Aiming at the low classification accuracy of unbalanced datasets, an unbalanced data classification algorithm based on improved SMOTE (Synthetic Minority Oversampling TEchnique) and AdaBoost algorithm (KSMOTE-AdaBoost) was proposed. Firstly, a noise sample identification algorithm was proposed according to the idea of K-Nearest Neighbors (KNN). The noise samples in the sample set were accurately identified and filtered out by the number of heterogeneous samples included in the K neighbors of the sample. Secondly, in the process of oversampling, the sample set was divided into different sub-clusters based on the idea of clustering. According to the cluster center of the sub-cluster and the number of samples the sub-cluster contains, the synthesis of new samples was performed between the samples in the cluster and the cluster center. In the process of sample synthesis, the data imbalance between classes as well as in the class was fully considered, and the samples were corrected in time to ensure the quality of the synthesized samples and balance the sample information. Finally, using the advantage of AdaBoost algorithm, the decision tree was used as the base classifier and the balanced sample set was trained and iterated several times until the termination condition was satisfied, and the final classification model was obtained. The comparative experiments were carried out on 6 KEEL datasets with G-mean and AUC selected as evaluation indicators. The experimental results show that compared with the classical oversampling algorithm SMOTE and ADASYN (ADAptive SYNthetic sampling approach), G-means and AUC have the highest of 3 groups in 4 groups. Compared with the existing unbalanced classification models SMOTE-Boost, CUS (Cluster-based Under-Sampling)-Boost and RUS (Random Under-Sampling)-Boost, among the 6 groups of data:the proposed classification model has higher G-means than CUS-Boost and RUS-Boost, and 3 groups are lower than SMOTE-Boost; AUC is higher than SMOTE-Boost and RUS-Boost, and one group is lower than CUS-Boost. It is verified that the proposed KSMOTE-AdaBoost has better classification effect and the model has higher generalization performance.

There are many redundant points and inflection points in the path planned by the traditional A^* algorithm. Therefore, an efficient path planning algorithm based on A^* algorithm was proposed. Firstly, the specific calculation method of the evaluation function was improved to reduce the calculation amount of the algorithm searching each interval, thereby reducing the path finding time and changing the generation path. Secondly, on the basis of improving the specific calculation method of the evaluation function, the weight ratio of the evaluation function was improved, and the redundant points and inflection points in the generation path were reduced. Finally, the path generation strategy was improved to delete the useless points in the generation path, improving the smoothness of the path. In addition, considering the actual width of the robot, the improved algorithm introduced an obstacle expansion strategy to ensure the feasibility of the planned path. The comparison of the improved A^* algorithm with three algorithms shows that the path of the improved A^* algorithm is more reasonable, the path finding time is shorter, and the smoothness is higher.

In order to effectively solve the overfitting problem in deep Convolutional Neural Network (CNN), a model prediction averaging method based on Dropout improved CNN was proposed. Firstly, Dropout was employed in the pooling layers to sparse the unit values of pooling layers in the training phase. Then, in the testing phase, the probability of selecting unit value according to pooling layer Dropout was multiplied by the probability of each unit value in the pooling area as a double probability. Finally, the proposed double-probability weighted model averaging method was applied to the testing phase, so that the sparse effect of the pooling layer Dropout in the training phase was able to be better reflected on the pooling layer in the testing phase, thus achieving the low testing error as training result. The testing error rates of the proposed method in the given size network on MNIST and CIFAR-10 data sets were 0.31% and 11.23% respectively. The experimental results show that the improved method has lower error rate than Prob. weighted pooling and Stochastic Pooling method with only the impact of pooling layer on the results considered. It can be seen that the pooling layer Dropout makes the model more generalized and the pooling unit value is helpful for model generalization and can effectively avoid overfitting.

