)in the context of reverse logistics,the corresponding vehicle routing problem model was established according to the actual situation and solved by memetic algorithm. In the process of solving the model,the Guided Ejection Search (GES)was used to generate the initial population. In the process of population evolution,the Edge Assembly Crossover (EAX)method was used to generate the offspring,and in order to improve the quality of solutions and the search efficiency of algorithms,multiple neighborhood structures were used to repair and educate the offspring. The performance of memetic algorithm was tested and compared with Genetic Algorithm (GA),parallel-Simulate Annealing algorithm (p-SA) and Discrete Cuckoo Search(DCS)algorithm on Wang and Chen test dataset. Experimental results show that the proposed algorithm obtains the current optimal solutions when solving all small-scale examples;the algorithm updates or achieves current optimal solutions on 70% examples when solving the standard-scale examples,and the obtained optimal solution has more than 5% improvement compared with the current optimal solution,fully verifying the good performance of the algorithm for solving VRPSPDTW.

Aiming at the problems of low coverage and high communication cost for wireless sensor deployment, an Improved Heuristic Ant Colony Algorithm (IHACA) merging Chaos optimization of padded Sensor Placements at Informative and cost-Effective Locations algorithm (IHACA-CpSPIEL) method for sensor deployment was proposed. Firstly, the correlation between observation points and unobserved points was established by mutual information, and the communication cost was described in the form of graph theory to establish the mathematical model with submodularity. Secondly, chaos operator was introduced to improve the global searching ability of pSPIEL (padded Sensor Placements at Informative and cost-Effective Locations) algorithm for local parameters, and then the optimal number of clusters was found. Then, the factors of the colony distance heuristic function and the pheromone updating mechanism were changed to jump out of the local solution of communication cost. Finally, Chaos optimization of pSPIEL algorithm (CpSPIEL) was integrated with the IHACA to determine the shortest path, so as to achieve the purpose of low-cost deployment. The experimental results show that the proposed algorithm can jump out of the local optimal solution well, and the communication cost is reduced by 6.5% to 24.0% compared with the pSPIEL algorithm, and has a faster search speed.

For the problems of scope decision-making of maintenance for civil aviation engine module and cost optimization of full-life maintenance, the engine module maintenance level decision-making and cost optimization model based on annealing frog leaping particle swarm optimization algorithm with return time interval as variable was proposed. Firstly, according to the maintenance logic diagram for each module in maintenance instruction manual and the replacement situation of life-limited parts, the engine shop visit cost function was built. Secondly, by using the annealing frog leaping particle swarm optimization algorithm, the shop visit costs of different return times and the maintenance level for each module in full life time were determined. Finally, based on examples, the proposed algorithm was compared with the basic particle swarm optimization algorithm, annealing particle swarm optimization algorithm and shuffled frog leaping optimization algorithm, and the influence of different return times on maintenance cost and reliability was analyzed. Experimental results indicate that, when the engine has five shop visits in its full life time, the average cost obtained using annealing frog leaping particle swarm optimization algorithm was 322.479 1 $/flight hour, which was the optimum value compared with those of the other three optimization algorithms. The proposed algorithm can facilitate the shop visit decision-making of airlines and overhaul companies.

Aiming at the problems that there is no unified registration of files, the whereabouts of files are not tracked, and the process of circulation is not standardized in the circulation of electronic files under the Internet ecology, a blockchain-based electronic file circulation scheme was proposed. Firstly, the design goals and design architecture of the electronic file circulation system based on blockchain were proposed using the multi-centralized system of the consortium blockchain in the blockchain. Secondly, blockchain-based electronic file circulation system was implemented by using a cloud storage platform to upload files for electronic file storage and adding time-stamps of the ownership transfer data of files to make the circulation process continuous, relevant, traceable, honest and credible. The data synchronization and tracing of the blockchain-based electronic file circulation system was achieved through using database calls to realize the data access. Finally, a smart contract for electronic file ownership transfer and query to verify and protect the contents of the files by reading the file identification. The security analysis and performance tests show that compared to the original one, the proposed scheme is more secure and enhances the credibility of the circulation information, at the same time, the shorter execution time of the smart contract makes the system have better reliability and traceability.

It is impractical to have a large size of training dataset in real work for neural network training, so a two-input stream generative neural network which can generate a new image with the given parameters was proposed, hence to augment the training dataset. The framework of the proposed neural network consists of a two-input steam convolution network and a deconvolution network. The two-input steam network has two convolution networks to extract features, and the deconvolution network is connected to the end. Two images with different angle were input into the convolution network to get high-level description, then an interpolation target image with a new perspectives was generated by using the deconvolution network with the above high-level description and set parameters. The experiment results on ShapeNetCore show that on the same dataset, the recognition rate of the proposed network has increased by 20% than the common network framework. This method can enlarge the size of the training dataset and is useful for multi-angle recognition.

