Accurate prediction of port traffic flow is a challenging task due to its stochastic uncertainty and time-unsteady characteristics. In order to improve the accuracy of port traffic flow prediction， a port traffic flow prediction model based on knowledge graph and spatio-temporal diffusion graph convolution network， named KG-DGCN-GRU， was proposed， taking into account the external disturbances such as meteorological conditions and the opening and closing status of the port-adjacent highway. The factors related to the port traffic network were represented by the knowledge graph， and the semantic information of various external factors were learned from the port knowledge graph by using the knowledge representation method， and Diffusion Graph Convolutional Network （DGCN） and Gated Recurrent Unit （GRU） were used to effectively extract the spatio-temporal dependency features of the port traffic flow. The experimental results based on the Tianjin Port traffic dataset show that KG-DGCN-GRU can effectively improve the prediction accuracy through knowledge graph and diffusion graph convolutional network， the Root Mean Squared Error （RMSE） is reduced by 4.85% and 7.04% and the Mean Absolute Error （MAE） is reduced by 5.80% and 8.17%， compared with Temporal Graph Convolutional Network （T-GCN） and Diffusion Convolutional Recurrent Neural Network （DCRNN） under single step prediction （15 min）.

To address the untrusted issue of cloud storage servers， a Dynamic ciphertext sorting and retrieval scheme based on blockchain was proposed. A balanced binary tree was utilized as the index tree to achieve sublinear search efficiency. A vector space model was employed to reduce text complexity. The sorting of search results for multiple keywords was achieved through the TF-IDF （Term Frequency-Inverse Document Frequency） weighted statistical algorithm. By employing a separate index tree for newly added files and maintaining a revocation list for deleted files， dynamic updating was enabled for the blockchain-based searchable encryption solution. Through leakage function， it is proven that the proposed scheme is secure against adaptive chosen keyword attacks. Performance testing analysis demonstrates that compared to the ｛key， value｝ index structure， the tree index structure adopted in the proposed scheme reduces index tree generation time by 98%， file search time by 7% and dynamic updating time by 99% averagely， with significant efficiency improvements on each step.

Mining users’ multi-dimensional opinions on products from the complaint texts of new energy vehicles can provide support for product design decisions. Because the complaint text has the characteristics of high entity density and lengthy sentence structure， the existing methods for Aspect-Opinion Pair Extraction （AOPE） suffer from weak correlations between aspect terms and opinion terms. To address this problem， an Aspect-Opinion pair Extraction model based on Context Enhancement （AOE-CE） was proposed， fusing topic features and text features as contextual representation to enhance the correlations between entities. This model was consisted of an entity recognition module and a relation detection module. Firstly， in the entity recognition module， the text was encoded by using a pre-trained model and a part-of-speech tagging tool. Secondly， Bi-directional Long Short-Term Memory （Bi-LSTM） network combined with multi-head attention was employed to capture contextual information and then derive text features. Subsequently， these text features were input into a Conditional Random Field （CRF） model to obtain the entity set. In the relation detection module， the topic features were obtained through BERT （Bidirectional Encoder Representations from Transformers） and fused with the text features to obtain the enhanced contextual representation. Then the tri-affine mechanism was used to enhance the correlations between entities with the help of contextual representation. Finally， the extraction result was obtained by Sigmoid. The experimental results show that the precision， recall， and F1 value of AOE-CE are 2.19， 1.08， and 1.60 percentage points higher than those of SDRN （Synchronous Double-channel Recurrent Network） model respectively， indicating that AOE-CE has better AOPE effect.

Aiming at the problem of low data availability caused by existing disturbance mechanisms that do not consider the semantic relationship of location points， a Trajectory Location Privacy protection Mechanism based on Differential Privacy was proposed， namely DP-TLPM. Firstly， the sliding windows were used to extract trajectory dwell points to generate the fuzzy regions， and the regions were sampled using exponential and Laplacian mechanisms. Secondly， a road network matching algorithm was proposed to eliminate possible semantic free location points in the sampled points， and the trajectory was segmented and iteratively matched by using Error Ellipse Matching （EEM）. Finally， a disturbance trajectory was formed based on the matched location points， which was sent to the server by the user. The mechanism was evaluated comprehensively by confusion quality and Root Mean Square Error （RMSE）. Compared with the GeoInd algorithm， the data quality loss of the DP-TLPM is reduced by 24% and the confusion quality of the trajectories is improved by 52%， verifying the effectiveness of DP-TLPM in terms of both privacy protection strength and data quality.

