For wind turbine fault warning, original Dynamic Time Warping (DTW) algorithm cannot measure the distance effectively between two multivariate time series data of wind turbines. Aiming at this problem, a DTW algorithm based on Hesitation Fuzzy Set (HFS-DTW) was proposed. The algorithm is an extended algorithm of the original DTW algorithm, which can measure the distance of both univariate and multivariate time series data, and has higher accuracy and speed compared to the original DTW algorithm. With the sub-sequence similarity distance applied as cost function, the length of sub-sequence and step parameters in HFS-DTW algorithm were optimized by using Imperialist Competitive Algorithm (ICA). The study shows that compared to the only DTW algorithm and the HFS-DTW algorithm with non-optimal parameter, the HFS-DTW with optimal parameter can mine more information on multi-dimensional feature point, and the output multi-dimensional feature point similar sequence has more details. And based on the proposed algorithm, the wind turbine gearbox fault can be warned 10 days in advance.

As a new research task, suggestion mining has important application value. Since traditional suggestion sentence classification methods have problems like complex rules, large labeling workload, high feature dimension and data sparsity, a PU (Positive and Unlabeled)-based suggestion sentence classification method was proposed. Firstly, some suggestion sentences were selected from an unlabeled review set by using a simple rule to form a positive example set; then a reliable negative example set was constructed by Spy technique in the feature space of autoencoder neural network to reduce the feature dimension and alleviate data sparsity; finally, Multi-Layer Perceptron (MLP) was trained by the positive example set and the reliable negative example set to classify the remaining unlabeled samples. On a Chinese dataset, the F1 value and the accuracy of the proposed method, reached 81.98% and 82.67% respectively. The experimental results show that the proposed method can classify suggestion sentences effectively without manually labelling the data.

In the traditional Convolutional Neural Network (CNN), the information cannot be transmitted to each other between the neurons of the same layer, the feature information at the same layer cannot be fully utilized, making the lack of the representation of the characteristics of the sentence system. As the result, the feature learning ability of model is limited and the text classification effect is influenced. Aiming at the problem, a model based on joint network CNN-BiGRU and attention mechanism was proposed. In the model, the CNN-BiGRU joint network was used for feature learning. Firstly, deep-level phrase features were extracted by CNN. Then, the Bidirectional Gated Recurrent Unit (BiGRU) was used for the serialized information learning to obtain the characteristics of the sentence system and strengthen the association of CNN pooling layer features. Finally, the effective feature filtering was completed by adding attention mechanism to the hidden state weighted calculation. Comparative experiments show that the method achieves 91.93% F1 value and effectively improves the accuracy of text classification with small time cost and good application ability.

Almost all of existing works on structure-preserving texture smoothing utilize the statistical features of pixels within local rectangular patches to distinguish structures from textures.However, the patch sizes of the rectangular regions are single-scale, which may lead to texture over-smoothed or non-smoothed for images with sharp structures or structures at different scales. Thus, an adaptive scale bilateral texture filtering method was proposed. Firstly, the patch size of rectangular region for each pixel was chosen adaptively from some given candidate sizes based on statistical analysis of local patches, where larger patch sizes were chosen for the homogeneous texture regions and smaller ones for regions near the structure edges. Secondly, guided image were computed via the adaptive patch sizes. Finally, the guided bilateral filtering was operated on the original image. The experimental results demonstrate that the proposed method can better preserve image structures and smooth textures.

For the problems of the traditional method of data record-replay, such as packet format association, close corporation with the controlled application, and low transmission efficiency, a new record-replay method based on data interception and cheating injection was proposed. Firstly, the network data packet was automatically intercepted through the service provider interface technique of Winsock 2. Secondly, the problem of the data sharing and high speed data access was solved by using the memory-mapped file technique. Finally, the saved data packet was intercepted into the user program by the data read operation motivated by the fake messages. The practical application shows that the new method is suitable for the distributed simulation and simulated training system for its merits such as the avoidance of network packet transmission, no necessity for corporation with the controlled application, irrelevance of the packet format, smooth recurrence with ten times the speed.

The conflicts between the real-time, efficient intelligent analysis and the inefficient, laborious trouble shooting, which are faced by most of video surveillance systems, can be resolved by Intelligent Video Quality Detection System (IVQDS). As a part of IVQDS, video jitter detection algorithm was focused in this paper. In the proposed method, sparse optical flow features were fused together with interest point matching algorithm; correctly matched point-set which was reliably detected according to the forward-backward error criterion, was used to estimate the global motion parameters, from which motion entropy was computed to measure the motion homogeneity of the video fragment. The experimental results tested on realistic surveillance video records have shown that the proposed algorithm can work under real-time environment against the effects from big movements with high detection performance.

For the issue of how to quickly estimate the accurate, reliable due date according to the order information and the features of the production system in Assembly To Order (ATO), a due date prediction model was constructed based on the influential mechanism analysis of the uncertainty factors. The model parameters included three parts: order release time, assembly cycle time and abnormal tardiness. Order release time was based on the availability of materials and production capacity. The assembly cycle time and abnormal tardiness were predicted by using Support Vector Regression (SVR) method based on actual production history data. The case study shows that the predicted results of the model are close to actual due date and it can be used to guide the order's delivery time consultation.

It is difficult for the object tracking algorithm based on single feature, to track the object in complex cases. Therefore, this paper proposed an algorithm fusing multiple features for object tracking based on adaptive background information. The algorithm was based on the use of color feature and gray level co-occurrence matrix texture feature to represent the object. Under the frame of particle filter, it analyzed the particle space distribution, particle value distribution and ability to distinguish the background information with different feature. Then it presented an efficient fusion coefficient calculation. According to the object's appearance of changes in the process of tracking, it updated the object template adaptively. The experimental results in different settings show that this algorithm greatly improves the resistance to background interference, under the premise of not reducing the real-time. In all sorts of situations, it has good stability and robustness.

The two-path algorithm in the acoustic echo cancellation is widely applied to avoid the false adaptation problem during the double-talk situation. This paper proposed an improved transfer logic for the two-path algorithm; based on the comparison of the Echo Return Loss Enhancement (ERLE) for the filters, the improved transfer logic decided whether to permit filter update. Furthermore, the improved transfer logic managed to detect the double-talk and avoid the false filter update, improved the convergence speed and allowed the reduction of the memory requirement and computational complexity. The simulation results show the improved performance of the proposed solution.

The method of automatic abstracting based on text clustering was brought forward to overcome the shortages of the current methods of automatic abstracting. This method used text clustering, which realized automatic abstracting of multi-document. For a specific plastic domain an automatic abstracting system named TCAAS based on text clustering was implemented, whose precision and recall was above 80%. And the precision and recall of automatic abstracting of multi-document was above 75%. Experiments proved that it is feasible to use the method to develop an automatic abstracting system, which is valuable for further study in more depth.

A method of setting up a experimental platform of loop coordinated manipulation and motion control for double-arms free flying space robots was presented. The FFSR(Free-Flying Space Robots) experimental platform frame in robotic simulation was created by the combination of the (VC++) 6.0,OpenGL,Matlab and Matcom. With the implementation of a resolved motion rate control algorithm, the dynamic process of the robots manipulating the target object coordinately was depicted, and the curves of the position and the angle of the spacecraft were shown, too. The results above verify the effectiveness of the algorithm and the methodology.