A new Maximum Power Point Tracking （MPPT） method， based on Self-Adaptive Particle Swarm Optimization （SAPSO）， was proposed to address the energy storage challenge in engine tandem composite turbine power generation systems. A Hybrid Energy Storage System （HESS） was introduced to augment the power capture capability of the generation system and replace single battery storage， achieving efficient and stable electrical energy storage. A control simulation model of energy storage optimization based on tandem composite turbine power generation was established using Matlab/Simulink software. The power tracking performance for various control methods and the energy storage characteristics of hybrid energy storage systems were compared and analyzed under predetermined operating conditions. Simulation results reveal that the proposed SAPSO-MPPT method outperforms the conventional P&O （Perturbation and Observation） control method， increasing power generation by 190 W and reducing response time by 0.15 s. Additionally， HESS could effectively track the demand power on the busbar， achieving power recovery efficiency of 95.3% . Finally， a test platform for the tandem composite turbine power generation system was developed using a modified Y24 engine bench to validate the fuel-saving potential of the proposed energy storage optimized control strategy. The test findings indicate that the suggested SAPSO-MPPT+HESS energy storage optimization strategy improves energy recovery efficiency by 0.53 percentage points compared to the original engine.

News text usually consists of tens to hundreds of sentences, which has a large number of characters and contains more information that is not relevant to the topic, affecting the classification performance. In view of the problem, a long text classification method combined with attention mechanism was proposed. Firstly, a sentence was represented by a paragraph vector, and then a neural network attention model of paragraph vectors and text categories was constructed to calculate the sentence's attention. Then the sentence was filtered according to its contribution to the category, which value was mean square error of sentence attention vector. Finally, a classifier base on Convolutional Neural Network (CNN) was constructed. The filtered text and the attention matrix were respectively taken as the network input. Max pooling was used for feature filtering. Random dropout was used to reduce over-fitting. Experiments were conducted on data set of Chinese news text classification task, which was one of the shared tasks in Natural Language Processing and Chinese Computing (NLP&CC) 2014. The proposed method achieved 80.39% in terms of accuracy for the filtered text, which length was 82.74% of the text before filtering, yielded an accuracy improvement of considerable 2.1% compared to text before filtering. The emperimental results show that combining with attention mechanism, the proposed method can improve accuracy of long text classification while achieving sentence level information filtering.

To deal with the problem of stance detection of Chinese social network reviews which lack theme or emotion features, a method of stance detection based on entity-emotion evolution Bayesian belief net was proposed in this paper. Firstly, three types of domain dependent entities, including noun, verb-object phrase and verb-noun compound attributive centered structure were extracted. The domain-related emotion features were extracted, and the variable correlation strength was used as a constraint on the learning of the network structure. Then the 2-dependence Bayesian network classifier was constructed to describe the dependence of entity, stance and emotion features. The stance of reviews was deducted from combination condition of entities and emotion features. Experiments were tested on Natural Language Processing & Chinese Computing 2016 (NLP&CC2016). The experimental results show that the average micro-F reaches 70.8%, and average precision of FAVOR and AGAINST increases by 4.1 percentage points and 3.1 percentage points over Bayesian network classification method with emotion features only respectively. The average micro-F on 5 target data sets of evaluation reaches 62.3%, which exceeds average level of the evaluation.

Chinese news title usually consists of a single word to dozens of words. It is difficult to improve the accuracy of news title classification due to the problems such as few characters and sparse features. In order to solve the problems, a new method for text semantic expansion based on word embedding was proposed. Firstly, the news title was expanded into triples consisting of title, subtitle and keywords. The subtitle was constructed by combining the synonym of title and the part of speech filtering method, and the keywords were extracted from the semantic composition of words in multi-scale sliding windows. Then, the Convolutional Neural Network (CNN) model was constructed for categorizing the expanded text. Max pooling and random dropout were used for feature filtering and avoidance of overfitting. Finally, the double-word spliced by title and subtitle, and the multi-keyword set were fed into the model respectively. Experiments were conducted on the news title classification dataset of the Natural Language Processing & Chinese Computing in 2017 (NLP&CC2017). The experimental results show that, the classification precision of the combination model of expanding news title to triples and CNN is 79.42% in 18 categories of news titles, which is 9.5% higher than the original CNN model without expanding, and the convergence rate of model is improved by keywords expansion. The proposed expansion method of triples and the constructed CNN model are verified to be effective.

