Stability sensitivity for a cable-based coal–gangue picking robot based on grey relational analysis

This article aims to establish the relationship between the position and cable tension influencing factors and the stability, and propose a method for quantitative stability sensitivity assessment for a cable-based coal–gangue picking robot. Firstly, a structural stability measure approach is proposed for the cable-based coal–gangue picking robot. Secondly, a stability sensitivity analysis model is developed to investigate the stability sensitivity on the selected influencing factors based on the grey relational degree, where the influencing degree of each factor on the stability for the cable-based coal–gangue picking robot is explored with grey relational analysis. At last, a numerical study is carried out to demonstrate the stability measure approach and stability sensitivity analysis model for the cable-based coal–gangue picking robot was scientific and reasonable, where the end-grab position set which the robot can meet the predetermined stability requirements is obtained. And meanwhile, the correlation of each influencing factor on the stability for the robot is calculated. And the stability sensitivity simulation results show that (1) the correlation of the seven influencing factors on the stability are, in a descending order, cable tension T 2 > cable tension T 4 > cable tension T 3 > cable tension T 1 > z-direction displacement of the end-grab > x-direction displacement > y-direction displacement; (2) among the influencing factors, the cable tensions have greater influence on the stability of coal–gangue picking robot, and it is followed by the z-direction displacement of the end-grab, while y-direction displacement is found to have the minimal influence. This article provides a guiding direction for robust design of the sorting trajectory planning and control of the coal–gangue picking robots.

A Critical Study on Stability Measures of Feature Selection with a Novel Extension of Lustgarten Index

Stability of feature selection algorithm refers to its robustness to the perturbations of the training set, parameter settings or initialization. A stable feature selection algorithm is crucial for identifying the relevant feature subset of meaningful and interpretable features which is extremely important in the task of knowledge discovery. Though there are many stability measures reported in the literature for evaluating the stability of feature selection, none of them follows all the requisite properties of a stability measure. Among them, the Kuncheva index and its modifications, are widely used in practical problems. In this work, the merits and limitations of the Kuncheva index and its existing modifications (Lustgarten, Wald, nPOG/nPOGR, Nogueira ) are studied and analysed with respect to the requisite properties of stability measure. One more limitation of the most recent modified similarity measure, Nogueira’s measure, has been pointed out. Finally, corrections to Lustgarten’s measure have been proposed to define a new modified stability measure that satisfies the desired properties and overcomes the limitations of existing popular similarity based stability measures. The effectiveness of the newly modified Lustgarten’s measure has been evaluated with simple toy experiments.

Stability of Wheat Genotypes by AMMI and Yield Evaluated under Peninsular Zone of India

Background: Efficient estimation of main and interaction effects under multi environment trials had been carried out by AMMI. Studies with low proportion of the variance explained by first interaction principal component IPCA1 under AMMI analysis had been reported biased interpretation for the stability of the genotypes.Methods: Weighted Average of Absolute scores (WAASB), quantitative stability measure had been recommended for selection of productive genotypes with broad adaptation. The selection of promising genotypes had been assisted by use of superiority index (WAASBY) that considered simultaneous use of yield and stability by allowing variable weighting mechanism for yield and stability.Result: Wheat GW509 and HI1633 genotypes had been identified by stability measure WAASB for the first year. While Superiority index considered stability and high yield settled for HI1633 and Raj4083 genotypes. Analytic measures of adaptability PRVG and MHPRVG observed suitability of HI1633 and Raj 4083. SI expressed positive linear behaviour with yield, MHPRVG and PRVG measures. While stability measure WAASB maintained moderate negative correlation with yield, SI, MHPRVG and PRVG. Wheat genotypes HI1633, HI1641 and HI1646 identified by stability measure for the next year of study. Superiority index selected HI1641, HI1633 and MACS6752 genotypes. SI expressed direct linear relation with yield, MHPRVG and PRVG. Stability measure exhibited indirect relationships with SI, MHPRVG, PRVG and yield Wheat genotypes HI1633, Raj 4083 for first year and HI1641, MACS6752 for the second year of study.

Financial Inclusion and Stability: Review of Theoretical and Empirical Links

This paper reviews the literature on financial stability and financial inclusion—two broad objectives of financial policy that may be mutually dependent. The review suggests the possible co-dependence of stability and inclusion. We build on this theoretical motivation by exploring stylized facts (correlations) obtained from data sets that have been widely used in the literature on financial inclusion and stability. The empirical correlations suggest that, on average, financial inclusion and stability correlate negatively, but the correlations vary systematically across individuals, firms, and country contexts. Depending on the financial instrument and stability measure, positive correlations are also likely. These associations reflect some findings in the existing literature, but also point to knowledge gaps that can be addressed by future research.

On the Computation of Concept Stability Based on Maximal Non-Generator for Social Networking Services

The concept stability measure under the Formal Concept Analysis (FCA) theory is useful for improving the accuracy of structure identification of social networks. Nevertheless, the stability calculation is an NP-complete task which is the primary challenges in practical. Most existing studies have focused on the approximate estimate to calculate the stability. Therefore, we focus on introducing the Maximal Non-Generator-based Stability Calculation (MNG-SC) algorithm that directly deals with accurate stability calculation to pave the way for FCA’s application in structures identification of social networks. Specifically, a novel perspective of stability calculation by linking it to Maximal Non-Generator (MNG) is first provided. Then, the equivalence between maximal non-generator and lower neighbor concept is first proved, which greatly improves scalability and reduces computational complexity. The performed experiments show that the MNG-SC outperforms the pioneering approaches of the literature. Furthermore, a case study of identifying abnormal users in social networks is presented, which demonstrates the effectiveness and potential application of our algorithm.

On the stability of periodic binary sequences with zone restriction

Traditional global stability measure for sequences is hard to determine because of large search space. We propose the k-error linear complexity with a zone restriction for measuring the local stability of sequences. For several classes of sequences, we demonstrate that the k-error linear complexity is identical to the k-error linear complexity within a zone, while the length of a zone is much smaller than the whole period when the k-error linear complexity is large. These sequences have periods $$2^n$$ 2 n , or $$2^v r$$ 2 v r (r odd prime and 2 is primitive modulo r), or $$2^v p_1^{s_1} \cdots p_n^{s_n}$$ 2 v p 1 s 1 ⋯ p n s n ($$p_i$$ p i is an odd prime and 2 is primitive modulo $$p_i^2$$ p i 2 , where $$1\le i \le n$$ 1 ≤ i ≤ n ) respectively. In particular, we completely determine the spectrum of 1-error linear complexity with any zone length for an arbitrary $$2^n$$ 2 n -periodic binary sequence.

Financial Stability of Islamic and Conventional Banks of the MENA Region: Post and Pre-Crisis of CAMELS Framework

The present study provides new empirical evidence of bank stability measure for 12 Islamic and conventional banks in the MENA region, for a period from 2005 to 2014. The most known method of measuring bank stability is using CAMELS variables; it was adopted by multiple central banks. After calculating financial ratios for the CAMELS framework, we calculate the average for each variable for the two types of banks, for three periods: Pre-crisis 2005-2006, Subprime Crisis 2007-2008, and Post-Crisis 2009-2014, to examine the effect of the crisis on the soundness of Islamic and conventional banks.