Stochastic model updating using distance discrimination analysis

This manuscript presents a stochastic model updating method, taking both uncertainties in models and variability in testing into account. The updated finite element（FE） models obtained through the proposed technique can aid in the analysis and design of structural systems. The authors developed a stochastic model updating method integrating distance discrimination analysis（DDA） and advanced Monte Carlo（MC） technique to（1） enable more efficient MC by using a response surface model,（2） calibrate parameters with an iterative test-analysis correlation based upon DDA, and（3） utilize and compare different distance functions as correlation metrics. Using DDA, the influence of distance functions on model updating results is analyzed. The proposed stochastic method makes it possible to obtain a precise model updating outcome with acceptable calculation cost. The stochastic method is demonstrated on a helicopter case study updated using both Euclidian and Mahalanobis distance metrics. It is observed that the selected distance function influences the iterative calibration process and thus, the calibration outcome, indicating that an integration of different metrics might yield improved results.

Neural Firing Mechanism Underlying Two-Electrode Discrimination by 3D Transcutaneous Electrical Nerve Stimulation Computational Model

Transcutaneous electrical nerve stimulation(TENS) has been widely used for sensory feedback which is a key consideration of improving the performance of prosthetic hands. Two-electrode discriminability is the key to realize high-spatial-resolution TENS, but the neural firing mechanism is not clear yet. The goal of this research is to investigate the neural firing patterns under two-electrode stimulation and to reveal the potential mechanisms. A three-dimensional(3 D) model is established by incorporating Aβ fiber neuron clusters into a layered forearm structure. The diameters of the stimulating electrodes are selected as 5, 7, 9 and 12 mm, and the two-electrode discrimination distance(TEDD) is quantified. It is found that a distant TEDD is obtained for a relatively large electrode size, and 7 mm is suggested to be the optimal diameter of stimulating electrodes. The present study reveals the neural firing patterns under two-electrode stimulation by the 3 D TENS model. In order to discriminate individual electrodes under simultaneous stimulation, no crosstalk of activated Aβ fibers exists between two electrodes. This research can further guide the optimization of the electrode-array floorplan.

Water quality evaluation of Haihe River with fuzzy similarity measure methods

Fuzzy similarity measures, which are used to judge the closeness of two fuzzy sets, are presented to evaluate the water quality of the Haihe River. Based on the membership functions and coefficient of variation as the weights, four fuzzy similarity measures （including Lattice similarity measure, Hamming similarity measure, Euclidean similarity measure and the max-min similarity measure） are used to classify the 299 samples into the proper water quality standard ranks. The results are compared with the traditional distance discriminant methods. The calculation of two traditional distance discriminant methods （both Euclidean distance and absolute value distance） is also based on the use of coefficients of variation as the weights. Without the Lattice similarity measure, for this method loses some information, the correct assignment of samples classified into the same water quality ranks is 75.92% with the other three similarity measures and two distance discriminant methods. This result shows the reliability of the five methods. Only considering the three similarity measures, there were only 1.01% of the samples that did not classify to the same ranks, while the corresponding ratio of the two distance discriminant methods was 5.69%. The results of leave-one-out cross validation show that more than 88% of the samples are classified to the proper ranks, which demonstrates that the similarity measures are suitable to evaluate the water quality of the Haihe River.