Speaker Identification （SI） in novels aims to determine the speaker of a quotation by its context. This task is of great help in assigning appropriate voices to different characters in the production of audiobooks. However， the existing methods mainly use fixed window values in the selection of the context of quotations， which is not flexible enough and may produce redundant segments， making it difficult for the model to capture useful information. Besides， due to the significant differences in the number of quotations and writing styles in different novels， a small number of labeled samples cannot enable the model to fully generalize， and the labeling of datasets is expensive. To solve the above problems， a novel speaker identification framework that integrates narrative units and reliable labels was proposed. Firstly， a Narrative Unit-based Context Selection （NUCS） method was used to select a suitable length of context for the model to focus highly on the segment closest to the quotation attribution. Secondly， a Speaker Scoring Network （SSN） was constructed with the generated context as input. In addition， the self-training was introduced， and a Reliable Pseudo Label Selection （RPLS） algorithm was designed to compensate for the lack of labeled samples to some extent and screen out more reliable pseudo-label samples with higher quality. Finally， a Chinese Novel Speaker Identification corpus （CNSI） containing 11 Chinese novels was built and labeled. To evaluate the proposed framework， experiments were conducted on two public datasets and the self-built dataset. The results show that the novel speaker identification framework that integrates narrative units and reliable labels is superior to the methods such as CSN （Candidate Scoring Network）， E2E_SI and ChatGPT-3.5.

Aiming at the problems that the Total Variation （TV） minimization method easily leads to image over-smoothing and block effects in Low-Dose Computed Tomography （LDCT） image reconstruction， an LDCT image reconstruction method based on low-rank and TV joint regularization was proposed to improve the visual quality of LDCT reconstructed images. Firstly， a low-rank and TV joint regularization based image reconstruction model was established， thus， more accurate and natural reconstruction results were obtained theoretically. Secondly， a low-rank prior with non-local self-similarity property was introduced to overcome the limitations of only using the TV minimization method. Finally， the Chambolle-Pock （CP） algorithm was used to optimize and solve the model， which improved the solution efficiency of the model and ensured the effective solution of the model. The effectiveness of the proposed method was verified under three different LDCT scanning conditions. Experimental results on Mayo dataset show that compared with the PWLS-LDMM （Penalized Weighted Least-Squares based on Low-Dimensional Manifold） method， NOWNUNM （NOnlocal Weighted NUclear Norm Minimization） method and CP method， at 25% dose， the proposed method increases the Visual Information Fidelity （VIF） by 28.39%， 8.30% and 2.93%， respectively； at 15% dose， the proposed method increases the VIF by 29.96%， 13.83% and 4.53%， respectively； at 10% dose， the proposed method increases the VIF by 30.22%， 17.10% and 7.66%， respectively. It can be seen that the proposed method can retain more detailed texture information while removing noise and stripe artifacts， which verifies that the proposed method has better noise artifact suppression capability.

To address the problem of the difficulty of monitoring and controlling enterprise emissions， a Vertical Federated Learning Enterprise Emission Prediction （VFL-EEP） model with integration of electricity data was proposed by considering the premise of secure data sharing and privacy protection. Firstly， within the framework of Vertical Federated Learning （VFL）， the logistic regression model was enhanced to allow the separation of data usage and model training without leaking the monitoring data of electricity and environmental protection enterprises. Then， the logistic regression algorithm was improved to incorporate with Paillier encryption technology for ensuring the security of model parameter transmission， thereby solving the issue of insecure communication among participants in VFL effectively. Finally， through experiments on simulated data， the pollution prediction results of the proposed model were compared with those of the centralized logistic regression model. The results show that the proposed model integrates electricity data under the premise of privacy security， and has the accuracy， recall， precision， and F1 value improved by 8.92%， 7.62%， 3.95%， and 11.86%， respectively， realizing the balance between privacy protection and model performance effectively.

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）.

