There would be lack of useful information in added unlabeled samples during the iterations of co-training algorithm, meanwhile, the labels of the samples labeled by multiple classifiers may happen to be inconsistent, which would lead to accumulation of classification errors. To solve the above problems, a co-training algorithm combining improved density peak clustering and shared subspace was proposed. Firstly, the two base classifiers were obtained by the complementation of attribute sets. Secondly, an improved density peak clustering was performed based on the siphon balance rule. And beginning from the cluster centers, the unlabeled samples with high mutual neighbor degrees were selected in a progressive manner, then they were labeled by the two base classifiers. Finally, the final categories of the samples with inconsistent labels were determined by the shared subspace obtained by the multi-view non-negative matrix factorization algorithm. In the proposed algorithm, the unlabeled samples with better representation of spatial structure were selected by the improved density peak clustering and mutual neighbor degree, and the same sample labeled by different labels was revised via shared subspace, solving the low classification accuracy problem caused by sample misclassification. The algorithm was validated by comparisons in multiple experiments on 9 UCI datasets, and experimental results show that the proposed algorithm has the highest classification accuracy rate in 7 data sets, and the second highest classification accuracy rate in the other 2 data sets.

In the case of limited mobile device performance, a Two-channel Dynamic Encryption Strategy (TDES) based on greedy algorithm was proposed to perform selective encryption to the data packet, so as to maximize the total privacy weight of packets in a limited time. First, the data packets were roughly classified into two categories according to the privacy weight of the data packets. Then, the weight ranking table was calculated by the privacy weight and the encryption time of the different data packets and sorted in descending order.The two types of data packets corresponded to two transmission channels, and the packet with the maximum privacy weight was encrypted for transmission until at the end of the transmission time. Finally, the remaining time inside the channel was checked, and the transmission channels of some packets were adjusted until the remaining time was less than the encryption time of any packet. The simulation of packet transmission tests shows that compared with Dynamic Data Encryption Strategy (D2ES) and greedy algorithm under the same time limit, the total privacy weight of the proposed strategy was increased by 9.5% and 10.3%, and the running time of the proposed strategy was reduced by 10.8% and 8.5%. Experimental results verify that the proposed TDES has shorter computation time and higher efficiency, which can well balance data security and equipment performance.

In view of the shortcomings of the current path planning strategies for multiple robots, such as high path coupling, long total path, long waiting time for collision avoidance, and the resulting problems of low system robustness and low robot utilization, a multi-robot path planning algorithm based on 3D spatiotemporal maps and motion decomposition was proposed. Firstly, the dynamic temporary obstacles in time dimension were generated according to the existing path set and the current robots' positions, and were expanded into 3D search space together with the static obstacles. Secondly, in the 3D search space, the total time of path motion was divided into three parameters:motion time, turning time, and in-situ dwell time, and the conditional depth first search strategy was used to calculate the set of all paths from the starting node to the target node that met the parameter requirements. Finally, all paths in the path set were traversed. For each path, the actual total time consumption was calculated. If the difference between the actual total time consumption and the theoretical total time consumption of a path was less than the specified maximum error, the path was considered as the shortest path. Otherwise, the remaining paths were continued to traverse. And if the differences between the actual total time and the theoretical total time of all paths in the set were greater than the maximum error, the parameters needed to be adjusted dynamically, and then the initial steps of algorithm were continued to execute. Experimental results show that, the path planned by the proposed algorithm has the advantages of short total length, less running time, no collision and high robustness, and the proposed algorithm can solve the problem of completing continuous random tasks by multi-robot system.

To evaluate the click efficiency of different graphic designs of Virtual Reality (VR) interactive interface, a prediction method for the completion time of directional tasks in virtual scenarios was proposed based on probabilistic Fitts' law, and an arbitrary shape selection model of VR was constructed. First, according to the actual needs of VR interface design, the influence of shape on the completion time of directional tasks was added, a relation function between hit probability and task difficulty index was constructed, the target centroid was set as the center point of the function to be integrated, and the definition of probabilistic Fitts' model under the virtual scenario was completed. Then, the first experiment was designed to obtain the constant term value of the probability function in the improved probabilistic Fitts' model. On this basis, the second experiment was designed to calculate the constant term of the prediction function in the improved probabilistic Fitts' model, so as to construct the improved probabilistic Fitts' model. Finally, the model was verified and evaluated on the actual click tasks in a VR tobacco sorting system. Experimental results show that the model can predict the task completion time in the virtual scenario.

