Quantitative comparison of similarity measure and entropy for fuzzy sets

Comparison and data analysis with the similarity measures and entropy for fuzzy sets were carried out. The distance proportional value between the fuzzy set and the corresponding crisp set was considered by the fuzzy entropy. The relation between the similarity measure and the entropy for fuzzy set was also analyzed. The fuzzy entropy was reformulated as the dissimilarity measure. Furthermore, crisp set having the minimum uncertainty with respect to the corresponding fuzzy set was also proposed. Finally, derivation of a similarity measure from entropy with the help of total information property was derived. A simple example shows the relation between similarity measure and fuzzy entropy, in which the summation of similarity measure and fuzzy entropy represents a constant value.

Quantitative detection and comparison of sulfate glycosaminoglycans content in extracellular matrix of in vitro cultured epiphyseal, articular and rib chondrocytes

Objective To establish a method for quantitative detection of the sulfate glycosaminoglycans ( GAG) content in extracellular matrix of in vitro cultured chondrocytes so as to evaluate the biological characteristics of epiphyseal, articular and rib chondrocytes. Methods Sulfate GAG content in extracellular matrix of three chondrocytes was measured by the modified dimethylmethylene blue (DMB) method. The changes of the toluidine blue (TB) stain of chondrocytes were observed by light microscope. Results Primary chondrocytes had the highest content of sulfate GAG in the extracellular matrix, ie, epiphyseal chondrocytes reached ( 70. 12 ± 7. 72 )μg/cm2, articular chondrocytes (92.00 ± 10.15) μg/cm2 and rib chondrocytes (80.61 ± 11. 40) μg/cm2, respectively. On the third pasage chondrocytes, epiphyceal chondrocytes decreased to (53.27 ± 9. 50 ) μg/cm2, articular chondrocytes to (63.88 ± 11.92) μg/cm2 and rib chondrocytes to (58.94 ±8.21) μg/cm2, respectively. The change of TB in every passage

Evaluation of Mid-Depth Currents of NCEP Reanalysis Data in the Tropical Pacific Using ARGO Float Position Information

The global project of the Array for Real-time Geostrophic Oceanography （ARGO） provides a unique opportunity to observe the absolute velocity in mid-depths of the world oceans. A total of 1597 velocity vectors at 1000 （2000） db in the tropical Pacific derived from the ARGO float position information during the period November 2001 to October 2004 are used to evaluate the intermediate currents of the National Centers for Environmental Prediction reanalysis. To derive reliable velocity information from ARGO float trajectory points, a rigorous quality control scheme is applied, and by virtue of a correction method for reducing the drift error on the surface in obtaining the velocity vectors, their relative errors are less than 25%. Based on the comparisons from the quantitative velocity vectors and from the space-time average currents, some substantial discrepancies are revealed. The first is that the velocities of the reanalysis at mid-depths except near the equator are underestimated relative to the observed velocities by the floats. The average speed difference between NCEP and ARGO values ranges from about -2.3cm s^-1 to -1.8 cm s^-1. The second is that the velocity difference between the ocean model and the observations at 2000 dB seems smaller than that at 1000 dB. The third is that the zonal flow in the reanalysis is too dominant so that some eddies could not be simulated, such as the cyclonic eddy to the east of 160°E between 20°N and 30°N at 2000 dB. In addition, it is noticeable that many floats parking at 1000 dB cannot acquire credible mid-depth velocities due to the time information of their end of ascent （start of descent） on the surface in the trajectory files. Thus, relying on default times of parking, descent and ascent in the metadata files gravely confines their application to measuring mid-depth currents.

Compared Insights on Machine-Learning Anomaly Detection for Process Control Feature

Anomaly detection is becoming increasingly significant in industrial cyber security,and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber attacks.However,different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature samples.As a sequence,after developing one feature generation approach,the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning algorithm.Based on process control features generated by directed function transition diagrams,this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their matching abilities.Furthermore,this paper not only describes some qualitative properties to compare their advantages and disadvantages,but also gives an in-depth and meticulous research on their detection accuracies and consuming time.In the verified experiments,two attack models and four different attack intensities are defined to facilitate all quantitative comparisons,and the impacts of detection accuracy caused by the feature parameter are also comparatively analyzed.All experimental results can clearly explain that SVM(Support Vector Machine)and WNN(Wavelet Neural Network)are suggested as two applicable detection engines under differing cases.