Deploying the YOLOv8L model on edge devices for road crack detection can achieve high accuracy， but it is difficult to guarantee real-time detection. To solve this problem， a target detection algorithm based on the improved YOLOv8 model that can be deployed on the edge computing device Jetson AGX Xavier was proposed. First， the Faster Block structure was designed using partial convolution to replace the Bottleneck structure in the YOLOv8 C2f module， and the improved C2f module was recorded as C2f-Faster； second， an SE （Squeeze-and-Excitation） channel attention layer was connected after each C2f-Faster module in the YOLOv8 backbone network to further improve the detection accuracy. Experimental results on the open source road damage dataset RDD20 （Road Damage Detection 20） show that the average F1 score of the proposed method is 0.573， the number of detection Frames Per Second （FPS） is 47， and the model size is 55.5 MB. Compared with the SOTA （State-Of-The-Art） model of GRDDC2020 （Global Road Damage Detection Challenge 2020）， the F1 score is increased by 0.8 percentage points， the FPS is increased by 291.7%， and the model size is reduced by 41.8%， which realizes the real-time and accurate detection of road cracks on edge devices.

Panoramic videos have attracted wide attention due to their unique immersive and interactive experience. The high bandwidth and low delay required for wireless streaming of panoramic videos have brought challenges to existing network streaming systems. Tile-based viewport adaptive streaming can effectively alleviate the streaming pressure brought by panoramic video， and has become the current mainstream scheme and hot research topic. By analyzing the research status and development trend of tile-based viewport adaptive streaming， the two important modules of this streaming scheme， namely viewport prediction and bit rate allocation， were discussed， and the methods in relevant fields were summarized from different perspectives. Firstly， based on the panoramic video streaming framework， the relevant technologies were clarified. Secondly， the user experience quality indicators to evaluate the performance of the streaming system were introduced from the subjective and objective dimensions. Then， the classic research methods were summarized from the aspects of viewport prediction and bit rate allocation. Finally， the future development trend of panoramic video streaming was discussed based on the current research status.

To address the issue that traditional Sequential Pattern Mining （SPM） does not consider pattern repetition and ignores the effects of utility （unit price or profit） and pattern length on user interest， a Top-k One-off high average Utility sequential Pattern mining （TOUP） algorithm was proposed. The TOUP algorithm mainly includes two core steps： average utility calculation and candidate pattern generation. Firstly， a CSP （Calculation Support of Pattern） algorithm based on the occurrence position of each item and the item repetition relation array was proposed to calculate pattern support， thereby achieving rapid calculation of the average utility of patterns. Secondly， candidate patterns were generated by itemset extension and sequence extension， and a maximum average utility upper bound was proposed. Based on this upper bound， effective pruning of candidate patterns was achieved. Experimental results on five real datasets and one synthetic dataset show that compared to the TOUP-dfs and HAOP-ms algorithms， TOUP algorithm reduces the number of candidate patterns by 38.5% to 99.8% and 0.9% to 77.6%， respectively， and decreases the running time by 33.6% to 97.1% and 57.9% to 97.2%， respectively. Therefore， the algorithm performance of TOUP is better， and it can mine patterns of interests to users more efficiently.

Aiming at the hidden danger of fire caused by electric bicycles and gas tanks taken into elevators， an improved attention mechanism was proposed to detect dangerous goods in elevator scene， and a method based on the mechanism was proposed. With YOLOX-s as the baseline model， firstly， a depthwise separable convolution was introduced in the enhanced feature extraction network to replace the standard convolution， which improved the reasoning speed of the model. Secondly， an Efficient Convolutional Block Attention Module （ECBAM） based on mixed-domain was proposed and embedded into the backbone feature extraction network. In the channel attention part of ECBAM， two fully connected layers were replaced by a one-dimensional convolution， which not only reduced the complexity of Convolutional Block Attention Module （CBAM） but also improved the detection precision. Finally， a multi-frame collaboration algorithm was proposed to reduce the false alarms of dangerous goods’ intrusion into the elevator by combining the dangerous goods detection results of multiple images. Experimental results show that compared with YOLOX-s， the improved model can increase the mean Average Precision （mAP） by 1.05 percentage points， reduce the floating point computational cost by 34.1% and reduce the model size by 42.8%. The improved model reduces false alarms in practical applications and meets the precision and speed requirements of dangerous goods detection in elevator scene.