Deep learning models have achieved remarkable results in remote sensing image classification. With the continuous collection of new remote sensing images， when the remote sensing image classification models based on deep learning train new data to learn new knowledge， their recognition performance of old data will decline， that is， old knowledge forgetting. In order to help remote sensing image classification model consolidate old knowledge and learn new knowledge， a remote sensing image classification model based on sample incremental learning， namely ICLKM （Incremental Collaborative Learning Knowledge Model） was proposed. The model consisted of two knowledge networks. The first network mitigated knowledge forgetting by retaining the output of the old model through knowledge distillation. The second network took the output of new data as the learning objective of the first network and effectively learned new knowledge by maintaining the consistency of the dual network models. Finally， two networks learned together to generate more accurate model through knowledge collaboration strategy. Experimental results on two remote sensing datasets NWPU-RESISC45 and AID show that， ICLKM has the accuracy improved by 3.53 and 6.70 percentage points respectively compared with FT （Fine-Tuning） method. It can be seen that ICLKM can effectively solve the knowledge forgetting problem of remote sensing image classification and continuously improve the recognition accuracy of known remote sensing images.

Aiming at the correlation problem between sleep stages and sleep apnea hypopnea， a sleep physiological time series classification method based on adaptive multi-task learning was proposed. Single-channel electroencephalogram and electrocardiogram were used for sleep staging and Sleep Apnea Hypopnea Syndrome （SAHS） detection. A two-stream time dependence learning module was utilized to extract shared features under joint supervision of the two tasks. The correlation between sleep stages and sleep apnea hypopnea was modeled by the adaptive inter-task correlation learning module with channel attention mechanism. The experimental results on two public datasets indicate that the proposed method can complete sleep staging and SAHS detection simultaneously. On UCD dataset， the accuracy， MF1（Macro F1-score）， and Area Under the receiver characteristic Curve （AUC） for sleep staging of the proposed method were 1.21 percentage points， 1.22 percentage points， and 0.008 3 higher than those of TinySleepNet； its MF2 （Macro F2-score）， AUC， and recall of SAHS detection were 11.08 percentage points， 0.053 7， and 15.75 percentage points higher than those of the 6-layer CNN model， which meant more disease segments could be detected. The proposed method could be applied to home sleep monitoring or mobile medical to achieve efficient and convenient sleep quality assessment， assisting doctors in preliminary diagnosis of SAHS.

At present， the phenomenon of network densification accelerates the degradation of channel transmission performance. And the widely used evaluation methods face significant challenges in evaluating channel transmission performance due to their limited consideration of parameters and constrained applicability. In response to the difficulties in evaluating channel transmission performance， a method for evaluating multi-parameter channel transmission performance through an improved Transmission Control Protocol/Internet Protocol （TCP/IP） frame structure was proposed. Firstly， the standardized test data was generated， including pseudo-random codes， basic curve data， and custom curve data， so as to ensure that the test data follow a uniform standard. Secondly， an improved TCP/IP frame structure was employed to package test data information， including total frame quantity and frame sequences， into the TCP/IP frames. In this way， the sending， receiving and parsing of test data were realized， and the statistics on basic channel transmission variables were completed， such as the number of frames by type， the number of frames by length， the total number of frames， and the volume of effective data. Finally， the received data were analyzed to obtain two types of high-level channel transmission information， namely frame error rate and bit error rate， completing the overall evaluation of the channel transmission performance. The designed method employed six parameters to evaluate channel quality， with the evaluation precision of the method reaching 0.01% and maintaining a minimum error margin of 0.01%. It is compatible with all communication channels using TCP/IP. Experimental results demonstrate that the proposed channel transmission performance evaluation method can perform the statistics and analysis of the six channel communication information， and evaluating the channel transmission performance accurately.

