LoRaWAN， as a wireless communication standard in Low Power Wide Area Network （LPWAN）， provides the support for the development of IoT （Internet of Things）. However， limited by the characteristics of incomplete orthogonality among Spreading Factor （SF） and the fact that LoRaWAN does not have a Listen-Before-Transmit （LBT） mechanism， the ALOHA-based transmission scheduling method will trigger serious channel conflicts， which reduces the scalability of LoRa （Long Range Radio） networks greatly. Therefore， in order to improve the scalability of LoRa network， Non-Persistent Carrier Sense Multiple Access （NP-CSMA） mechanism was proposed to replace the medium access control mechanism of ALOHA in LoRaWAN. The time of accessing the channel for each node with the same SF in LoRa network was coordinated by LBT， and multiple SF signals were transmitted in parallel for the transmission between different SFs， thus reducing the interference of same SF and avoiding inter-SF interference in the common channel. To analyze the impact of NP-CSMA on the scalability of LoRa networks， LoRa networks constructed by Lo RaWAN and NP-CSMA were compared by theoretical analysis and NS3 simulation. Experimental results show that NP-CSMA has 58.09% higher theoretical Packet Delivery Rate （PDR） performance than LoRaWAN under the same conditions， at a network communication load rate of 1. In terms of channel utilization， NP-CSMA increases the saturated channel utilization by 214.9% and accommodates 60.0% more nodes compared to LoRaWAN. In addition， the average latency of NP-CSMA is also shorter than that of the confirmed LoRaWAN at a network traffic load rate of less than 1.7， and the additional energy consumption to maintain the CAD （Channel Activity Detection） mode is 1.0 mJ to 1.3 mJ and 2.5 mJ to 5.1 mJ lower than the additional energy consumption required by LoRaWAN to receive confirmation messages from the gateway when spreading factor is 7 and 10. The above fully reflects that NP-CSMA can improve LoRa network scalability effectively.

Aiming at the problem that it is hard to process the temporal semantic information during formulating and providing intelligent services, a temporal semantic understanding model for intelligent service systems was proposed. For service message texts in natural language, temporal information extraction, mapping, and semantic modeling were implemented, so as to provide a universal temporal semantic expression pattern for intelligent service systems. Firstly, a heuristic strategy was adopted to automatically extract temporal phrases and construct time information knowledge base without any manual intervention. Then, a temporal information mapping method based on temporal unit was proposed, to complete quantitative expression of absolute time and logical reasoning of relative time. Finally, a temporal semantic model was constructed by comprehensively using temporal information and contextual information. In service message test set, experimental results show that the precision of time information extraction is as high as 97.58% and the mapping precision is greater than 85%. And the satisfying effect of semantic modeling is shown.

Aiming at dialog generation problem, a dialog generation model based on hierarchical encoding and deep reinforcement learning, namely Enhanced Hierarchical Recurrent Encoder-Decoder (EHRED) was proposed to solve the problem that standard sequence to sequence (seq2seq) architectures are more likely to raise highly generic responses due to the Maximum Likelihood Estimate (MLE) loss function. A multi-round dialog model was built by hierarchical structure, and a hierarchical layer was added to enhance the memory of history dialog based on the standard seq2seq architecture, and then a language model was used to build reward function, replacing traditional MLE loss function with policy gradient method in deep reinforcement learning for training. Experimental results show that EHRED can generate responses with richer semantic information and improve by 5.7-11.1 percentage points in standard manual evaluation compared with the widely used traditional standard seq2seq Recurrent Neural Network (RNN) dialog generation model.

To solve the problem that the methods currently used to construct Chinese knowledge graph system are time-consuming, have low accuracy and require a lot of manual intervention, an integrated end-to-end automatically constructed solution based on rich data from Chinese encyclopedia was proposed, and a user-oriented Chinese knowledge graph was implemented. In this solution, some property and related text information of the original encyclopedia data were scraped to local system uninterruptedly by the custom Web crawler, and saved as a triple with extended attributes. Through graph-oriented database Cayley and document-oriented database MongoDB, the data in the archived triple files was imported in the back-end system, and then converted to a huge knowledge graph system in order to provide various services dependent on the Chinese knowledge graph in the front-end system. Compared with other knowledge graph systems, the proposed system significantly reduces the construction time; moreover, the number of entities and relations is at least 50% higher than that of the other knowledge graph systems such as YAGO, HowNet and the Chinese Concept Dictionary.

