Combined control strategy for synchronization control in multi-motor-pendulum vibration system

Multi-motor-pendulum vibration systems have been applied to design shale shakers in petroleum drilling engineering. However, synchronization of the multi-motor-pendulum vibration system is instable on account of external load disturbance and systematic parameter restriction, which is a principal factor to decrease screening efficiency of shale shakers. In this work, to maintain stability synchronization of three eccentric rotors driven by three induction motors, a combined synchronous control strategy by tracking velocity and phase among the motors is proposed. First, the dynamic model of the system is deduced based on the Lagrange equation. Second, adjacent cross-coupled control combined with master–slave control is designed to control speed and synchronization between the motors in the multi-motor-pendulum vibration system. Third, to ensure the precision and robustness of the control system, the velocity error, phase error, and coupling error controllers are designed with reaching law algorithm and global sliding mode control; and stability of the controller system is validated by the Lyapunov theorem. Finally, the effectiveness of the control strategy is verified by numerical simulation and compared with previous findings. The results indicate that synchronous state and velocity overshoot of the motors can be controlled with the combined control strategy; and robustness of the control strategy is better than other methods.

Aggregated Biomass Model Systems and Carbon Concentration Variations for Tree Carbon Quantification of Natural Mongolian Oak in Northeast China

Three systems of additive biomass models were developed and the effects of tree components, tree sizes, and tree growing regions on the carbon concentration were analyzed for Mongolian oak (Quercus mongolica Fisch. ex Ledeb.) in the natural forests of Northeastern China. The nonlinear seemingly unrelated regression (NSUR) method was used to fit each of the three systems simultaneously; namely, aggregated model systems with no parameter restriction (AMS0), aggregated model systems with one parameter restriction (AMS1), and aggregated model systems with three parameter restrictions (AMS3). A unique weighting function for each biomass model was applied to address the heteroscedasticity issue. The systems assertively guarantee the additivity property, in which, the summation of the respective predicted tree components (i.e., root, stem, branch, and foliage) will match the prediction of subtotals (i.e., crown and aboveground) and total biomass. Using one-, two-, and three-predictor combinations (i.e., D (diameter at breast height), D and H (total height), and D, H, and CL (crown length)) as the general model underlying formats, three systems of additive biomass model were developed. Our results indicate that (1) all of the aggregated model systems performed well and the differences between the systems were relatively small; (2) the rank order of the three systems based on an array of statistics are as follows: AMS0 > AMS1 > AMS3; (3) the carbon concentration significantly varied depending on the types of tree tissues and growing regions; (4) the regional respective component carbon concentration and regional weighted mean carbon concentration multiplied by observed biomass value appeared to be the best approach to calculate carbon stock.

Study of the diversity of AGN dust models

The dust component of active galactic nuclei (AGN) produces a broad infrared spectral energy distribution (SED), whose power and shape depends on the fraction of the source absorbed, and the geometry of the absorber respectively. This emitting region is expected to be concentrated within the inner ∼5 pc of the AGN which makes almost impossible to image it with the current instruments. The study the infrared SED by comparison between infrared AGN spectra and predicted models is one of the few way to infer the properties of this dust component. We explore a set of six dusty models of AGN with available SEDs, namely Fritz et al. (2006), Nenkova et al. (2008), Hoenig & Kishimoto (2010), Siebenmorgen et al. (2015), Stalevski et al. (2016), and Hoenig & Kishimoto (2017). They cover a wide range of morphologies, dust distributions and compositions. We explore the discrimination among models and parameter restriction using synthetic spectra (Gonzalez-Martin et al. 2019A, submitted), and perform spectral fitting of a sample of 110 AGN with Spitzer/IRS drawn from the Swift/BAT survey (Gonzalez-Martin et al. 2019B, submitted). Our conclusion is that most of these models can be discriminated using only mid-infrared spectroscopy as long as the host galaxy contribution is less than 50%. The best model describing sample is the clumpy disk-wind model by Hoenig & Kishimoto (2017). However, large residuals are shown irrespective of the model used, indicating that AGN dust is more complex than models. We found that the parameter space covered by models is not completely adequate. This talk will give tips for observers and modelers to actually answer the question: how is the dust arrange in AGN? This question will be one of the main subjects of future research with JWST in the AGN field.

Biomass Modelling ofAndrostachys johnsoniiPrain: A Comparison of Three Methods to Enforce Additivity

Three methods of enforcing additivity of tree component biomass estimates into total tree biomass estimates forAndrostachys johnsoniiPrain were studied and compared, namely, the conventional (CON) method (a method that consists of using the same independent variables for all tree component models, and for total tree model, and the same weights to enforce additivity), seemingly unrelated regression (SUR) with parameter restriction, and nonlinear seemingly unrelated regression (NSUR) with parameter restriction. The CON method was found to be statistically superior to any other method of enforcing additivity, yielding excellent fit statistics and unbiased biomass estimates. The NSUR method ranked second best but was found to be biased. The SUR method was found to be the worst; it exhibited large bias and had a poor fit for the biomass. Therefore, we recommend that only the CON and NSUR methods should be used for further estimates, provided that their limitations are considered, that is, exclusion of contemporaneous correlations for the CON method and consideration of the significant bias of the NSUR method.

Profit Maximization In The National Football League

<p class="MsoBodyText" style="text-align: justify; margin: 0in 0.5in 0pt; mso-pagination: none;"><span style="font-style: normal; font-size: 10pt;"><span style="font-family: Times New Roman;">This paper investigates if NFL teams maximize profits with respect to ticket price.<span style="mso-spacerun: yes;">&nbsp; </span>We modify Ferguson et al&rsquo;s (1991) NHL paper as it pertains to the NFL.<span style="mso-spacerun: yes;">&nbsp; </span>A profit function incorporating variable revenue and cost factors such as gate receipts and player expenses is employed.<span style="mso-spacerun: yes;">&nbsp; </span>A systems model is used as the estimation procedure to identify the determinants of ticket prices for NFL franchises.<span style="mso-spacerun: yes;">&nbsp; </span>The model implies a Kuhn-Tucker based cross equation parameter restriction that result from attendance capacity constraints.<span style="mso-spacerun: yes;">&nbsp; </span>Results from the regression are then used in conjunction with other data to numerically test the first order necessary profit maximization conditions.<span style="mso-spacerun: yes;">&nbsp; </span>The results indicate that over 80% of NFL teams set ticket prices in a manner consistent with gate receipt and profit maximization.</span></span></p>