On the premise of ensuring the properties of nodes in the network， the purpose of attribute network representation learning is to learn the low-dimensional dense vector representation of nodes by combining structure and attribute information. In the existing attribute network representation learning methods， the learning of attribute information in the network is ignored， and the interaction of attribute information with the network topology is insufficient， so that the network structure and attribute information cannot be fused efficiently. In response to the above problems， a Dual auto-Encoder Network Representation Learning （DENRL） algorithm was proposed. Firstly， the high-order neighborhood information of nodes was captured through a multi-hop attention mechanism. Secondly， a low-pass Laplacian filter was designed to remove the high-frequency signals and iteratively obtain the attribute information of important neighbor nodes. Finally， an adaptive fusion module was constructed to increase the acquisition of important information through the consistency and difference constraints of the two kinds of information， and the encoder was trained by supervising the joint reconstruction loss function of the two auto-encoders. Experimental results on Cora， Citeseer， Pubmed and Wiki datasets show that DENRL algorithm has the highest clustering accuracy and the lowest algorithm running time on three citation network datasets compared with DeepWalk， ANRL （Attributed Network Representation Learning） and other algorithms， achieves these two indicators of 0.775 and 0.460 2 s respectively on Cora datasets， and has the highest link prediction precision on Cora and Citeseer datasets， reaching 0.961 and 0.970 respectively. It can be seen that the fusion and interactive learning of attribute and structure information can obtain stronger node representation capability.

The goal of Camouflage Object Segmentation （COS） is to detect hidden objects from the background. In recent years， Camouflage Object Detection （COD） based on Convolutional Neural Network （CNN） has developed rapidly， but there is still a problem that the complete object cannot be accurately detected in scenes with highly similar foreground/background. For the above problem， a COS method based on Channel Attention （CA） and edge fusion， called CANet （Network based on Channel Attention and edge fusion）， was proposed to obtain a complete segmentation result with clearer edge details of camouflage objects. Firstly， the SE （Squeeze-and-Excitation） attention was introduced to extract richer high-level semantic features. Secondly， an edge fusion module was proposed to restrain interference in low-level features and make full use of edge details information of the image. Finally， a channel attention module based on depthwise separable convolution was designed to gradually integrate cross-level multi-scale features in a top-down manner， which further improved detection accuracy and efficiency. Experimental results on multiple public COD datasets show that compared to eight mainstream methods such as SINet （Search Identification Net）， TINet （Texture-aware Interactive guidance Network） and C²FNet （Context-aware Cross-level Fusion Network）， CANet performs better and can obtain rich camouflage objects’ internal and edge detail information. Among them， CANet improves the structure-measure index by 2.6 percentage points compared to SINet on the challenging COD10K dataset. CANet has superior performance and is suitable for medical detection of lesion areas similar to human tissue， military detection of hidden targets， and other related fields.

Aiming at the classification problems of Three-Dimensional （3D） models， a method of few-shot recognition of 3D models based on Transformer was proposed. Firstly， the 3D point cloud models of the support and query samples were fed into the feature extraction module to obtain feature vectors. Then， the attention features of the support samples were calculated in the Transformer module. Finally， the cosine similarity network was used to calculate the relation scores between the query samples and the support samples. On ModelNet 40 dataset， compared with the Dual-Long Short-Term Memory （Dual-LSTM） method， the proposed method has the recognition accuracy of 5-way 1-shot and 5-way 5-shot increased by 34.54 and 21.00 percentage points， respectively. At the same time， the proposed method also obtains high accuracy on ShapeNet Core dataset. Experimental results show that the proposed method can recognize new categories of 3D models more accurately.

Abnormal situations inside the vehicle battery cannot be predicted and warned in time， which leads to electric vehicle accidents and brings serious threats to drivers and passengers’ life and property safety. Aiming at the above problem， a Contrastive Transformer Encoder Decoder （CT-ED） model was proposed for multivariate time series anomaly detection. Firstly， different views of an instance were constructed through data augmentation， and the local invariant features of the data were captured by contrastive learning. Then， based on Transformer， the data were encoded from two perspectives of time dependence and feature dependence. Finally， the data were reconstructed by the decoder， and the reconstruction error was calculated as the anomaly score to detect anomalies of the machine under the actual operating conditions. Experimental results on SWaT， SMAP， MSL three public datasets and Electric Vehicle power battery （EV） dataset show that compared to the suboptimal model， the F1-scores of the proposed model increase by 6.5%， 1.8%， 0.9%， and 7.1% respectively.The above results prove that CT-ED is suitable for anomaly detection under different operating conditions， and balancing the precision and recall of anomaly detection.