In the era of big data, research in topic evolution is mostly based on the classical probability topic model, the premise of word bag hypothesis leads to the lack of semantic in topic and the retrospective process in analyzing evolution. An online incremental feature ontology based topic evolution algorithm was proposed to tackle these problems. First of all, feature ontology was built based on word co-occurrence and general WordNet ontology base, with which the topic in text stream was modeled. Secondly, a text stream topic matrix construction algorithm was put forward to realize online incremental topic evolution analysis. Finally, a text topic ontology evolution diagram construction algorithm was put forward based on the text steam topic matrix, and topic similarity was computed using sub-graph similarity calculation, thus the evolution of topics in text stream was obtained with time scale. Experiments on scientific literature showed that the proposed algorithm reduced time complexity to O(nK+N), which outperformed classical probability topic evolution model, and performed no worse than sliding-window based Latent Dirichlet Allocation (LDA). With ontology introduced, as well as the semantic relations, the proposed algorithm can demonstrate the semantic feature of topics in graphics, based on which the topic evolution diagram is built incrementally, thus has more advantages in semantic explanatory and topic visualization.

To solve the problem that classical Mutual Information (MI) image registration may lead to local extremum, a registration method for multispectral magnetic resonance images based on Cross Cumulative Residual Entropy (CCRE) was proposed. Firstly, the gray level of reference and floating images were compressed into 5 and 7 bits. Then the Hanning windowed Sinc interpolation was used to calculate the CCRE of 5-bit grayscale images, and the Brent algorithm was used to search the CCRE to get the initial transformation parameters of pre-registration. Finally, the Partial Volume (PV) interpolation was adopted to calculate the CCRE of 7-bit grayscale images, and the Powell algorithm was applied to optimize the CCRE to get final parameters from the pre-registration parameters. The experimental results show that the robustness of the proposed method is improved compared with the CCRE registration of PV interpolation, while the registration time is saved about 90% and accuracy is improved compared with the CCRE of Hanning windowed Sinc interpolation. The presented method ensures robustness, efficiency and accuracy, so it is suitable for multi-spectral image registration.

To solve the challenge of accurate user group discovering, a user topic discovering algorithm based on trust chain, which was composed by three steps, i.e., topic space discovering, group core user discovering and topic group discovering, was proposed. Firstly, the related definitions of the proposed algorithm were given formally. Secondly, the topic space was discovered through the topic-correlation calculation method and a user interest calculation method for topic space was addressed. Further, the trust chain model, which was composed by atomic, serial, and parallel trust chains, and its trust computation method of topic space were presented. Finally, the detail algorithms of topic group discovering, including topic space discovering algorithm, core user discovering algorithm and topic group discovering algorithm, were proposed. The experimental results show that the average accuracy of the proposed algorithm is 4.1% and 11.3% higher than that of the traditional interest-based and edge density-based group discovering methods. The presented algorithm can improve the accuracy of user group organizing effectively, and it will have good application value for user identifying and classifying in social network.

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.

To solve the problem of Fine Particulate Matter (PM2.5) concentration prediction, a PM2.5 concentration prediction model was proposed. First, through introducing the comprehensive meteorological index, the factors of wind, humidity, temperature were comprehensively considered; then the feature vector was conducted by combining the actual concentration of SO2, NO2, CO and PM10; finally the Least Squares Support Vector Machine (LS-SVM) prediction model was built based on feature vector and PM2.5 concentration data. The experimental results using the data from the city A and city B environmental monitoring centers in 2013 show that, the forecast accuracy is improved after the introduction of a comprehensive weather index, error is reduced by nearly 30%. The proposed model can more accurately predict the PM2.5 concentration and it has a high generalization ability. Furthermore, the author analyzed the relationship between PM2.5 concentration and the rate of hospitalization, hospital outpatient service amount, and found a high correlation between them.

In order to solve the problem of being sensitive to external interference such as defects and occlusions in the existing discriminant analysis, a Sparsity reconstruction-based Discriminant Analysis (SDA) for dimensionality reduction was proposed in the term of local sparse representation. The algorithm firstly made use of sparse representation to complete local sparsity reconstruction in each class, and then completed between-class sparsity reconstruction with the average of each different class. Finally the algorithm preserved the ratio between the between-class sparsity reconstruction information and the within-class sparsity reconstruction information in the process of dimensionality reduction. The algorithm promotes the computational efficiency of sparse representation and the robust performance of discriminant analysis. The experimental results on AR and UMIST face datasets show, compared with Graph-based Fisher Analysis (GbFA) algorithm and Reconstructive-based Discriminant Analysis (RDA) algorithm, the proposed algorithm promotes 2-10 percent in the highest recognition accuracy based on nearest neighbor classification.