Betweenness centrality is a common metric for evaluating the importance of nodes in a graph. However， the update efficiency of betweenness centrality in large-scale dynamic graphs is not high enough to meet the application requirements. With the development of multi-core technology， algorithm parallelization has become one of the effective ways to solve this problem. Therefore， a Parallel Algorithm of Betweenness centrality for dynamic networks （PAB） was proposed. Firstly， the time cost of redundant point pairs was reduced through operations such as community filtering， equidistant pruning and classification screening. Then， the determinacy of the algorithm was analyzed and processed to realize parallelization. Comparison experiments were conducted on real datasets and synthetic datasets， and the results show that the update efficiency of PAB is 4 times that of the latest batch-iCENTRAL algorithm on average when adding edges. It can be seen that the proposed algorithm can improve the update efficiency of betweenness centrality in dynamic networks effectively.

The parity blocks of the Maximum-Distance-Separable （MDS） code are all global parity blocks. The length of the reconstruction chain increases with the expansion of the storage system， and the reconstruction performance gradually decreases. Aiming at the above problems， a new type of Non-Maximum-Distance-Separable （Non-MDS） code called local redundant hybrid code Code-LM（s，c） was proposed. Firstly， two types of local parity blocks called horizontal parity block in the strip-set and horizontal-diagonal parity block were added in any strip-sets to reduce the length of the reconstruction chain， and the parity layout of the local redundant hybrid code was designed. Then， four reconstruction formulations of the lost data blocks were designed according to the generation rules of the parity blocks and the common block existed in the reconstruction chains of different data blocks. Finally， double-disk failures were divided into three situations depending on the distances of the strip-sets where the failed disks located and the corresponding reconstruction methods were designed. Theoretical analysis and experimental results show that with the same storage scale， compared with RDP （Row-Diagonal Parity）， the reconstruction time of CodeM（s，c） for single-disk failure and double-disk failure can be reduced by 84% and 77% respectively； compared with V²-Code， the reconstruction time of Code-LM（s，c） for single-disk failure and double-disk failure can be reduced by 67% and 73% respectively. Therefore， local redundant hybrid code can support fast recovery from failed disks and improve reliability of storage system.

In order to solve the problem of trajectory privacy leakage caused by the collection of numerous trajectory information of moving objects， a dummy trajectory-based trajectory privacy protection algorithm was proposed. In this algorithm， considering the user’s locations under disclosure， a heuristic rule was designed based on the comprehensive measure of trajectory similarity and location diversity to select the dummy trajectories， so that the generated dummy trajectories were able to effectively hide the real trajectory and sensitive locations. Besides， the trajectory directed graph strategy and the grid-based map strategy were proposed to optimize the execution efficiency of the algorithm. Experimental results on real trajectory datasets demonstrate that the proposed algorithm can effectively protect the real trajectory with high data utility.

Aiming at the problems of current aviation card readers, include poor portability, slow speed and tags' little capacity, a design method of large capacity Radio Frequency Identification (RFID) system based on STM32 was proposed. Using STM32 microprocessor as a core and adopting CR95HF radio chip, a new handled RFID card reader which worked in High Frequency (HF) and supported ISO 15693, ISO 18092 protocols was designed. The design of power, antenna and optimization of software speed, error rate was discussed in detail. A new large compiled capacity passive tag was also designed whose capacity is up to 32KB to form a large capacity RFID system with card reader. The experimental results show that, compared with the traditional card reader, the reading and writing speed of the card reader increases by 2.2 times, error rate reduces by 91.7% and tag capacity increases 255 times. It provides a better choice for fast, accurate and high data requirements of aviation logistics.

Concerning the problem that pedestrians would be partially or seriously shaded by each other in video monitoring, this paper proposed a people counting algorithm based on human body skeleton feature. At first, the initial human skeleton was extracted by morphological skeleton extraction algorithm. Then the optimal skeleton feature was obtained by eliminating outliers and pseudo branches. Finally, this paper established a head detection response rule through analyzing the characteristics of skeleton in head areas to detect the head of pedestrian, and completed people counting by counting the heads of pedestrians. The experimental results show that the algorithm can solve the problems of partial and serious shading in video monitoring. For relatively sparse scene, the overall people counting accuracy rate of the algorithm is about 95%.

