Narrow Results

Invasive weed optimization (IWO) algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm are inclined to fall into local optimum with lower convergence accuracy when separately used to deal with large scale global optimization (LSGO) problems. In order to fully utilize the advantages of these two intelligent algorithms and complement each other, following the idea of portfolio optimization, this paper correspondingly adjusts and improves the quantum models of IWO and QPSO, organically integrates the two algorithms, and proposes the quantum-behaved invasive weed optimization (QIWO) algorithm. This mixed algorithm can achieve the purpose of information exchange and cooperative search through alternate search enables the make algorithm converge to the optimal solution quickly, properly overcoming the defects of falling into local optimum and premature convergence. Test results of 20 LSGO functions show that compared with other algorithms, QIWO has stronger global optimization capability, faster convergence speed and higher convergence accuracy.

In the actual environment of security detection, many kinds of liquids often exist in the same detection background, and their dangerous levels are difficult to identify. Therefore, it is very important to research on identifying the dangerous levels of various liquids. The paper establishes the $S$-parameter database of tested samples under specific detection environment with free space method. In the actual detection, ultra-wide-band (UWB) centimeter wave is used to measure the $S$-parameters of several detected liquids first. Then the fast independent component analysis (FastICA) algorithm is used for unmixing the mixed signal by Newton’s iteration method and the negative entropy maximization search principle. The unmixed signal matches with the sample database adaptively, so the dangerous levels of the detected liquids are identified. Multiple experiments show that FastICA algorithm can reach a matching rate of 95% between water and 90${}^{\#}$ gasoline or alcohol and 90${}^{\#}$ gasoline, it also can reach a matching rate of around 73% between water and alcohol. This algorithm has a quick response and high reliability for identification of dangerous liquids. FastICA algorithm in this paper is applied for detecting the dangerous liquids for the first time, and it has high application value.

In this paper, we proposed an adaptive fuzzy rule-based color image segmentation algorithm. By using look-up table for designing a FIS (Fuzzy Inference System) to compute similarity percentage of the neighboring pixels and region feature histogram information, then we can use fast-merge for achieving a new color image segmentation algorithm. The experimental results show that our algorithm can produce good results compared to some existing algorithm.

In project managing, the performance salary management mode is often used to motivate project managers and other similar staff to improve performance or reduce the cost. But the engineering activities who own a lot of internal and external uncertain factors can not be known by the principle. It is difficult for to develop a suitable incentive target to project managers etch. This paper thinks that the manager self master the maximum of information on engineering activities. So this paper sets up an incentive model: the project managers themselves report performance objectives; owner gives the managers reward or punishment combined with their reported performance and actual performance. The model to ensure that the project manager is only accurate self reported its results to get the maximum profit. At the same time, it cans incentive managers to improve performance or reduce the cost. This paper focuses on setting up the model, analyzing the model parameters. And cite an example analyze them.