Eccentricity Parameters Identification for a Motorized Spindle System Based on Improved Maximum Likelihood Method

As a kind of rotor system, the electric spindle system is the core component of the precision grinding machine. The vibration caused by the mass imbalance is the main factor that causes the vibration of the grinding machine. Identifying the eccentricity parameters in an electric spindle system is a key issue in eliminating mass imbalances. It is difficult for engineers to understand the approximate range of eccentricity by experience; that is, it is difficult to obtain a priori information about eccentricity. At the same time, due to the geometric characteristics of the electrospindle system, the material factors and the randomness of the measurement response, these uncertain factors, even in a small case, are likely to cause large deviations in the eccentricity recognition results. The search algorithm used in the maximum likelihood method to identify the eccentricity parameters of the electrospindle system is computationally intensive, and the sensitivity in the iterative process brings some numerical problems. This paper introduces an Advance-Retreat Method (ARM) of the search interval to the maximum likelihood method, the unknown parameter increment obtained by the maximum likelihood method is used as the step size in the iteration, and the Advance-Retreat Method of the search interval is used to adjust the next design point so that the objective function value is gradually decreasing. The recognition results under the three kinds of measurement errors show that the improved maximum likelihood method improves the recognition effect of the maximum likelihood method and can reduce the influence of uncertainty factors on the recognition results, and the robustness is satisfactory.

USA Wind Energy-Caused Bat Fatalities Increase with Shorter Fatality Search Intervals

Wind turbine collision fatalities of bats have likely increased with the rapid expansion of installed wind energy capacity in the USA since the last national-level fatality estimates were generated in 2012. An assumed linear increase of fatalities with installed capacity would expand my estimate of bat fatalities across the USA from 0.89 million in 2012 to 1.11 million in 2014 and to 1.72 million in 2019. However, this assumed linear relationship could have been invalidated by shifts in turbine size, tower height, fatality search interval during monitoring, and regional variation in bat fatalities. I tested for effects of these factors in fatality monitoring reports through 2014. I found no significant relationship between bat fatality rates and wind turbine size. Bat fatality rates increased with increasing tower height, but this increase mirrored the increase in fatality rates with shortened fatality search intervals that accompanied the increase in tower heights. Regional weighting of mean project-level bat fatalities increased the national-level estimate 17% to 1.3 (95% CI: 0.15–3.0) million. After I restricted the estimate’s basis to project-level fatality rates that were estimated from fatality search intervals <10 days, my estimate increased by another 71% to 2.22 (95% CI: 1.77–2.72) million bat fatalities in the USA’s lower 48 states in 2014. Project-level fatality estimates based on search intervals <10 days were, on average, eight times higher than estimates based on longer search intervals. Shorter search intervals detected more small-bodied species, which contributed to a larger all-bat fatality estimate.

Extended GRASP-Capacitated K-Means Clustering Algorithm to Establish Humanitarian Support Centers in Large Regions at Risk in Mexico

Mexico is located within the so-called Fire Belt which makes it susceptible to earthquakes. In fact, two-thirds of the Mexican territory have a significant seismic risk. On the other hand, the country’s location in the tropical zone makes it susceptible to hurricanes which are generated in both the Pacific and Atlantic Oceans. Due to these situations, each year many communities are affected by diverse natural disasters in Mexico and efficient logistic systems are required to provide prompt support. This work is aimed at providing an efficient metaheuristic to determine the most appropriate location for support centers in the State of Veracruz, which is one of the most affected regions in Mexico. The metaheuristic is based on the K-Means Clustering (KMC) algorithm which is extended to integrate (a) the associated capacity restrictions of the support centers, (b) a micro Genetic Algorithm μGA to estimate a search interval for the most suitable number of support centers, (c) variable number of assigned elements to centers in order to add flexibility to the assignation task, and (d) random-based decision model to further improve the final assignments. These extensions on the KMC algorithm led to the GRASP-Capacitated K-Means Clustering (GRASP-CKMC) algorithm which was able to provide very suitable solutions for the establishment of 260 support centers for 3837 communities at risk in Veracruz, Mexico. Validation of the GRASP-CKMC algorithm was performed with well-known test instances and metaheuristics. The validation supported its suitability as alternative to standard metaheuristics such as Capacitated K-Means (CKM), Genetic Algorithms (GA), and Variable Neighborhood Search (VNS).

Compact and hash based variants of the suffix array

Full-text indexing aims at building a data structure over a given text capable of efficiently finding arbitrary text patterns, and possibly requiring little space. We propose two suffix array inspired full-text indexes. One, called SA-hash, augments the suffix array with a hash table to speed up pattern searches due to significantly narrowed search interval before the binary search phase. The other, called FBCSA, is a compact data structure, similar to Mäkinen’s compact suffix array (MakCSA), but working on fixed size blocks. Experiments on the widely used Pizza & Chili datasets show that SA-hash is about 2–3 times faster in pattern searches (counts) than the standard suffix array, for the price of requiring 0.2n–1.1nbytes of extra space, wherenis the text length. FBCSA, in one of the presented variants, reduces the suffix array size by a factor of about 1.5–2, while it gets close in search times, winning in speed with its competitors known from the literature, MakCSA and LCSA.