Traditional user intention recognition methods in chatting robot are usually based on template matching or artificial feature sets. To address the problem that those methods are difficult, time-consuming but have a week extension, an intention recognition model based on Biterm Topic Model (BTM) and Bidirectional Gated Recurrent Unit (BiGRU) was proposed with considering the features of the chatting texts about health. The identification of user's medical intention was regarded as a classification problem and topic features were used in the hybrid model. Firstly, the topic of user's every chatting sentence was mined by BTM with quantification. Then last step's results were fed into BiGRU to do context-based learning for getting the final representation of user's continuous statements. At last, the task was finished by making classification. In the comparison experiments on crawling corpus, the BTM-BiGRU model obviously outperforms to other traditional methods such as Support Vector Machine (SVM), even the F value approximately increses by 1.5 percentage points compared to the state-of-the-art model combining Convolution Neural Network and Long-Short Term Memory Network (CNN-LSTM). Experimental results show that the proposed method can effectively improve the accuracy of the intention recognition focusing on characteristics of the study.

Tumor in brain Magnetic Resonance Imaging (MRI) images is often difficult to be segmented accurately due to noise, gray inhomogeneity, complex structrue, fuzzy and discontinuous boundaries. For the purpose of getting precise segmentation with less position bias, a new method based on Fuzzy C-Means (FCM) clustering and morphological multi-scale modification was proposed. Firstly, a control parameter was introduced to distinguish noise points, edge points and regional interior points in neighborhood, and the function relationship between pixels and the sizes of structure elements was established by combining with spatial information. Then, different pixels were modified with different-sized structure elements using morphological closing operation. Thus most local minimums caused by irregular details and noises were removed, while region contours positions corresponding to the target area were largely unchanged. Finally, FCM clustering algorithm was employed to implement segmentation on the basis of multi-scale modified image, which avoids the local optimization, misclassification and region contours position bias, while remaining accurate positioning of contour area. Compared with the standard FCM, Kernel FCM (KFCM), Genetic FCM (GFCM), Fuzzy Local Information C-Means (FLICM) and expert hand sketch, the experimental results show that the suggested method can achieve more accurate segmentation result, owing to its lower over-segmentation and under-segmentation, as well as higher similarity index compared with the standard segmentation.

Concerning the shortcomings in improving the sensing ability and reducing the amount of data transmission of the conventional dual-threshold cooperative spectrum sensing under the communication environment of uncertain noise, an improved dual-threshold cooperative spectrum sensing algorithm based on two-step fusion was introduced in Fusion Center (FC). Firstly, this algorithm got rid of the negative influences of some drop-out users by filtering all cognitive users. Then set the dual-threshold adaptively according to the uncertainty of the noise to strengthen the sensing adaptability of system under uncertain noise circumstance. Finally, by adopting a strategy of two-step fusion in FC, this algorithm made a compromise between the high detection ability and low amount of the data transmission. Compared with the conventional dual-threshold spectrum sensing algorithm, the theoretical analysis and simulation indicate that the proposed algorithm can not only avoid the cognitive failure and enhance the cognitive performance on the condition of a low data transmission, but also show an obvious improvement under a high noise uncertainty.

Concerning the optimization of the overall complexity problem on the high-performance computing platform, a new algorithm named Group Mosquito Host-Seeking Algorithm (GMHSA) was proposed. GMHSA was an intelligent optimization algorithm inspired by mosquitoes sucking blood behavior. It involved max-min fairness and group interaction behavior. The producer group was chosen according to the concept of leader decision and the leadership functions were constructed to make each group maintain their own superiority as well as getting rid of local optimal solution. The algorithm was tested by Traveling Salesman Problem (TSP) and compared with other swarm intelligent algorithms. In the parallel experiment of 16 nodes, the speedup of GMHSA was 15.8, which was nearly linear speedup. Moreover, it could be directly used to solve transport problems and other practical optimal problems. The results indicate that GMHSA has highly parallelism and scalability, and it is an effective measurement for solving complex optimal problems involving behavior.

To improve malodor management and emergency response ability, this paper proposed a regional malodor on-line monitoring platform solution. Referring to network-load equilibrium and dynamic extendibility, the platform implemented real-time monitoring and remote monitoring. The remote monitoring module corresponding to the Remote Desktop Protocol (RDP) could adjust a terminal's parameters, alarm beyond limit and sample at different grades. Based on Advanced Encryption Standard (AES) and MD5 digital-signature technology, a combined algorithm was designed to improve the safety of the RDP. The platform has been piloting in Dagang Petrochemical Industrial Park in Tianjin Binhai New Area，which could accumulate data and experience for the research of future malodor diffusion model as well as malodor pollution control, and make technical preparation for malodor on-line monitoring system to merge into the Internet of Things (IOT) for environmental protection.