Concerning the problem that how to detect spammer groups writing fake reviews on the e-commerce platforms, a Weighted reviewer Graph based Spammer group detection Algorithm (WGSA) was proposed. Firstly, a weighted reviewer graph was built based on the co-reviewing feature with the weight calculated by a series of group spam indicators. Then, a threshold was set for the edge weight to filter the suspicious subgraphs. Finally, considering the community structure of the graph, the community discovery algorithm was used to generate the spammer groups. Compared with K-Means clustering algorithm ( KMeans), Density-Based spatial clustering of applications with noise (DBscan) and hierarchical clustering algorithm on the large dataset Yelp, the accuracy of WGSA is higher. The characteristics and distinction of the detected spammer groups were also analyzed, which show that spammer groups with different activeness have different harm. The high-active group is more harmful and should be concerned more.

Concerning the fact that the existing rate adaptation algorithms based on Dynamic Adaptive Streaming over HTTP (DASH) have frequent bitrate switching and low average bitrate in wireless network, a Rate Smooth Switching (RSS) algorithm based on DASH standard was proposed. Firstly, a sliding window was used by the bandwidth detection mechanism of the algorithm to sample the download speed of historical segments to calculate the bandwidth offset coefficient, the fluctuation of the bandwidth was initially determined according to the value of offset coefficient, and the situation of the fluctuation was further determined whether there was a consistent variation trend, thereby distinguishing continuous variation and short-term jitter of the bandwidth, and the bandwidth prediction value corresponding to each circumstance was calculated. Secondly, with bandwidth fluctuation, buffer occupancy and variation, bandwidth prediction value considered, the rate decision model of the algorithm adopted Fast Buffering (FB), Slow Switching (SS), Fast Rising (FR), Limited Declining (LD), Stable Holding (SH) strategies and sleeping mechanism to dynamically control the video bitrate selection process. The experimental results show that compared with fuzzy-based DASH rate adaptation algorithm and modulated throughput driven rate adaptation algorithm, the proposed algorithm can not only increase the bitrate to optimum level in the shortest time at the beginning of video playback to improve the average bitrate, but also minimize the number of bitrates' switching in the case of sudden change and frequent fluctuation of bandwidth, thus obtaining a good quality of experience for wireless video users.

Focusing on the problem that the Retrieval Matching Question and Answer (RMQA) model has weak adaptability to Chinese corpus and the neglection of semantic information of the sentence, a Chinese text semantic matching model based on Convolutional neural network Latent Semantic Model (CLSM) was proposed. Firstly, the word- N-gram layer and letter- N-gram layer of CLSM were removed to enhance the adaptability of the model to Chinese corpus. Secondly, with the focus on vector information of input Chinese words, an entity attention layer model was established based on the attention mechanism algorithm to strengthen the weight information of the core words in sentence. Finally, Convolutional Neural Network (CNN) was used to capture the input sentence context structure information effectively and the pool layer was used to reduce the dimension of semantic information. In the experiments based on a medical question and answer dataset, compared with the traditional semantic models, traditional translation models and deep neural network models, the proposed model has 4-10 percentage points improvement in Normalized Discount Cumulative Gain (NDCG).

The security mechanism of REpresentational State Transfer (REST) architecture is not perfect. In order to solve the problem, the security analysis and evaluation of REST architecture based on attack graph was proposed, and the security quantitative evaluation of REST architecture was realized by using attack graph. Firstly, the possible attack of REST architecture was predicted, the REST architecture attack graph model was constructed accordingly, and the attack probability parameter and attack realization parameter were calculated. Then, according to the attack state and attack behavior of attack graph, the security protection measures were proposed. In view of the above, the REST architecture attack graph model was reconstructed, and the attack probability parameter and attack realization parameter were recalculated too. By comparison, after the adoption of security protection measures, the attack possibility parameter has been reduced to about 1/10, and the attack realization parameter has been reduced to about 1/86. The comparison results show that the constructed attack graph can effectively and quantitatively evaluate the security performance of REST architecture.

In order to achieve effective integration and accurate fusion of multi-source Point of Interest (POI) data, a Mutually-Nearest Method considering Distance and Category (MNMDC) was proposed. Firstly, for spatial attributes, standardized weight algorithm was used to calculate the spatial similarity of the object to be fused, and the fusion set was obtained. Secondly, for non-spatial attributes, Jaro-Winkle algorithm was used to eliminate some objects with consistent categories by a low threshold, and exclude some objects with inconsistent categories by a high threshold. Finally, non-spatial Jaro-Winkle algorithm with distance constraint, category consistency constraint and high threshold was used to find out the missing objects in the spatial algorithm. The experimental results show that the average accuracy reaches 93.3%, compared with Combined Normal Weight and Title-similatity algorithm (COM-NWT) and the grid correction methods, the accuracy of MNMDC method in seven different groups of coincidence degree data, the average accuracy increases by 2.7 percentage points and 1.6 percentage points, the average recall increases by 2.3 and 1.4 percentage points. The MNMDC method allows more accurate fusion of POI data during actual fusion.