Traditional automatic text summarization has generally no word count requirements while many social network platforms have word count limitation. Balanced performance is hardly obtained in short text summarization by traditional digest technology because of the limitation of word count. In view of this problem, a new automatic short text summarization method was proposed. Firstly, the values of relationship mapping, length mapping, title mapping and position mapping were calculated to respectively form some sets of candidate sentences. Secondly, the candidate sentences sets were mapped to abstract sentences set by multiple mapping strategies according to series of multiple mapping rules, and the recall ratio was increased by putting central sentences into the set of abstract sentences. The experimental results show that multiple mappings can obtain stable performance in short text summarization, the F measures of ROUGE-1 and ROUGE-2 tests are 0.49 and 0.35 respectively, which are better than the average level of NLP&CC2015 evaluation, proving the effectiveness of the method.

To solve poor diversity problem of user interests in content-based news recommendation and cold-start problem in hybrid recommendation, a new method of news recommendation based on fusion of content-based recommendation and collaborative filtering was proposed. Firstly, the content-based method was used to find the user's interest. Secondly, similar user group of the target user was found out by using hybrid similarity pattern which contains content similarity and behavior similarity, and the user's potential interest was found by predicting the user's interest in feature words. Next, the user interest model with characteristics of personalization and diversity was obtained by fusing user's existed interest and potential interest. Lastly, the recommendation list was output after calculating the similarity of candidate news and fusion model. The experimental results show that, compared with the content-based recommendation methods, the proposed method obviously increases F-measure and Diversity; and it has equivalent performance with hybrid recommendation method, however it does not need time to accumulate enough user clicks of candidate news and has no cold start problem.

To solve the low extraction efficiency for extracting hot topics in huge amounts of micro-blog data, a new topics detection method based on user role orientation was proposed. Firstly, some noise data of parts of users were filtered out by user role orientation. Secondly, the feature weight was calculated by the Term Frequency-Inverse Document Frequency (TF-IDF) function combined with semantic similarity to reduce the error caused by semantic expression. Then, the improved Single-Pass clustering algorithm was used to extract the topics of micro-blog. Lastly, the heat evaluation of micro-blog topics was made according to the number of reposts and comments, thus the hot topics were found. The results show that the average missing rate and false detection rate respectively decrease by 12.09% and 2.37%, and further indicate the topic detection accuracy rate is effectively improved and the method is feasible.

In order to deal with the poor or invalid tracking performance caused by the color sensitivity of Continuously adaptive Mean Shift (CamShift) algorithm, a new CamShift tracking algorithm based on local feature matching was proposed. The new algorithm used the method of Speeded-Up Robust Feature (SURF) to extract the local feature points containing the image information from the target and searched areas of multi-channel images, and then matched the feature points by the method of approximate nearest neighbor searching. The location, scale and orientation information of the feature points were obtained utilizing the purified matching results, therefore the CamShift method was constrained and updated to improve the accuracy and stability of tracking. The experimental results show that the new algorithm can outperform the classic CamShift algorithm and the similar improved algorithms for rotating and zooming objects against complex backgrounds in real-time tracking.

This paper discussed the connotation and extension of two hot concepts, namely, "The Internet of Things (IoT)" and "vehicular CPS (Cyber Physical System)". By analyzing their courses of development, history and interrelationship, and comparing their applications in daily life, the authors are in favor that vehicular CPS can be regarded as an application of IoT in vehicular networks. Furthermore, the authors look into the bright future of services and applications resulting from vehicular CPS. With this in mind, the authors discussed the key technologies of implementing vehicular CPS and introduced an initiative. The authors also discussed in detail the academic research at home and abroad in the area of the Internet of Things and vehicular CPS.

To solve the security problem of using Virtual Private Network (VPN) technology to connect mobile networks and fixed-IP networks, this paper presented and achieved a new type of mobile security system based on IPSec VPN. This system, on the basis of IPSec VPN and remote access service, establishes a complete authentication mechanism based on smart card and X.509 certification, and has a unified security information service platform. At last, the results of the experiment show that the system can spread the information of the fixed-IP networks safely and seamlessly in mobile networks.

Serving as the bridge that links detection and containment, automatic signature extraction has played an important role in anti-worm. Traditional Internet worm signature extraction algorithms were introduced. Based on the  analysis of their mechanisms and major defections, an extraction algorithm  based on common feature set was presented. It supported low complexity extraction and optimization, as well as the tradeoff between sensitivity and specialization, and had remarkable superiority in dealing with background noise and cross infection.