To overcome the randomness of RRT-Connect and slow convergence of B-RRT ^*(asymptotically-optimal Bidirectional Rapidly-exploring Random Tree) in path generation, an efficient path planning algorithm based on B-RRT ^*, abbreviated as EB-RRT ^*, was proposed. Firstly, an intelligent sampling function was intriduced to achieve more directional expansion of random tree, which could improve the smoothness of path and reduce the seek time. A rapidly-exploring strategy was also added in EB-RRT ^* by which RRT-Connect exploration mode was adopted to ensure rapidly expanding in the free space and improved asymptotically-optimal Rapidly-exploring Random Tree (RRT ^*) algorithm was adopted to prevent trapped in local optimum in the obstacle space. Finally, EB-RRT ^* algorithm was compared with Rapidly-exploring Random Tree (RRT), RRT-Connect, RRT ^* and B-RRT ^* algorithms. The simulation results show that the improved algorithm is superior to other algorithms in the efficiency and smoothness of path planning. It reduced the path planning time by 68.3% and the number of iterations by 48.6% compared with B-RRT ^* algorithm.

In the process of mobile robot path planning, it is difficult for the Rapidly-exploration Random Tree (RRT) algorithm to sample narrow channels. In order to deal with this problem, an improved bridge detection algorithm was proposed, which is dedicated to narrow channel sampling. Firstly, the environment map was pre-processed and the obstacle edge coordinate set was extracted as the sampling space for the bridge detection algorithm, thus avoiding a large number of invalid sampling points and making the sampling points distribution of the narrow channel more rational. Secondly, the process for bridge endpoint construction was improved, and the operation efficiency of the bridge detection algorithm was increased. Finally, a slight variant Connect algorithm was used to expand the narrow channel sample points rapidly. For the narrow channel environment map in the experiment, the improved algorithm has the success rate increased from 68% to 92% compared with the original RRT-Connect algorithm. Experimental results show that the proposed algorithm can sample the narrow channel well and improve the efficiency of path planning.

In order to meet the performance and throughput requirements of distributed systems, the asynchronous message communication is a common strategy. However, this strategy can not solve the consistency problem of the distributed system. In order to solve this problem, this paper proposed the establishment of consistency guarantee platform. Firstly, the system fulfilled idempotency and strong consistency between business data and message production/consumption records. Secondly, a message monitoring strategy was established. And it could be decided whether a message was correct or the compensation/idempotent operation was needed, according to the monitoring rules and production/consumption records, in order to realize the eventual consistency of the distributed system based on message communication. Lastly, the Separation of Concerns (SoC) and horizontal segmentation methods were adopted in design and realization of this platform. Experiments and analyses have shown the better performance of this distributed message communication, comparing to the asynchronous communication. This platform could timely check and handle the inconsistency and thus achieve the eventual consistency, i.e. the final eventual consistency of the whole system. Also the platform design could easily be adopted to multiply business systems, which means this platform is not only superior-performed but also economic.

Channel congestion occurs when the vehicular traffic density increases to a certain extent in Internet of Vehicles (IoV), even if there are only beacons in the wireless channel. To solve the problem, a Distributed-Weighted Fair Power Control (D-WFPC) algorithm was proposed. Firstly, considering the actual channel characteristics in IoV, the Nakagami-m fading channel model was used to establish the random channel model. Then, the mobility of the nodes in IoV was considered, and a power control optimization problem was established based on the Network Utility Maximization (NUM) model, which kept the local channel load under the threshold to avoid congestion. Finally, a distributed algorithm was designed by solving the problem with dual decomposition and iterative method. The transmit power of each vehicle was dynamically adjusted according to the beacons from neighbor vehicles. In the simulation experiment, compared with the fixed transmit power schemes, the D-FWPC algorithm reduced the delay and packet loss ratio effectively with the increase of traffic density, the highest reduction was up to 24% and 44% respectively. Compared with the Fair distributed Congestion Control with transmit Power (FCCP) algorithm, the D-FWPC algorithm had better performance all the way and the highest reduction in delay and packet loss ratio was up to 10% and 4% respectively. The simulation results show that the D-WFPC algorithm can converge quickly and ensure messages to be transmitted with low delay and high reliability in IoV.