In order to overcome the disadvantages of Differential Evolution (DE) algorithm such as low optimization accuracy, slow convergence and poor stability, an Adaptive Differential Evolution algorithm based on Multi-Mutation strategy (ADE-MM) was proposed. Firstly, two disturbance thresholds with learning functions were used in the selection of three mutation strategies to increase the diversity of the population and expand the search scope. Then, according to the successful parameters of the last iteration, the current parameters were adjusted adaptively to improve the search accuracy and speed. Finally, vector particle pool method and central particle method were used to generate new vector particles to further improve the search effect. Tests were performed on 8 functions for 5 comparison algorithms (Random Mutation Differential Evolution (RMDE), Cross-Population Differential Evolution algorithm based on Opposition-based Learning (OLCPDE), Adaptive Differential Evolution with Optional External Archive (JADE), Self-adaptive Differential Evolution (SaDE), Modified Differential Evolution with p-best Crossover (MDE_pBX)), and each example was independently performed 30 times. The ADE-MM algorithm achieves a complete victory in the comparison of mean and variance, 5 independent wins and 3 tie wins are achieved in the 30-dimensional case; 6 independent wins and 2 tie wins are obtained in the 50-dimensional case; in 100-dimensional case, all are won independently. At the same time, in the Wilcoxon rank sum test, winning rate and time-consuming analysis, the ADE-MM algorithm also achieves excellent performance. The results show that ADE-MM algorithm has stronger global search ability, convergence and stability than other five comparison algorithms.

The noise assumption and motion assumption of trajectory should be demanded in dynamic trajectory prediction based on Kalman filter. In order to eliminate this insufficiency, the metabolism GM(1,1) model was introduced in dynamic trajectory prediction. Thus a prediction algorithm based on weighted grey GM(1,1) model (TR_GM_PR algorithm)was presented. Firstly, sub-trajectories with different length before forecasting point were cut out in order, then the relative fitting errors and predicted values of sub-trajectories were calculated using grey GM(1,1) model. Secondly, the normalization processing of relative fitting errors was carried out, and the weights of predicted values were set according to the result. Finally, using the linear combination of predicted values and their corresponding weights, the running tendency of trajectory in future was predicted. Experiments were conducted with the Atlantic weather Hurricane data from 2000 to 2008. Compared with hurricane trajectory prediction method with pattern matching, TR_GM_PR algorithm improves the prediction accuracy ratio of 6 hours by 2.6056 percentage points to 67.6056%. The experimental results show that TR_GM_PR algorithm is suitable for short-term trajectory prediction. In addition, the new algorithm has simple calculation and high real-time performance, and can effectively improve the prediction accuracy of dynamic trajectory.

Aiming at the problem that serial Particle Swarm Optimization (PSO) algorithms are time-consuming to deal with big tasks, a novel shared parallel PSO (Shared-PSO) algorithm was proposed. The multi-core processing power was used to reduce time to get resolution. In order to facilitate communication of particles, a shared area was set up and a random strategy was applied to switch particles. Several serial PSO algorithms could be permitted to update particle information because of the universality of its algorithm flow. Shared-PSO was applied on the standard optimization test set CEC (Congress on Evolutionary Computation) 2014. The experiment results show that the execution time of Shared-PSO is a quarter of the serial PSO's. The proposed algorithm can effectively improve the execution efficiency of serial PSO, and expand applied range of PSO.

To effectively overcome the free-rider problem existing in Peer-to-Peer (P2P) network, this paper presented resource allocation scheme based on Nash bargaining which guarantees the minimum Quality of Service (QoS). Firstly, the article built the system model of the minimum QoS, analysis indicated that the cooperative peer' bargaining power is positively related to the maximum contribution ability but the non-cooperative peer' bargaining power is negative related to the maximum contribution ability, so, the cooperative peers can obtain more resources than non-cooperative peers; secondly, the article demonstrated that the cooperative peer who has larger relative bargaining power could obtain more resources than the others. Lastly, simulations show that to guarantee the peers receiving the minimum QoS, the cooperation peers resource allocation related to the initial resource allocation and the Nash bargaining power and other factors; the initial resource allocation is positively related to the maximum contribute ability of the cooperation peers, which reduces when the number of peers increases; the bargaining power decreases when the number of peers increases and resource allocation increases when the bargaining power increases. This resource allocation mechanism was compared with the classical average resource allocation mechanism which also guarantees the fairness, the cooperators can obtain more resources. The simulation results verified that the greater the bargaining power of nodes, the more resources to obtain within the minimum QoS.