The current network relationship analysis mainly studies the association relationship or group relations between users. Due to the variety of characteristic relation between users in mobile communication network, the relationship between the users and the groups are also diverse. On the basis of the specific groups with certain communication correlation and location similarity in the mobile communication network, the position prediction was introduced to the correlation measurement of position item, the location trajectory correlation measurement criterion was established, and an association user mining algorithm was proposed. The experimental result indicates that the proposed method can achieve the measuring of the relationship between users and the groups, and discover potential users associated with specific groups.

Beidou satellite has been used as external illuminator due to its features such as continuous coverage to our country, less motion relative to the earth, unapparent Doppler frequency, simple disturbance of adjacent channel and high security. Taking account of the location error caused by atmospheric refraction, a location error correction method was given, to further improve the position accuracy. The simulation results show that radar position errors could reduce correspondingly with the increase of elevation or the decrease of target altitude. Passive radar position accuracy is improved greatly by correcting atmospheric refraction.

The traditional fault monitoring methods have some problems such as great deviation and slow speed. To solve these problems, a link fault monitoring algorithm based on the wavelet transform was presented. This algorithm used the dynamic polling scheme to detect the optical power and used the local characteristic in time-frequency domain of the wavelet transform to extract the fault information from the detection value. The monitoring optical power value was decomposed with multi-scale to eliminate the effects of noise, thereby improving the accuracy of the fault monitoring. The experimental results show that compared to the analytucal methods in time domain, the proposed fault monitoring algorithm based on wavelet transform is better to overcome the effects of noise. The leakage alarm rate is reduced to zero and the false alarm rate is decreased by five percentage. The monitoring time is between 2.53ms and 3.12ms, which can meet the real-time requirement.

For the Chameleon algorithm using distance function to measure the similarity of data points, resulting in that the two proximate points may only have a few common characteristics, minimum half has practical difficulties, the merger needs artificial specified threshold value, and can not be revoked once the merger is completed. Therefore, the authors improved Chameleon algorithm and proposed a new Chameleon algorithm using Weighted Shared nearest neighbors Graph (WSnnG). Firstly, it measured the similarity by using the number of shared nearest neighbors, further constructed the WSnnG. Secondly, it resolved minimum half through the introduction of the network module evaluation function, then according to the structural equivalence similarity degree as a basis for merger. Finally, a new cohesion measure was discussed to solve problems that can not be revoked after the merger. The experimental results on UCI data sets and four two-dimensional artificial data sets show that the improved Chameleon algorithm using WSnnG has greatly improved in clustering accuracy and running time.

Since there are deficiencies in tracking moving object based on either color feature or gradient feature under complex background, a new algorithm CG_CamShift was proposed with the combination of the two features. This algorithm made full use of the color histogram description of the overall goal and the gradient orientation histogram description of the structural information, and predicted the position of the moving object in combination with the Kalman filtering. It resolved the problem of losing object caused by illumination and shading under complicated background. The experimental results show that the algorithm enhances the tracking accuracy while guaranteeing the real-time performance. In addition, it has stronger robustness.

Feature selection algorithm based on the Information Gain (IG) can solve the problem of high-dimension and magnanimous data in intrusion forensics, but it neglects the correlation between features, which can lead to the redundancy of features, and affect the speed and accuracy of intrusion forensics. Therefore, an Improved Information Gain (IIG) algorithm based on feature redundancy was proposed. In the improved algorithm, the irrelevant features and the redundant features were removed by adding the judgments of redundancy between features, which effectively simplified feature subset. The experimental results show that the proposed algorithm can effectively select features, ensure detection accuracy and improve processing speed.