Aiming at the problem of artificial artifacts due to phase disorder in frequency-domain speech enhancement algorithms， which limits the denoising performance and decreases the speech quality， a speech enhancement algorithm based on Multi-Scale Ladder-type Time-Frequency Conformer Generative Adversarial Network （MSLTF-CMGAN） was proposed. Taking the real part， imaginary part and magnitude spectrum of the speech spectrogram as input， the generator first learned the local and global feature dependencies between temporal and frequency domains by using time-frequency Conformer at multiple scales. Secondly， the Mask Decoder branch was used to learn the amplitude mask， and the Complex Decoder branch was directly used to learn the clean spectrogram， and the outputs of the two decoder branches were fused to obtain the reconstructed speech. Finally， the metric discriminator was used to judge the scores of speech evaluation metrics， and high-quality speech was generated by the generator through minimax training. Comparison experiments with various types of speech enhancement models were conducted on the public dataset VoiceBank+Demand by subjective evaluation Mean Opinion Score （MOS） and objective evaluation metrics.Experimental results show that compared with current state-of-the-art speech enhancement method CMGAN （Comformer-based MetricGAN）， MSLTF-CMGAN improves MOS prediction of the signal distortion （CSIG） and MOS predictor of intrusiveness of background noise （CBAK） by 0.04 and 0.07 respectively， even though its Perceptual Evaluation of Speech Quality （PESQ） and MOS prediction of the overall effect （COVL） are slightly lower than that of CMGAN， it still outperforms other comparison models in several subjective and objective speech evaluation metrics.

Aiming at the problem of resource auction mechanism in cloud environment， a more General multi-unit FAlse-name-proof auction mechanism for vIrTual macHine allocation （GFAITH） was studied and designed. First， the system model was formally defined. Then， around the design goals of being truthfulness and false-name-proof， it was proved that when considering the diversity of user demands， a new form of cheating， Demand-Reduction （DR） cheating， would emerge， which could destroy the truthful and false-name-proof properties， and the experimental results show that it would seriously affect the system performance. Based on the above， the GFAITH was proposed to achieve the design goals in three stages： user pre-processing， pre-allocation and pricing， and resisting demand reduction cheating. It is theoretical proved that the resource allocation of GFAITH is feasible and able to resist false-name-proof. Experimental results show that GFAITH can effectively guarantee the performance of the system from indicators such as revenue and social wealth， verifying the effectiveness and efficiency of the proposed mechanism.

Aiming at the complicated and time-consuming problems of brushed Direct-Current （DC） motor Proportion Integral Differential （PID） parameter tuning， a PID parameter tuning method based on improved Genetic Algorithm （GA） was proposed. Firstly， a fitness enhanced elimination through selection rule was proposed， which improved the selection process of traditional GA. Then， a gene infection crossover method was proposed to ensure the increase of the average fitness value in the evolution process. Finally， the unnecessary copy operation in traditional GA was deleted to improve the running speed of the algorithm. Modeling and simulation analysis were carried out through the motor transfer function. Experimental results show that， compared with conventional tuning methods， the proposed improved GA can significantly improve the PID parameter tuning effect. At the same time， compared with the traditional GA， the improved GA reduces the evolutionary generation number required to achieve the same evolutionary effect by 79%， and increases the running speed of the algorithm by 4.1%. The proposed improved GA improves GA from the two key operation steps of selection and crossover， and is applied to PID parameter tuning to make the rise time less， the stability time shorter， and the overshoot smaller.

To eliminate the most common problem of the flooding of Address Resolution Protocol (ARP) messages in Ethernet, a new ARP proxy mechanism, taking account of hybrid environment of Software-Defined Network (SDN) and traditional network was proposed environment. In this mechanism, the advantage of network-wide view of SDN paradigm was used to register hosts information once accessing into the network and update the records in real-time by keeping trace of hosts' dynamics and network failure. Thus, most ARP request messages could be responded directly by the controller. The evaluation results show that this proposed scheme reserves the auto-configuration characteristic, which is transparent to hosts, compatible with the existed hardware without any changes, reduces network traffic and allows redundant links existed in network, so as to improve the scalability of the Ethernet.

