Aiming at the problems that swarm intelligence optimization algorithms are easy to fall into local optimum as well as have low population diversity in the optimization process and are difficult to optimize high-dimensional functions， an Alternately Optimizing Algorithm based on Brownian-movement and Gradient-information （AOABG） was proposed. First， a global and local alternately optimizing strategy was used in the proposed algorithm， which means the local search was switched in the range of getting better and the global search was switched in the range of getting worse. Then， the random walk of uniform distribution probability based on gradient information was introduced into local search， and the random walk of Brownian motion based on optimal solution position was introduced into global search. The proposed AOABG algorithm was compared with Harris Hawk Optimization （HHO）， Sparrow Search Algorithm （SSA） and Special Forces Algorithm （SFA） on 10 test functions. When the dimension of test function is 2 and 10， the mean value and standard deviation of AOABG’s 100 final optimization results on 10 test functions are better than those of HHO， SSA and SFA. When the test function is 30-dimensional， except for Levy function where HHO performs better than AOABG but the mean value of the two is in the same order of magnitude， AOABG performs best on the other nine test functions with an increase of 4.64%-94.89% in the average optimization results compared with the above algorithms. Experimental results show that AOABG algorithm has faster convergence speed， better stability and higher accuracy in high-dimensional function optimization.

Aiming at the problem that the complexity of cervical cell image fine segmentation makes it difficult to achieve automatic abnormal cell screening based on cell image segmentation, a cervical cell classification algorithm without fine segmentation step was proposed. Firstly, a new feature named MAXimum Section (MAXSection) was defined for describing the distribution of pixel values, and was combined with Back Propagation (BP) neural network and Selective Search algorithm to realize the accurate extraction of nucleus Region Of Interest (ROI) (the highest accuracy was 100%). Secondly, two parameters named estimated length and estimated width were defined based on MAXSection to describe morphological changes of abnormal nucleus. Finally, according to the characteristic of absolute enlargement of cervical nucleus when cervical cancer occurs, the classification of abnormal nucleus (at least one parameter of estimated length and width is greater than 65) and normal nucleus (estimated length and width are both less than 65) can be realized by using the above two parameters. Experimental results show that the proposed algorithm has screening accuracy of 98.89%, sensitivity of 98.18%, and specificity of 99.20%. The proposed algorithm can complete the total process from the input of whole Pap smear image to the output of final screening results, realizing the automation of abnormal cervical cell screening.

Global System for Mobile Communications for Railway （GSMR） communication system of highspeed railway is vulnerable to the interference of wireless signals of other types. In this paper, the authors analyzed and researched several GSMR interference signals and proposed a new kind of signal separation algorithm based on the multiframe statistical spectrum. The idea of the algorithm was that the interference signal was divided into multiple frames and accumulated to statistical spectrum. If the number of frames was sufficient, its spectral parameter tended to be a constant. The experimental results show that the algorithm can quickly separate the original and interference signals in the case of known original signal spectrum, its effect and versatility is better, and the complexity of the algorithm is less than the traditional method.

A concurrent processes grouping output method was proposed to address the problem that storage performance decreases when testing Nucleus for European Modeling of the Ocean (NEMO) system with massive processes on TH-1A supercomputer. This method designed a reasonable control strategy based on TH-1A architecture and split processes into groups to alleviate the process competition of concurrency I/O. Testing result shows that the storage performance of the GYRE012 global sample can be improved more than 33% using the optimization method of concurrent processes grouping output, and in the mean time the total performance can be improved about 28%.

Most of the existing algorithms for Wireless Sensor Networks (WSN) localization in underground coal mine have problems of low accuracy and high cost. A new 3D localization algorithm was proposed for underground coal mine based on the regular deployment of beacon nodes. First, the beacon nodes were deployed according to the characteristics of the underground tunnel; second, the beacon nodes and unknown node were projected onto the same high level while doing location estimation; third, 2D position was calculated by trilateration; at last the 3D position was obtained by means of combining the height difference between unknown nodes and beacon nodes. The theoretical analysis and simulation results show that the algorithm is of less calculation, low communication, higher positioning accuracy and good stability.