Inaugural editorial: Military Medical Research.

Military medicine is one of the most innovative part of human civilization. Along with the rapid development of medicine and advances in military techniques, military medicine has become the focus and intersection of new knowledge and new technologies. Innovation and development within military medicine are always ongoing, with a long and challenging path ahead. The establishment of "Military Medical Research" is expected to be a bounden responsibility in the frontline of Chinese military medicine.

[Oil atomic spectrometric feature selection by Parzen window based vague sets theory].

Large quantity and ambiguity of oil atomic spectrometric information greatly affects the applicable efficiency and accuracy in fault diagnosis. A novel method for choosing less and effective spectrometric features is presented. Based on gearbox test bed, we simulated the normal wear state and two typical faults to acquire the lubricant samples. The three wear states are regarded as three vague sets, and spectrometric feature values are vague values on vague sets. Based on similarity between vague values, mean vague sensibility (MVS) is defined to describe the sensitive degree of spectrometric feature to wear state. Besides, the membership degrees of vague sets greatly depend on human experience. The probability density distribution of spectrometric data of three wear states was estimated with Parzen window. Combined with Bayesian formula, the range of vague sets membership was calculated. Experimental results verify that the proposed method is of efficient help in choosing high fault-sensitive features from so many spectrometric features.

[Research on oil atomic spectrometric data semi-supervised fuzzy C-means clustering based on Parzen window].

A Parzen window based semi-supervised fuzzy c-means (PSFCM) clustering algorithm was presented. The initial clustering centers of fuzzy c-means (FCM) were determined with training samples. The membership iteration of FCM was redefined after the membership degrees of testing samples relatively to each state were calculated using Parzen window. Two typical faults of gear box were simulated through the gear box bed in order to acquire the lubricant samples. Concentration of Fe, Si and B, which were the representative elements, was selected as the three-dimensional feature vectors to be analyzed with FCM and PSFCM clustering methods. The clustering results were that the correct ratio of FCM was 48.9%, while that of PSFCM was 97.4% because of integrating with supervised information. Experimental results also indicated that it can reduce the dependence of the experience and lots of faults data to introduce PSFCM into oil atomic spectrometric analysis. It was of great help in improving the wear faults diagnosis ratio.

[Research on monitoring mechanical wear state based on oil spectrum multi-dimensional time series model].

A new method using oil atomic spectrometric analysis technology to monitor the mechanical wear state was proposed. Multi-dimensional time series model of oil atomic spectrometric data of running-in period was treated as the standard model. Residues remained after new data were processed by the standard model. The residues variance matrix was selected as the features of the corresponding wear state. Then, high dimensional feature vectors were reduced through the principal component analysis and the first three principal components were extracted to represent the wear state. Euclidean distance was computed for feature vectors to classify the testing samples. Thus, the mechanical wear state was identified correctly. The wear state of a specified track vehicle engine was effectively identified, which verified the validity of the proposed method. Experimental results showed that introducing the multi-dimensional time series model to oil spectrometric analysis can fuse the spectrum data and improve the accuracy of monitoring mechanical wear state.