To achieve high-performance and practical space manipulator control software, a software architecture for space manipulator control system based on multithreading and round-robin queue was proposed under the C/S structure, as well the details of implementation. After analyzing the features and functional requirements of space manipulator control software, the various functions of manipulator control software were distributed to four different parallel threads, according to the principle of transverse block and vertical stratification, and two round-robin queues were created for caching, to improve the control systems data processing ability and reduce unnecessary waiting time. Those four threads and two round-robin queues communicated with each other to work together. The experimental results show that it is easy enough to control space manipulators with a short delay through this software architecture, and the performance meets the actual needs, which proves the effectiveness and feasibility of this scheme.

Images captured in hazy weather suffer from poor contrast and low visibility. This paper proposed a single image defogging algorithm to remove haze by combining with the characteristics of HSI color space. Firstly, the method converted original image from RGB color space to HSI color space. Then, based on the different affect to hue, saturation and intensity, a defogged model was established. Finally, the range of weight in saturation model was obtained by analyzing original images saturation, then the range of weight in intensity model was also estimated, and the original image was defogged. In comparison with other algorithms, the experimental results show that the running efficiency of the proposed method is doubled. And the proposed method effectively enhances clarity, so it is appropriate for single image defogging.

Concerning the defect that the traditional filtering window cannot be adaptively extended and the standard mean filter algorithm could blur edges easily, a new adaptive weighted mean filtering algorithm based on city block distance was proposed. First, the noise points can be detected with switch filtering ideas. Then, for each noise point, the window was extended according to the city block distance, and the window size was adaptively adjusted based on the number of signal points within the window. Last, the weighted mean of the signal points in the window was taken as the gray value of the noise points to achieve the effective recovery of the noise points. The experimental results show that the algorithm can effectively filter out salt-and-pepper noise, especially for the larger-noise-density image, and denoising effect is more significant.

Considering the applications deployed in the cloud which are rather complicated and different applications exhibit different sensitivity to issues of specific resources, an architecture based main mode method was proposed to classify applications into CPU-intensive, memory-intensive, network-intensive, and I/O-intensive precisely, enabling better scheduling of resources in the cloud; An ARIMA (AutoRegressive Integrated Moving Average) model-based prediction algorithm, which was also implemented, can lower average prediction error (7.59% high average forecast error and 6.06% low average forecast error) when forecasting consumption of resources; Appropriate modifications have been made on the traditional virtualization-based application cloud architecture to solve the inflexibility and inefficiency of the architecture based on virtual machine.

To solve the Small Sample Size (SSS) problem and the "inferior" problem of traditional Fisher Linear Discriminant Analysis (FLDA) when it is applied to Synthetic Aperture Radar (SAR) image recognition tasks, a new image feature extraction technique was proposed based on weighted two-directional and two-dimensional linear discriminant analysis (W(2D)2LDA). First, the scatter matrices in the two-directional and two-dimensional linear discriminant analysis criterion were modified by adding weights. Then, feature matrix was extracted by W(2D)2LDA. The experimental results with MSTAR dataset verify the effectiveness of the proposed method, and it can strengthen the feature's discrimination and obtain better recognition performance with fewer memory requirements simultaneously.

In a time flooded with massive spam messages, clearing them waste a huge amount of effort and time. The mining sent feature of spam messages is the key to solving this problem. On the basis of analyzing current text-message filtering mechanisms, an effective interaction period is proposed by combining the discrete interaction units of the message sender into a consecutive interaction unit according to the essence of median filter. Utilizing the ratio of input to output and Effective Interaction Period (EIP), a general filtering algorism of spam message is built. Experimenting on 20 millions real messages, the recall ratio of the proposed algorithm is 99.51% and the precision ratio is 49.90%. The experimental results indicate that the novel algorism greatly enhances the efficiency and velocity of detection, which can be applied to spam messages real-time intercepted technology.