To solve the problems of updating, modifying, upgrading and maintaining the function of robot by offline and static method, SoftMan was introduced for robot platform, and the architecture of robot system, whose managing center is host SoftMan, was built. The host SoftMan was mainly researched. Firstly, the architecture of host SoftMan was constructed. Then the descriptive unification model of knowledge and behavior of host SoftMan was put forward, the knowledge model was constructed and implemented based on data structure, and the design specifications and reference realization of the algorithm were given for its main service behaviors. Finally, the robot system was unified with the SoftMan system. Through the test, the function of robot was successfully replaced online and dynamically, verifying the correctness and feasibility of the method of designing and implementing the host SoftMan.

In order to overcome the problems of low convergence precision and easily relapsing into local optimum in Fruit fly Optimization Algorithm (FOA), by introducing the Levy flight strategy into the FOA, an improved FOA called double subgroups FOA with the characteristics of Levy flight (LFOA) was proposed. Firstly, the fruit fly group was dynamically divided into two subgroups (advanced subgroup and drawback subgroup) whose centers separately were the best individual and the worst individual in contemporary group according to its own evolutionary level. Secondly, a global search was made for drawback subgroup with the guidance of the best individual, and a finely local search was made for advanced subgroup by doing Levy flight around the best individual, so that not only both the global and local search ability balanced, but also the occasionally long distance jump of Levy flight could be used to help the fruit fly jump out of local optimum. Finally, two subgroups exchange information by updating the overall optimum and recombining the subgroups. The experiment results of 6 typical functions show that the new method has the advantages of better global searching ability, faster convergence and more precise convergence.

To solve the security problems between the reader and the server of mobile Radio Frequency IDentification (RFID) caused by wireless transmission, a two-way authentication protocol based on pseudo-random function was provided. It satisfied the EPC Class-1 Generation-2 industry standards, and mutual certifications between tags, readers and servers were achieved. The security of this protocol was also proved by using GNY logic. It can effectively resist track, replay and synchronization attack etc.; simultaneously, its main calculations are transferred to the server, thereby reducing the calculations and cost of the tag.

Aiming at the problem of extracting the useful signal in the complex background of chaotic noise rapidly and accurately, the phase space reconstruction theory based on complex nonlinear system was proposed, and the method of improved Extreme Learning Machine (ELM) was used to predict the single step error and detect the weak signal. The improved K-means clustering algorithm was used to select the optimal family as training set, the improved extreme learning machine chose the weight value and the offset to improve the detection accuracy and speed. The one step prediction model of chaotic noise sequence with Lorenz system was established, and the weak target signals (including periodic signal and transient signal) that lost in the chaotic noise were detected, then the IPIX radar data of Canada Mc Master University were used, and the floater signal in sea clutter noise was extracted to do the experimental research. The results show that the method can effectively detect the very weak signal in chaos background noise, at the same time, the influence of noise was restrained to the chaotic background signal, compared with the traditional algorithms such as Radial Basis Function (RBF), the prediction accuracy is increased by 25%, the detection threshold is increased by -5 dB, the training time is reduced by 77.1 s, it has more obvious advantages in practical application.

Focusing on the issues of size-varying and angle-varying of the images, and low recognition rate and poor robustness in image recognition, a morphological image recognition method was proposed. Firstly, image was centralized and normalized, and the silhouettes of image was converted into binary image. Secondly, varable circles were used to extract morphological features of image, and a fan-shaped area feature vector was established. Finally, multi-scale analysis method was applied to image recognition and image angle analysis. Compared with traditional method in the conditions such as angle independence, proportion independence and profile robustness, the experimental results show that the proposed method has higher recognition rate, and can analyze the angle difference between the images. The method is robust to noise, and can significantly reduce the influence of different image scale and rotation angle on image recognition.

The reheat steam temperature control system has the problems of multivariable control, difficult control, and so on. In order to solve the problems, a fast calculation method of multivariable control for reheat steam temperature was proposed,which was based on Smith control method and Predictive Functional Control (PFC) method. First of all, the reheat steam temperature multivariable control system was decomposed into three single-variable control systems. In every single-variable control system, the other two control volumes were taken as the interference terms. Secondly, according to Smith control idea, every single-variable control system was designed. Finally, on the basis of improving the performance index of the predictive functional control, three single-variable control systems were considered synthetically to realize the reheat steam temperature control. The simulation results of reheat steam temperature control show that with less parameters and explicit physical meaning, the proposed method is about 50 times as fast as the traditional predictive control with constraint conditions. The experimental results show that the proposed algorithm can effectively improve the quality of the reheat steam temperature control in the field.