A new kind of computer aided conceptual design method was studied, control parameter of the skeleton structure theory was analyzed, the radian as control parameter was increased and the entities were analyzed. The skeleton structure parameters were optimized by Genetic Algorithm (GA), and the quality of the results of GA was optimized by controlling parents' codes. The experiment used Visual C++ as the platform, and ACIS-HOOPS class as foundation. A conceptual design of chandelier shape was given, after abstracting skeleton structure, modeling, genetic variation and producing results of conceptual design steps, a large number of overall coordination and elegant chandelier forms were available for user to choose.

With the advancement of Graphics Processing Unit (GPU) and the creation of its new feature of programmability, it has come possible to transfer some of the processing stages in general numerical algorithms from CPU to GPU in order to accelerate the computation. In this paper, starting from a brief introduction to Alternative Direction Implicit Finite Difference Time Domain (ADI-FDTD) algorithm, detailed introduction and analysis were given to the fundamentals and the key technique of GPU for accelerating ADI-FDTD computation, in combination with the implementation frame of the conjugate gradient method for solving linear equations system on GPU. Finally, some computed examples were presented, and various comparisons were made to prove the efficiency and accuracy of this acceleration approach.

The multi-designated verifiers signature is a special digital signature that the signature could only be checked by several designated verifiers. A multi-designated verified signature scheme and an identity based multi-designated verified signature scheme were proposed based on the technique of multilinear forms. The security analysis of the schemes shows that they have the property of unforgeability, source hiding and privacy of signer's identity.

The Fuzzy Clustering （FC） theory into information filtering system based on Genetic Algorithm （GA） was introduced, to cluster the training texts by using the fuzzy similar matrix depending on each individual of GA's species, and the species according to the results of clustering with the proposed fitness function was evaluated. Doing GA operators using this evaluation method iteratively, we got a template which expressed user's interest better, to improve the precision of information filtering. Then, we did experiment to test the efficiency by applying this method to a designed information filtering system.

Comprehensive review of the research of one kind of anti-collision algorithms and analysis of their disadvantages were provided. Based on the analysis of the frame size adjusting method of the most in common use branch dynamic framed slotted Aloha algorithm, this paper brought forward an improved resolution by dynamically adjusting the frame size and group division, which can consumedly enhance the system efficiency and its stability.

In a multi-system heterogeneous LAN, the lack of interoperability for the MPI program in the different operating environments makes it difficult to make full use of the LAN computing resources in the parallel Finite Difference Time Domain(FDTD) algorithm. To resolve this problem, a new method of using Serverce Message Block（SMB） protocol to realize heterogeneous FDTD computing was presented, and by some means such as memoryfile access, memorymapped array and redundancy computing, the impact of SMB communication latency upon the parallel performance was released. Some computed examples were given to prove the feasibility and accuracy of this method. The numerical results show that performance indexes, such as speedup, parallel efficiency, etc. are close to the isomorphism FDTD computing using MPI.

Anti-collision protocol in EPC Gen2 protocol is flexible, so reasonable algorithm can evidently improve the system performance. Based on the research of its Q value adjusting method and the process of this important protocol, a new Q value adjusting method and an improved slotted random Aloha method were proposed. The simulation results show that the improved algorithms can increase the system throughput and lower the delay of tag identification, showing a good performance.

As a common disaster on transmission lines， ice disaster affects the safe operation of the power system seriously. A digital twin system for ice shedding of overhead transmission lines was designed and implemented to meet the precise mapping and real-time communication and visualization presentation requirements of de-icing vibrations of actual transmission lines. Firstly， the idea of real-time mapping of physical information was introduced， and the overall framework of the transmission line de-icing digital twin system was built based on the Unity3D platform. Then， an analytical solution based iterative model during any time period was established and solved， and the iterative model was combined with the measured time-varying parameters for segmented iterative correction， so as to complete modeling of the digital twin model. At the same time， the calculated twin data were displayed on the twin interaction platform for transmission line de-icing vibrations. Finally， this system was applied to simulate successive three levels of conductor ice shedding to verify the effectiveness of the model. Experimental results show that the results calculated by the twin model are highly consistent with the actual measurements， and the Mean Absolute Percentage Error （MAPE） of the twin model system is within 0.5%. It can be seen that the proposed method can establish an accurate model that reflects the operating status of the transmission lines.