At present, the safety analysis of SNAKE algorithm is mainly about interpolation attack and impossible differential attack. The paper evaluated the security of SNAKE(2) block cipher against integral attack. Based on the idea of higher-order integral attack, an 8-round distinguisher was designed. Using the distinguisher, integral attacks were made on 9/10 round SNAKE(2) block cipher. The attack results show that the 10-round SNAKE(2) block cipher is not immune to integral attack.

Multiple Sclerosis (MS) is a chronic disease that affects the central nervous system and MS lesions are visible in conventional Magnetic Resonance Imaging (cMRI). A new method for the automatic segmentation of MS White Matter Lesions (WML) on cMRI was presented, which enabled the efficient processing of images. Firstly the Kernel Fuzzy C-Means (KFCM) clustering was applied to the preprocessed T1-weight (T1-w) image for extracting the white matter image. Then region growing algorithm was applied to the white matter image to make a binary mask. This binary mask was then superimposed on the corresponding T2-weight (T2-w) image to yield a masked image only containing white matter, lesions and background. The KFCM was reapplied to the masked image to obtain WML. The testing results show that the proposed method is able to segment WML on simulated images of low noise quickly and effectively. The average Dice similarity coefficient of segmentation result is above 80%.

Concerning the difficulty of data sparsity and new user problems in many collaborative recommendation algorithms, a new collaborative recommendation algorithm based on matrix factorization and user nearest neighbor was proposed. To guarantee the prediction accuracy of the new users, the user nearest neighbor model based on user data and profile information was used. Meanwhile, large data sets and the problem of matrix sparsity would significantly increase the time and space complexity. Therefore, matrix factorization was introduced to alleviate the effect of data problems and improve the prediction accuracy. The experimental results show that the new algorithm can improve the recommendation accuracy effectively, and solve the problems of data sparsity and new user.

The sentiment analysis has aroused the interest of many researchers in recent years，since the subjective texts are useful for many applications. Sentiment analysis is to mine and analyze the subjective text, aiming to acquire valuable knowledge and information. This paper surveyed the status of the art of Chinese sentiment analysis. Firstly, the technique was introduced in detail, according to different granularity levels, namely word, sentence, and document; and the research of product review and news review were presented respectively. Then evaluation and corpus for Chinese text sentiment analysis were introduced. The difficulty and trend of Chinese text sentiment analysis were concluded finally. This paper focuses on the major methods and key technologies in this field, making detailed analysis and comparison.

To solve the problems of huge storage, difficult management, poor display of data and decision support, a Migration Information System (MIS) based on three-dimension GIS named World Wind was designed and developed. Its system architecture and functional modules were designed, MIS was developed based on World Wind Java SDK, Digital Elevation Mode (DEM) and image data were divided, stored, organized and scheduled, based on which integration and application of migration information were accomplished. A case study shows that the system has good extensibility and 3D effect.

Current Pornographic images filtering algorithms have some problems, such as the high false positive rate toward the bikinis images and insufficiency of filtering pornographic images with pornographic actions. The paper proposes a novel pornographic image filtering model based on High-level Semantic Bag-Of-Visual-Words. Firstly, local feature points in sex scene are detected using the SURF algorithm and then high-level semantic dictionary is constructed by fusing the context of the visual vocabularies and spatial-related high-level semantic features of pornographic images. Experimental results show that the model has an accuracy up to 87.6% when testing the multi-person pornographic images, which is significantly higher than the existing pornographic image filtering algorithm based on Bag-Of-Visual-Words.

Aiming at several key issues in the fuzzy comprehensive evaluation, the structure of cooperating fuzzy comprehensive evaluation system(CFCES) was intorduced which used of CSCW technology. Analyzing the merits and demerits of both traditional concentrating and distributing control methods, gave formally a basic model of CFCES based on roles,A basic model of CFCES based on roles was given formally after analyzing the merits and demerits of both traditional concentrating and distributing control methods, and the implementing procedure of role-based cooperating components in CFCES was described.