In order to reduce the dependence on the number of labeled images, a novel semi-supervised learning method was proposed for automatic segmentation of nuclei. Firstly, a novel Convolutional Neural Network (CNN) was used to extract the cell region from the background. Then, a confidence map for the input image was generated by the discriminator network via applying a full convolutional network. At the same time, the adversarial loss and the standard cross-entropy loss were coupled to improve the performance of the segmentation network. Finally, the labeled images and unlabeled images were combined with the confidence maps to train the segmentation network, so that the segmentation network was able to identify the nuclei in the extracted cell regions. Experimental results on 84 images (1/8 of the total images in the training set were labeled, and the rest were unlabeled) showed that the SEGmentation accuracy measurement (SEG) score of the proposed nuclei segmentation method achieved 77.9% and F1 score of the method was 76.0%, which were better than those of the method when using 670 images (all images in the training set were labeled).

In order to forecast the number of equipment maintenance people more easily and validly, a common approach of selecting the features of input vector in Support Vector Machine (SVM) named Main Factor Method (MFM) was proposed. The relevant terms of "main factor", "driving factor", "voluntary action" and "actions' carrier" were defined, based on which the theoretical MFM was constructed. Firstly, the predicting vector's main factor of voluntary actions was setup by "infinitely related principle" and "action purpose" method. Then the driving factors which can be looked as the characteristics of SVM input vector were refined through the selected main factor and "selecting principles of driving factors". The experimental results and comparison with other congeneric methods show that the proposed method can select the more accurate prediction with the value of relative average error 0.0109.

The effect data of actions show a significant randomness because of lots of uncertain elements in the course of action. In order to explore the rules of warfare hidden behind the data, the effectiveness evaluation was studied based on statistical analysis method. The basic concept of action and its effectiveness were analyzed. With the simulation data produced by enhanced irreducible semi-autonomous adaptive combat neural simulation toolkit (EINSTein), a single, a group and multi group experimental methods were used to study the statistical characteristics of offensive actions and find out that to a party who has a combat advantage, compared with increased number of personnel, the increased radius of firepower can achieve better operational results. On this basis, an evaluation method of action effectiveness was proposed and validated with simulation data. Therefore, a feasible resolution is provided to evaluate the action effectiveness based on actual combat data.