Through analyzing the advantages and disadvantages of traditional immersive display systems, a new immersive display system was designed and implemented. In this system, the light illuminated by one projector was reflected by a cylindrical reflector to a cambered rear projection screen. Seamless projection picture can be obtained and coherent displaying of wideangle virtual scene could be implemented. By properly designing the surface of cylindrical reflector, it implemented uniform enlargement of the image on the horizontal direction. The distortion of the projection picture caused by the curvature of the screen could be basically removed through prior distortion of projected image and participants could acquire more moving freedom by means of rear projection. This system overcame the problem of image mosaics in traditional multiprojector and multidisplay immersive display systems. It is simple for manufacturing and can achieve satisfying immersive display quality, which is verified by the experimental result.

To meet the simulation need about the damage features of active load section of protective engineering, precise damage analysis was designed on module and workflow mechanism. Following the current rule of military damage phenomena and simulation design, the precise damage analysis function and module were described by models, the UML use case and its design goal of such system were clearly put forward. On the basis of cell modules, which were optimally decomposed from the precise damage analysis on active load section of protective engineering, the modular designing idea and logical frame were advanced, the function model and data flow mechanism were erected, and the precise simulation damage analysis was carried out based on window thread design. Finally, a case study shows its good alteration, which provides the effective simulation tool for the essential vulnerability about the active load section of protective engineering.

To solve the semantic heterogeneity and sharing problems in the simulation process of precision guided ammunition operations, the entity and domino effect model was erected on the basis of ontology engineering. With the system model description of the precision guided ammunition and its damage effect, the description semantics was advanced by the precision guided ammunition ontology. Then the solution for design and construction of ontology was given, and the ontology system for precision guided ammunition domain was determined. Based on the ample share foundation of precision guided ammunitions, the simulation precision guided ammunition model was practically designed and applied to the damage simulation experiment from the guided ammunition to protective structure, which established the foundation for the knowledge management of the entity and damage effect with ontology engineering.

Many current Radio Frequency IDentification （RFID） authentication protocols cannot conform with the EPC Class 1 Gen 2 (EPC Gen2) standards or cannot meet the requirements of low-cost tags for the RFID authentication protocol. A new RFID authentication protocol based on the EPC Class 1 Gen 2 (EPC Gen2) standards was proposed and the security proof was given with BAN logic. After analyzing the security, the proposed protocol can meet the RFID security demands: information confidentiality, data integrity and identity authentication.

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.

This paper integrated the characteristics of the grey absolute relation with the advantages of the median filter to combine the pixels within the n×n template into two sequences, where n is an odd number that is greater than or equal to 3. Then, the characteristics of the grey absolute relation were used to determine the similarity between the two sequences. Finally, the degree of similarity was adopted to determine whether the current pixel is noise or not, and then the value of median filter was used to replace the noise one. The experimental results show that this algorithm has better filtering effect than the standard median filter method and other filtering methods while keepings more details of the original image.

For effectively ensuring users' privacy and data security of the Radio Frequency Identification (RFID) system, a new RFID mutual authentication protocol was proposed in this paper, which was designed by the method of combining elliptic curves and Weil pairing. In this protocol, the mutual authentication and anonymous authentication were realized, and the traffic analysis attack, impersonation attack and replay attack were resisted. Compared with the random Hash lock, Hash chain and New-Gen2, this protocol can resist most of the attacks which have been discovered.

Concerning the defects of the current clustering algorithm for its dependence only on the initial clustering center and failure in exactly distinguishing classes of non-concave shape, this paper applied the knowledge of group learning into the clustering algorithm and proposed the anomaly intrusion detection algorithm P-BFS based on graph clustering. In order to obtain more correct classification model, this algorithm introduced a measurement method of data points similarity based on the approximate function. The experimental results suggest the advantages of the application of the graph clustering algorithm in the intrusion detection system. In addition, it indicates that compared with the classical K-means clustering algorithm, P-BFS has better performance.

Security routing protocol is a key element to guarantee satellite network security. To solve the problem that most of routing protocols lack security scheme, the Elliptic Curve Pintsov-Vanstone Signature Scheme (ECPVSS) was used to attain confidentiality and authentication of packets, and trust evaluation scheme could exclude internal malicious node from the route path. Then a security routing protocol oriented to High Altitude Platform (HAP)/Low Earth Orbit (LEO) architecture was formed. The analysis results show that the proposed protocol can prevent network from some common routing attacks.