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There are still relatively few research studies on topic detection and tracking of microblogs. Traditional methods applied to microblogs cost too much calculation and the rate of accuracy is low. Therefore, combined with characteristics of large data of documents, short single documents, few hot topics and colloquial contents, the article studies and fulfills methods of topic detection and tracking of microblogs.

The processing and application of public opinion are getting more and more attention all over the world with increasing technologies and systems (software) one after another, however, rare of them focus on the technology of reverse relation. In this paper, it takes the classic association algorithms Apriori and FP-Tree as its basis, presents a process design of reverse relation on public opinion, and realizes the acquisition and query of hot public opinions in social networks.

Due to the expansion of data scale, the data in Transportation Management System (TMS) of the State Grid has the features of distributed storage, more data noise and larger data dimension, making data analysis extremely difficult. Therefore, data sorting and feature selection should be carried out on the basis of the original data for subsequent learning tasks. The optimization method of feature selection for TMS classification learning is able to effectively select a large number of features, thus improving the accuracy of classification learning. In order to train a feature selection model suitable for TMS, this paper studies the method of applying random forest model integrated by multiple decision trees to TMS feature selection. By comparing the number of decision trees, the criteria of feature classification, the maximum number of features in candidate subset of feature classification, and the change of model accuracy after feature rearrangement with key algorithms, a feature selection method most suitable for TMS data is given and verified by experiments. Experimental results show that the proposed optimization method of feature selection lays a good foundation for future learning tasks.

The effectiveness evaluation of ship-borne communication equipment (SCE for short) is studied. The existing methods of equipment effectiveness evaluation are summarized. In view of the fuzziness, uncertainty and difficulty in quantifying the management factors of SCE by probability and statistics method, the overall effectiveness evaluation of SCE is carried out by using fuzzy comprehensive evaluation method. Considering the use characteristics of SCE, Availability Dependability and Capability (ADC for short) method is used to evaluate the service capability index, and the results are integrated into the overall evaluation of the fuzzy comprehensive evaluation method. Two case calculations and simulation results are given, and the corresponding decision-making reference is given according to the results.

Semantic knowledge-base has important meaning for increasing the deepness of NLP. Some comparatively mature Semantic knowledge-base such as WordNet, HowNet and Thesaurus was developed by manpower, and has many difficulties on actual application. In order to capture Chinese word knowledge of relating status moue automatically and demonstrably, this paper presented the concept of word correlation and a calculation method of word correlation based on statistic. Then a correlation net based on Chinese words which have strong domain characteristic was built. In order to resolve the difficulty of processing the huge amount of data, a hard disk storing method of array segmentation was designed. The semantic knowledge gained by the experiment had the advantage of empiricism. It is veracity and generalization is strong so it can play an important role in many fields such as text categorization, text retrieval, text filtering, etc.

To find malicious information jamming countermeasures and techniques of content attack, the paper proposed two malicious jamming algorithms, including the Blind Jamming Algorithm and the Precise Jamming Algorithm. Experiments, conducted on 3,183 texts with the Chinese Lexical Analysis System (ICTCLAS) shows the effectiveness of Blind Jamming Algorithm, making the accuracy rate of Chinese word segmentation from 94% dropped to 43%. Besides, topic classification experiments show that the Blind Jamming Algorithm could affect the topic classification significant. Further experiments on 5,999 texts with the semantic orientation analyzer (Sentifier) shows that the Sentifiers average precision is declined from 77% to 60% by jamming, which shows the jamming effectiveness of the two algorithms we have proposed.

Cognitive attacks, which influence the user’s perceptions and behavior, are more serious than physical or even syntactic attacks. We construct an attack model for cognitive Network based on scientific analysis of Network material system to visualize cognitive attack. The cognitive attack model consists of two parts: attackers and netizens. We formalize each element of the model. The cognitive attack example analysis illustrates the rationality of the model.

The present Extraction for Keyword in Chinese (EKC for short) is merely directed against explicit keywords or prototype keywords, and has not take into account those tacit keywords distorted by network hackers with the method of active jamming in Chinese. For this purpose, with the help of M. Polanyi’s theory of tacit knowledge, this paper presents a new approach for the tacit EKC (TEKC for short), which can improve the ratio of precision and recall for information filtering. Based on the TEKC, the paper presents a set of classifications of how to distort the explicit keywords and the solutions to calculate the tacit distortion of those tacit keywords. Furthermore, 4 algorithms were designed, including in picture tacit, textspeak tacit, fake paleography tacit and character tacit, which can extract the tacit keywords in text but traditional EKC could not. Owing to the increased number of extracted keywords, the recall of keywords raised and the precision of information filtering improved. Experiments show that the classification of tacit keywords in Chinese, the calculation of tacit distortion and the algorithms to tacit extract the keywords in Chinese, etc can effectively improve the performance of EKC and raise the recall of web filtering algorithms based on TEKC.

this paper deeply studies the fault models of digital circuits and proposes a combined implementation of BIST test mode, on the basis of which a new implementation of PODEM is proposed. In addition, this paper also studies the generation of test vector algorithm and a fault diagnosis system for special purpose equipment is designed and implemented based on the research in this paper, which wins the 2 prize in a province level. Keywords—PODEM, fast fault diagnosis, BIST, SCOAP, dirigibility, observability.

Biologically-inspired methods such as evolutionary algorithms and neural networks are well suited to dynamic problems. Artificial immune systems are proving useful in the field of information filtering. We tackle this dynamic problem with IHIF, a harmful information filtering model inspired by the immune system. It is based on a self-organising antibody network that reacts to dynamic evolvement in order to define and preserve the features of harmful Web pages. The experiment results demonstrate IHIF’s ability to filter harmful information.

The main objective of this paper is to analyze models, techniques, algorithms and infrastructures needed to complete decentralized data fusion. It is often to use the Simple Tracking Fusion Algorithm in decentralized data fusion for Multi-sensor. But that algorithm is on the hypothesis that the output of each local filter is uncorrelated. If the fusion result is given back to each local filter, the output of the local filter will has correlation with each other. For that case, if still using the Simple Tracking Fusion Algorithm, the fusion data will lose consistency. But in the Covariance Intersection Algorithm, it is unnecessary to consider the correlation of each local filter. In this paper, a new decentralized data fusion algorithm with feedback framework based on the Covariance Intersection algorithm is proposed for Multi-sensor. The simulation results show the effectiveness and robustness of the proposed algorithm.