At present, the researches of ensemble clustering mainly focus on the optimization of ensemble strategy, while the measurement and optimization of the quality of basic clustering are rarely studied. On the basis of information entropy theory, a quality measurement index of basic clustering was proposed, and a three-way screening method for basic clustering was constructed based on three-way decision. Firstly, α, β were reset as the thresholds of three-way decision of basic clustering screening. Secondly, the average cluster quality of each basic clustering was calculated and was used as the quality measurement index of each basic clustering. Finally, the three-way decision was implemented. For one three-way screening, its decision strategy is:1) deleting the basic clustering if the quality measurement index of the basic clustering is less than the threshold β; 2) keeping the basic clustering if the quality measurement index of the basic clustering is greater than or equals to the threshold α; 3) recalculating the quality of a basic clustering and if the quality measurement index of the basic clustering is greater than β and less than α or equals to β. For the third option, the decision process continues until there is no deletion of basic clustering or reaching the times of iteration. The comparative experiments show that the three-way screening method of basic clustering can effectively improve the ensemble clustering effects.

Focusing on the issue that the path calculated by traditional path planning algorithm is not necessarily the optimal path in reality, a path planning algorithm which combined the experience of taxi driving and took time as a measure was proposed. The implementation of this algorithm was to transform the path planning technology which took calculation as the center into data-driven mining technology which regarded data as the center. Firstly, the real manned trajectory data were extracted from a large number of taxi trajectory data and matched to the road network data. Then, the access frequency of the road segments were calculated according to the calculation results of map-matching, and Top- k road sections were selected as hot sections; Secondly, the similarity of road tracks between hot sections was calculated, and the trajectories were clustered to build k sections of hot road map based on the road network. Finally, an improved A ^* algorithm was used to calculate the optimal path. The experimental results show that compared with the traditional shortest path planning algorithm and the path planning algorithm based on hierarchical road network, the path planning method based on hot section map can shorten the length of the planning path and the travel time and improve the time efficiency of path planning.

The existing Time Division Multiple Access (TDMA) scheduling methods for industrial emergency data under the conditions of asynchronous and multi-channel medium have the problems of high delay, saturated Control Channel (CC), and large energy consumption. To solve these problems, an Emergency data scheduling algorithm Oriented Asynchronous Multi-channel industrial wireless sensor networks, called EOAM, was proposed. First, the receiver-based strategy was adopted to solve the problem of saturated control channel during asynchronous multi-channel scheduling. Then a well-designed Special Channel (SC) together with the priority indication method was proposed to provide fast channel switch and real-time transmission of emergency data; additionally, the non-urgent data was allowed to occupy channel by a backoff-based mechanism indicated by the priority indication method, which could ensure the utilization of special channel. EOAM was suitable for both unicast and broadcast communications. The simulation results show that, compared with the Distributed Control Algorithm (DCA), the transmission delay of EOAM can reach 8 ms, the reliability is above 95%, and the energy consumption is reduced by 12.8%, which can meet the transmission requirements of industrial emergency data.

The available network management approaches are not able to monitor Multi-protocol Label Switch (MPLS) Traffic Engineering (TE) tunnels dynamically in real-time. To address this problem, a real-time monitoring approach for MPLS TE tunnels, called MPLS TE Tunnels Real-time Monitoring (MTRM), was proposed in this paper. A probe was placed in the network to collect Open Shortest Path First-TE (OSPF-TE) signaling messages passively. Based on the collected information, a MPLS network model was built and tunnel paths were dynamically computed using the tunnel paths real-time monitoring algorithm. The MPLS TE tunnel paths could thus be monitored in real-time. The simulation experiments were carried out on a MPLS network with 15 nodes. The results show that the proposed approach can monitor the changes of MPLS TE tunnels within five seconds, with precision over 90%. This real-time monitoring approach can greatly reduce the difficulties of MPLS network management and TE implementation, and it has a wide application prospect.

In order to improve the speed of global optimization algorithm, a global optimization algorithm of intelligence control system was presented. The feedback idea of closed loop control system was applied in this algorithm that made the value of the object function gradually close to the input in the iterative process until reaching the global optimization. The key of the algorithm lies in the design of control strategy and the initial setting of parameters. In order to reduce the difficulty of initial setting of parameters and ensure the precision of the algorithm, the filled function was used to improve the global optimization algorithm of intelligence control system. Verified by twelve standard test functions, the improved algorithm is faster than filled function method, and is more accurate than the global optimization algorithm of intelligence control system.

In order to meet the demands for both availability and processing speed, in reference to dark channel prior estimation, an image restoration algorithm based on Approximate Point Spread Function Examining (APEX) algorithm commonly used in image deblurring was proposed. Meanwhile, because different-sized images under different weather conditions have different APEX parameters, the APEX parameter value was adjusted dynamically according to sandstorm and fog degree. Furthermore, unlike other multiple images methods, the proposed algorithm needs only one image to be the input. The experimental results show that the proposed algorithm is effective in restoring images. By using color constancy algorithm, the source color components were balanced; furthermore, the visual effect of images was enhanced.