Aiming at the mechanical system related to multi-component failure， a reliability evaluation method of multi-component system based on time-varying Copula function was proposed. Firstly， the nonlinear Wiener process was introduced to characterize the performance degradation process， and the Copula function was used to characterize the correlation between multiple component failures. Secondly， based on the evolutionary equation of the Copula function approximation of the Fourier series， the fitting effects of the Fourier series on common time-varying forms were verified by Monte Carlo （MC） simulation. In addition， the likelihood ratio statistic was used to test the existence of time-varying correlation， indicating the necessity of time-varying correlation research. The example analysis shows that compared with the static correlation model， the time-varying correlation model has the log-likelihood function value increased by 4.36%， and the Akaike Information Criterion （AIC） decreased by 3.81%， achieving more accurate reliability evaluation results.
By comprehensively comparing the models of traditional knowledge graph representation learning， including the advantages and disadvantages and the applicable tasks， the analysis shows that the traditional single-modal knowledge graph cannot represent knowledge well. Therefore， how to use multimodal data such as text， image， video， and audio for knowledge graph representation learning has become an important research direction. At the same time， the commonly used multimodal knowledge graph datasets were analyzed in detail to provide data support for relevant researchers. On this basis， the knowledge graph representation learning models under multimodal fusion of text， image， video， and audio were further discussed， and various models were summarized and compared. Finally， the effect of multimodal knowledge graph representation on enhancing classical applications， including knowledge graph completion， question answering system， multimodal generation and recommendation system in practical applications was summarized， and the future research work was prospected.
Internet of Vehicles （IoV） traffic monitoring requires the transmission， storage and analysis of private data of users， making the security guarantee of private data particularly crucial. However， traditional security solutions are often hard to guarantee real-time computing and data security at the same time. To address the above issue， security protocols， including two initialization protocols and a periodic reporting protocol， were designed， and a Software Guard Extensions （SGX）-based IoV traffic monitoring Secure Data Processing Framework （SDPF） was built. In SDPF， the trusted hardware was used to enable the plaintext computation of private data in Road Side Unit （RSU）， and efficient operation and privacy protection of the framework were ensured through security protocols and hybrid encryption scheme. Security analysis shows that SDPF is resistant to eavesdropping， tampering， replay， impersonation， rollback， and other attacks. Experiment results show that all computational operations of SDPF are at millisecond level， specifically， all data processing overhead of a single vehicle is less than 1 millisecond. Compared with PFCF （Privacy-preserving Fog Computing Framework for vehicular crowdsensing networks） based on fog computing and PPVF （Privacy-preserving Protocol for Vehicle Feedback in cloud-assisted Vehicular Ad hoc NETwork （VANET）） based on homomorphic encryption， SDPF has the security design more comprehensive： the message length of a single session is reduced by more than 90%， and the computational cost is reduced by at least 16.38%.
Aiming at the problem of category vocabulary noise and label noise in weakly-supervised text classification tasks， a weakly-supervised text classification model with label semantic enhancement was proposed. Firstly， the category vocabulary was denoised on the basis of the contextual semantic representation of the words in order to construct a highly accurate category vocabulary. Then， a word category prediction task based on MASK mechanism was constructed to fine-tune the pre-training model BERT （Bidirectional Encoder Representations from Transformers）， so as to learn the relationship between words and categories. Finally， a self-training module with label semantics introduced was used to make full use of all data information and reduce the impact of label noise in order to achieve word-level to sentence-level semantic conversion， thereby accurately predicting text sequence categories. Experimental results show that compared with the current state-of-the-art weakly-supervised text classification model LOTClass （Label-name-Only Text Classification）， the proposed method improves the classification accuracy by 5.29， 1.41 and 1.86 percentage points respectively on the public datasets THUCNews， AG News and IMDB.
The session-based recommendation algorithm based on Recurrent Neural Network （RNN） can effectively model the long-term dependency in the session， and can combine the attention mechanism to describe the main purpose of the user in the session. However， it cannot bypass the items that are not related to the user’s main purpose in the process of session modeling， and is susceptible to their influence to reduce the recommendation accuracy. In order to solve problems， a new paired coding scheme was designed， which transformed the original input sequence embedding vector into a three-dimensional tensor representation， so that non-adjacent behaviors were also able to be linked. The tensor was processed by a two-dimensional Conventional Neural Network （CNN） to capture the relationship between non-adjacent items， and a Neural Attentive Recommendation Machine introducing two-dimensional COnvolutional neural network for Session-based recommendation （COS-NARM） model was proposed. The proposed model was able to effectively skip items that were not related to the user’s main purpose in the sequence. Experimental results show that the recall and Mean Reciprocal Rank （MRR） of the COS-NARM model on multiple real datasets such as DIGINETICA are improved to varying degrees， and they are better than those of all baseline models such as NARM and GRU-4Rec+. On the basis of the above research， Euclidean distance was introduced into the COS-NARM model， and the OCOS-NARM model was proposed. Euclidean distance was used to directly calculate the similarity between interests at different times to reduce the parameters of model and reduce the complexity of model. Experimental results show that the introduction of Euclidean distance further improves the recommendation effect of the OCOS-NARM model on multiple real datasets such as DIGINETICA， and makes the training time of the OCOS-NARM model shortened by 14.84% compared with that of the COS-NARM model， effectively improving the training speed of model.
