optimal balancing is a new approach based on optimal control a which modes, controls and the amount of equilibrium scale are calculated simultaneously. this approach has a significant effect on increasing performance of industrial robots which causes maximum decreasing of energy consumption. despite many applications of robotics such as welding and gluing, the path is predefined , but other unk...

Skewness indicates a lack of symmetry in a distribution. Knowing the symmetry of the underlying data is important for parametric analysis, fitting distributions or doing transformations to the data. The coefficient of skewness is the commonly used measure to identify a lack of symmetry in the underlying data, although graphical procedures can also be effective. We evaluate five different method...

I introduce a new statistical method, analysis of skewness, for quantifying large-scale evolutionary trends as a combination of both passive and driven trends. My approach is based on the skewness of subclades within a parent clade. I partition the total skewness of the parent clade into three components: (1) skewness between subclades; (2) skewness within subclades; and (3) skewness due to cha...

In the past decade, LIght Detection And Ranging (LIDAR) has been recognised by both the commercial and public sector as a reliable and accurate technique for land surveying. However, the analysis of Digital Surface Models (DSMs) from complex LIDAR data is still challenging. Commonly, the first task to investigate LIDAR data point clouds is to separate ground and object points as a preparatory s...

The existence of many large transactions distributed databases with high data schemas, the centralized approach for mining association rules in such databases will not be feasible. Some distributed algorithms have been developed [FDM, CD], but none of them have considered the problem of data skews in distributed mining of association rules. The skewness of datasets reduces the workload balancin...

W find that initial public offerings (IPOs) with high expected skewness experience significantly greater first-day returns. The skewness effect is stronger during periods of high investor sentiment and is related to differences in skewness across industries as well as to time-series variation in the level of skewness in the market. IPOs with high expected skewness earn more negative abnormal re...

   The present study is an attempt toward evaluating the performance of portfolios using mean-variance-skewness model with negative data. Mean-variance non-linear framework and mean-variance-skewness non- linear framework had been proposed based on Data Envelopment Analysis, which the variance of the assets had been used as an input to the DEA and expected return and skewness were the output. C...

Existing studies have found that ex post stock returns are positively skewed, but such skewness is only persistent for individual stocks, not for portfolios. This implies that the ex post knowledge of skewness may not be useful in ex ante portfolio selection. However, the portfolios in these studies are not optimally formed because preferences for skewness are not taken into consideration when ...

The first part of this article deals with fitting of regression equations for the sampling properties, variance of L-standard deviation (l2), and bias and variance of L-skewness (t3), based on Monte-Carlo simulation results, for generalised Normal (Lognormal-3) and Pearson-3 distributions. These fitted equations will be useful in formulating goodness-of-fit test statistics in regional frequency...