High ambiguity samples are easy to be mislabeled by the co-training algorithm, which would decrease the classifier accuracy, and the useful information hidden in unlabeled data which were added in each iteration is not enough. To solve these problems, a co-training algorithm combined with active learning and density peak clustering was proposed. Before each iteration, the unlabeled samples with high ambiguity were selected and added to the labeled sample set after active labeling, then density peak clustering was used to cluster the unlabeled samples to obtain the density and relative distance of each unlabeled sample. During iteration, the unlabeled samples with higher density and further relative distance were selected to be trained by Naive Bayes (NB) classification algorithm. The processes were iteratively done until the termination condition was satisfied. Mislabeled data recognition problem could be improved by labeling samples with high ambiguity based on active learning algorithm, and the samples reflecting data space structure well could be selected by density peak clustering algorithm. Experimental results on 8 datasets of UCI and the pima dataset of Kaggle show that compared with SSLNBCA (Semi-Supervised Learning combining NB Co-training with Active learning) algorithm, the accuracy of the proposed algorithm is up to 6.67 percentage points, with an average improvement of 1.46 percentage points.

Spy technology in Positive and Unlabeled (PU) learning is easily susceptible to noise and outliers, which leads to the impurity of reliable positive instances, and the mechanism of selecting spy instances in the initial positive instances randomly tends to cause inefficiency in dividing reliable negative instances. To solve these problems, a PU learning framework combining new spy technology and semi-supervised self-training was proposed. Firstly, the initial labeled instances were clustered and the instances closer to the cluster center were selected to replace the spy instances. These instances were able to map the distribution structure of unlabeled instances effectively, so as to better assist to the selection of reliable negative instances. Then, the reliable positive instances divided by spy technology were purified by self-training, and the reliable negative instances which were divided as positive instances mistakenly were corrected by secondary training. The proposed framework can solve the problem of PU learning that the classification efficiency of traditional spy technology is susceptible to data distribution and random spy instances. The experiments on nine standard data sets show that the average classification accuracy and F-measure of the proposed framework are higher than those of Basic PU-learning algorithm (Basic_PU), PU-learning algorithm based on spy technology (SPY), Self-Training PU learning algorithm based on Naive Bayes (NBST) and Iterative pruning based PU learning (Pruning) algorithm.

According to the problem that unlabeled samples of high confidence selected by self-training method contain less information in each iteration and self-training method is easy to mislabel unlabeled samples, a Naive Bayes self-training method based on semi-supervised clustering and data editing was proposed. Firstly, semi-supervised clustering was used to classify a small number of labeled samples and a large number of unlabeled samples, and the unlabeled samples with high membership were chosen, then they were classified by Naive Bayes. Secondly, the data editing technique was used to filter out unlabeled samples with high clustering membership which were misclassified by Naive Bayes. The data editing technique could filter noise by utilizing information of the labeled samples and unlabeled samples, solving the problem that performance of traditional data editing technique may be decreased due to lack of labeled samples. The effectiveness of the proposed algorithm was verified by comparative experiments on UCI datasets.

The prior work of video analysis technology is video foreground detection in complex scenes. In order to solve the problem of low accuracy in foreground moving target detection, an improved moving object extraction algorithm for video based on Visual Background Extractor (ViBE), called ViBE⁺, was proposed. Firstly, in the model initialization stage, each background pixel was modeled by a collection of its diamond neighborhood to simply the sample information. Secondly, in the moving object extraction stage, the segmentation threshold was adaptively obtained to extract moving object in dynamic scenes. Finally, for the sudden illumination change, a method of background rebuilding and update-parameter adjusting was proposed during the process of background update. The experimental results show that, compared with the Gaussian Mixture Model (GMM) algorithm, Codebook algorithm and original ViBE algorithm, the improved algorithm's similarity metric on moving object extracting results increases by 1.3 times, 1.9 times and 3.8 times respectively in complex video scene LightSwitch. The proposed algorithm has a better adaptability to complex scenes and performance compared to other algorithms.