feedback compensation
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Author(s):  
Ahmad Sahro Wardi

The aims of this study to analyze the effect of job characteristics (X1), compensation (X2) and religiosity (X3) on employee loyalty (Y) Tabassam Farm Mojokerto. Job characteristics have five indicators, namely job skills, task identity, task significance, task autonomy, and task feedback. Compensation has six indicators, namely salary, incentives, bonuses, allowances, insurance, and non- financial rewards. Religiosity has five indicators, namely ideology, practice, experience, knowledge and application. Employee loyalty has four indicators, namely obedience, responsibility, dedication, and integrity. The population of this research is Tabassam Farm employees who have worked for more than two years.This research sample of 73 people. The hypothesis used in this study uses multiple linear regression analysis. The results of this study are job characteristics (X1) and religiosity (X3) have a simultaneous and partial effect on employee loyalty, while the compensation variable (X2) has a simultaneous but not partial effect on employee loyalty. The value of the coefficient of determination in this study is 64.4% which indicates that employee loyalty is influenced by job characteristics, compensation, and religiosity, while the remaining 35.6% is influenced by other variables not examined in this study.


Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 298
Author(s):  
Yu-Ting Chen ◽  
Chian-Song Chiu ◽  
Ya-Ting Lee

Mobile robots are widely used in many applications, while various types of mobile robots and their control researches have been proposed in literature. Since swarm robots have higher flexibility and capacity for teamwork, this paper presents a grey estimator-based tracking controller for formation trajectory tracking of swarm robots. First, wheel-type mobile robots are used and modeled for the controller design. Then, a grey dynamic estimator is developed to estimate the environmental disturbance and model uncertainty for linear feedback compensation. As a result, the asymptotic trajectory tracking is assured, so that the application on the swarm robot formation is achieved for a multi-agent system. The computational complexity is slightly reduced by the design. Finally, in order to verify the reliability of swarm robot formation, several types of formation are maintained by the grey estimator-based feedback linearization controller.


2021 ◽  
Author(s):  
Chunling Yang ◽  
Xue Zhang ◽  
Xuqiang Yang

Author(s):  
Caroline A. Niziolek ◽  
Benjamin Parrell

Purpose Speakers use auditory feedback to guide their speech output, although individuals differ in the magnitude of their compensatory response to perceived errors in feedback. Little is known about the factors that contribute to the compensatory response or how fixed or flexible they are within an individual. Here, we test whether manipulating the perceived reliability of auditory feedback modulates speakers' compensation to auditory perturbations, as predicted by optimal models of sensorimotor control. Method Forty participants produced monosyllabic words in two separate sessions, which differed in the auditory feedback given during an initial exposure phase. In the veridical session exposure phase, feedback was normal. In the noisy session exposure phase, small, random formant perturbations were applied, reducing reliability of auditory feedback. In each session, a subsequent test phase introduced larger unpredictable formant perturbations. We assessed whether the magnitude of within-trial compensation for these larger perturbations differed across the two sessions. Results Compensatory responses to downward (though not upward) formant perturbations were larger in the veridical session than the noisy session. However, in post hoc testing, we found the magnitude of this effect is highly dependent on the choice of analysis procedures. Compensation magnitude was not predicted by other production measures, such as formant variability, and was not reliably correlated across sessions. Conclusions Our results, though mixed, provide tentative support that the feedback control system monitors the reliability of sensory feedback. These results must be interpreted cautiously given the potentially limited stability of auditory feedback compensation measures across analysis choices and across sessions. Supplemental Material https://doi.org/10.23641/asha.14167136


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