In order to solve the problems such as high time cost， inaccuracy and influence of parameter setting on algorithm performance when optimizing parameters of Convolutional Neural Network （CNN） by traditional manual methods， a variable Convolutional AutoEncoder （CAE） method based on Teaching-Learning-Based Optimization （TLBO） was proposed. In the algorithm， a variable-length individual encoding strategy was designed to quickly construct the CAE structure， and stack CAEs to a CNN. In addition， the excellent individual structure information was fully utilized to guide the algorithm to search the regions with more possibility， thereby improving the algorithm performance. Experimental results show that the classification accuracy of the proposed algorithm achieves 89.84% when solving medical image classification problems， which is higher than those of traditional CNN and similar neural networks. The proposed algorithm solves the medical image classification problems by optimizing the CAE structure and stacking CNN， and effectively improves the classification accuracy of medical image classification.
In order to solve the problem that static attribute reduction cannot update attribute reduction efficiently when the number of attributes in the set-valued decision information system changes continuously， an incremental attribute reduction method with knowledge granularity as heuristic information was proposed. Firstly， the related concepts of the set-valued decision information system were introduced， then the definition of knowledge granularity was introduced， and its matrix representation method was extended to this system. Secondly， the update mechanism of incremental reduction was analyzed， and an incremental attribute reduction method was designed on the basis of knowledge granularity. Finally， three different datasets were selected for the experiments. When the number of attributes of the three datasets increased from 20% to 100%， the reduction time of the traditional non-incremental method was 54.84 s， 108.01 s， and 565.93 s respectively， and the reduction time of the incremental method was 7.57 s， 4.85 s， and 50.39 s respectively. Experimental results demonstrate that the proposed incremental method is more faster than the non-incremental method under the condition that the accuracy of attribute reduction is not affected.
Aiming at efficient data acquisition, real-time precise positioning and attitude measurement problems of geostress low-frequency electromagnetic monitoring, real-time data acquisition system was designed and implemented in combination with positioning and attitude measurement module. The hardware system took ARM microprocessor (S3C6410) as control core based on embedded Linux. The hardware and software design architecture were introduced in detail. In addition, the algorithm of positioning and attitude measurement characteristics data extraction was proposed. Monitoring terminal of data acquisition and processing was designed using Qt/Embedded GUI programming technique based on LCD (Liquid Crystal Display) and achieved human-computer interaction. Meanwhile, the required data could be real-time stored to SD card. The results of system debugging and actual field experiments indicate that the system can complete the positioning and attitude data acquisition and processing, effectively solve the problem of real-time positioning for in-situ monitoring. It also can realize geostress low-frequency electromagnetic monitoring with high-speed, real-time and high reliability.
Due to the threats of Cross-Site Scripting (XSS) attack in Online Social Network (OSN), a approach combined classifiers and improved n-gram model was proposed to detect the malicious OSN webpages infected with XSS code. Firstly, similarity-based features and difference-based features were extracted to build classifiers and the improved n-gram model. After that, the classifiers and model were combined to detect malicious webpages in OSN. The experimental results show that compared with the traditional classifier detection methods, the proposed approach is more effective and the false positive rate is about 5%.
To realize automatic and quantitative assessment in home-based upper extremity rehabilitation for stroke, an Extreme Learning Machine (ELM) based prediction model was proposed to automatically estimate the Fugl-Meyer Assessment (FMA) scale score for shoulder-elbow section. Two accelerometers were utilized for data recording during performance of 4 tasks selected from shoulder-elbow FMA and 24 patients were involved in the study. Accelerometer-based estimation was obtained by preprocessing raw sensor data, extracting data features, selecting features based on Genetic Algorithm and ELM. Then 4 single-task models and a comprehensive model were built individually using the selected features. Results show that it is possible to achieve accurate estimation of shoulder-elbow FMA score from the analysis of accelerometer sensor data with a root mean squared prediction error value of 2.1849 points. This approach breaks through the subjective and time-consuming property of traditional outcome measures which rely on clinicians at hand and can be easily utilized in the home settings.
