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In order to solve the problems of strong coupling, complex constraints and high dimensions in optimal scheduling of multi-energy hub systems, differential evolution quantum particle swarm optimization (DEQPSO) algorithm is proposed. The algorithm combines the mutation, crossover and selection operations in the differential evolution algorithm with the particle position update formula in the quantum particle swarm optimization algorithm, which increases the diversity of the population in the quantum particle swarm optimization algorithm, solves the problem that the particles are easy to fall into the local optimum in the middle and later stages of the search, and improves the global search ability of the algorithm. The standard test function is used to test the algorithm. The test results show that the algorithm has good convergence and global search ability. In this paper, the algorithm is applied to the optimal scheduling of multi-energy hub system, and the calculation results show the effectiveness and applicability of the algorithm.

Using total counts in simultaneous annual surveys, we monitored the population size and spatial distribution of oriental white storks (Ciconia boyciana) wintering in Poyang Lake between 1998 and 2011. Results showed that Poyang Lake wetland is an important wintering ground for oriental white storks, with an annual average population number of 2 305±326. The population sizes in 2004, 2005, 2010, and 2011 were higher than the highest-ever estimate of its global population. In 2005, we recorded 3 789 individuals, which was the maximum population number within the period of 1998–2011. The storks inhabited 52 lakes, with the greatest distance between these lakes being 180.3 km. The storks presented a clustered distribution pattern in the Poyang Lake wetland, irrespective of the number of individuals or occurrence frequencies. Shahu, Dahuchi, Banghu, and Hanchihu were most frequently used lakes and had the largest annual average numbers of storks. There was a significant positive correlation between occurrence frequency and annual average number of storks in the lakes. Most of the lakes important for storks were covered by existing nature reserves, though some lakes outside the reserves were also frequently used. About 64.9%±5.5% of the storks were found in nature reserves. In addition, the storks more frequently used and clumped in significantly larger flocks in lakes within nature reserves than lakes outside.

This work presents a approach for multiple cooperating mobile robots for moving object tracking in unknown environment. Each robot in the team uses the full covariance extend Kalman filter based algorithm to simultaneously localize the robot and target while building a landmark feature map of the surrounding environment. Meanwhile, in local robot system the covariance intersection based data fusion method is used to fuse information sent by the other robot teammates, those information may contains the location of target and the location of robot itself from other teammate’s point of view. The method is distributed, and let the multi-robot system have the ability of robustness. The results of simulation validate a higher accuracy of our method compared with non-fusion single robot solution.

This paper provides a method for flying robot path planning for overhead powerline inspection which based on the unmanned micro-helicopter model. The robot coordinate system is set up to solve position of the unknown and dynamic obstacles in the robot coordinates. This paper presents the key points algorithm for the path planning which generates new key points through real-time information and data that detected by sensors in unknown environment. The algorithm has good convergence which can be used effectively in unknown dynamic vast environment, and can inspect power lines equipment and location of key areas effectively under the safety of the robot. The simulation results of f path planning of flying robot for overhead powerline inspection based on key Points algorithm prove the feasibility and validity of the algorithm.

In wireless sensor network applications, effective clustering algorithm can reduce energy consumption, which can increase network scalability and lifetime. In most of traditional clustering methods, clusters do not form until their cluster heads are selected and these algorithms are usually running under the single data transmission mode. A clustering strategy for WSNs based on Fuzzy Cluster Mean (FCM) is proposed in this paper. It is a new clustering mechanism of generating the clusters first and then selecting the cluster head(CH). This strategy based on FCM has good characteristics of clustering quick, reducing energy consumption and being applied in different data transmission modes. The correctness and feasibility is validated in simulations. It is shown that energy consumption is better than the similar clustering algorithms. When different selection modes of CH are selected in this strategy, the clustering strategy based on FCM shows the best efficiency in two data transmission modes.