To deal with the problem of instance selection for big data, an instance selection algorithm based on Random Forest (RF) and voting mechanism was proposed for big data. Firstly, a dataset of big data was divided into two subsets:the first subset is large and the second subset is small or medium. Then, the first large subset was divided into q smaller subsets, and these subsets were deployed to q cloud computing nodes, and the second small or medium subset was broadcast to q cloud computing nodes. Next, the local data subsets at different nodes were used to train the random forest, and the random forest was used to select instances from the second small or medium subset. The selected instances at different nodes were merged to obtain the subset of selected instances of this time. The above process was repeated p times, and p subsets of selected instances were obtained. Finally, these p subsets were used for voting to obtain the final selected instance set. The proposed algorithm was implemented on two big data platforms Hadoop and Spark, and the implementation mechanisms of these two big data platforms were compared. In addition, the comparison between the proposed algorithm with the Condensed Nearest Neighbor (CNN) algorithm and the Reduced Nearest Neighbor (RNN) algorithm was performed on 6 large datasets. Experimental results show that compared with these two algorithms, the proposed algorithm has higher test accuracy and smaller time consumption when the dataset is larger. It is proved that the proposed algorithm has good generalization ability and high operational efficiency in big data processing, and can effectively solve the problem of big data instance selection.

Concerning the problem that the accuracy and real-time effects of virtual-real registration in Augmented Reality (AR) based on vision are greatly affected by the changes of illumination, occlusion and perspective, which is easy to lead to failure of registration, a virtual-real registration method of natural features based on Binary Robust Invariant Scalable Keypoints-Speeded Up Robust Features (BRISK-SURF) algorithm was proposed. Firstly, Speeded Up Robust Features (SURF) feature extractor was used to detect the feature points. Then, Binary Robust Invariant Scalable Keypoints (BRISK) descriptor was used to describe the feature points in binary, and the feature points were matched accurately and efficiently by combining Hamming distance. Finally, the virtual-real registration was realized according to the homography relationship between images. Experiments were performed from the aspects of image feature matching and virtual-real registration. Results show that the average precision of BRISK-SURF algorithm is basically the same with that of SURF algorithm, is about 25% higher than that of BRISK algorithm, and the average recall of BRISK-SURF is increased by about 10% compared to that of BRISK algorithm; the result of the virtual-real registration method based on BRISK-SURF is close to the reference standard data with high precision and good real-time performance. The Experimental results illustrate that the proposed method has high recognition accuracy, registration precision and real-time effects for images with different illuminations, occlusions and perspectives. Besides, the interactive tourist resource presentation and experience system based on AR is realized by using the proposed method.

Concerning low representation ability and long learning time for complex and variable environmental factors in greenhouses, a prediction method based on improved Deep Belief Network (DBN) combined with Empirical Mode Decomposition (EMD) and Gated Recurrent Unit (GRU) was proposed. Firstly, the temperature environment factor was decomposed by EMD, and then the decomposed intrinsic mode function and residual signal were predicted at different degrees. Secondly, glia was introduced to improve DBN, and the decomposition signal was used to multi-attribute feature extraction combined with illumination and carbon dioxide. Finally, the signal components predicted by GRU were added together to obtain the final prediction result. The simulation results show that compared with empirical decomposition belief network (EMD-DBN) and glial DBN-glial chains (DBN-g), the prediction error of the proposed method is reduced by 6.25% and 5.36% respectively, thus verifying its effectiveness and feasibility of predictions in greenhouse time series environment with strong noise and coupling.

The tool calibration means calculating the transformation matrix of the tool coordinate system relative to the end of the robot coordinate system. Traditional solution realizes point constraint by manual teaching. A calibration method based on visual camera space positioning was proposed. It used the camera to build the relation between the 3D space of the robot and the 2D space of camera to achieve the point constraints of the center point of the rings marks which were used as feature points and stuck on the edge factor. Visual positioning did not need camera calibration and other tedious process. The Tool Center Point (TCP) was figured out based on the forward kinematics derivation process of the robot and camera space point constraint. The calibration error of repeated experiments was less than 0.05 mm, and the absolute positioning error was less than 0.1 mm. The experimental results verify that the tool calibration based on camera space positioning has high repeatability and reliability.

For the entity recognition of commodity attributes in clothing commodity title, a hybrid method combining Conditional Random Field (CRF) with entity boundary detecting rules was proposed. Firstly, the hidden entity hint character messages were obtained through a statistical method; secondly, statistical word indicators and their implications were interpreted with a granularity of character; thirdly, entity boundary detecting rules was proposed based on the entity hint characters and statistical word indicators; finally, a method for identifying threshold values in rules was proposed based on empirical risk minimization. In the comparison experiments with character-based CRF models, the overall precision, recall and F1 score were increased by 1.61%, 2.54% and 2.08% respectively, which validated the efficiency of the entity boundary detecting rule. The proposed method can be used in e-commerce Information Retrieval (IR), e-commerce Information Extraction (IE) and query intention identification, etc.

Electrocardiogram (ECG) signal has attracted widespread interest for the potential use in biometrics due to its ease-of-monitoring and individual uniqueness. To address the accuracy and real-time performance problem of human identification, a fast and robust ECG-based identification algorithm was proposed in this study, which was particularly suitable for miniaturized embedded platforms. Firstly, a dynamic-threshold method was used to extract stable ECG waveforms as template samples and test samples; then, based on a modified Dynamic Time Warping (DTW) method, the degree of difference between matching samples was calculated to reach a result of recognition. Considering that ECG is a kind of time-varying and non-stationary signals, ECG template database should be dynamically updated to ensure the consistency of the template and body status and further improve recognition accuracy and robustness. The analysis results with MIT-BIH Arrhythmia database and own experimental data show that the proposed algorithm has an accuracy rate at 98.6%. On the other hand, the average running times of dynamic threshold setting and optimized DTW algorithms on Android mobile terminals are about 59.5 ms and 26.0 ms respectively, which demonstrates a significantly improved real-time performance.

In the service computing mode, affection behaviour was employed to improve the efficiency of service composition. Firstly, an affection space was built to meet behaviour demands, and cognition was defined to reason state change of affection. In the change processing, mapping was done between affection and cognition, and emotion decay and emotion update were defined to maintain the stability of affective change. Secondly, affective mechanism was put into ant colony algorithm, which formed an affection ant colony algorithm, and the algorithm was applied to Web Service Modeling Ontology (WSMO) service composition. Finally, the paper adopted a Virtual Travel Agency (VTA) example under WSMO to show this approach was effective and feasible.

There are some SaaS problems for Web service and REST (Representational State Transfer) interfaces recognition in the software generation process. Therefore, an approach was proposed based on partitioning algorithm, which employed partitioning algorithm to implement function partition of SaaS and define difference nodes for difference functions. At the same time, the similarity between nodes was defined to accomplish partition, which improved the efficiency of SaaS functions. Secondly, according to the changing requirements, addressing and interrupt approach was presented to realize software generation of SaaS. Finally, an SaaS online sale software in Amazon cloud computing was analyzed, which approves that the approach is feasible and available.