Adaptive Control for Multi-Shaft with Web Materials Linkage Systems

In this paper, a fuzzy disturbance observer and a high-gain disturbance observer based on a variable structure controller are applied to deal with imprecise multi-shaft with web materials linkage systems taking into account the variation of the moment of inertia. Specifically, a high-gain disturbance observer and an adaptive fuzzy algorithm are separately applied to estimate system uncertainties and external disturbances. The high-gain disturbance observer is designed with auxiliary variables to avoid the amplification of the measurement disturbance, and the fuzzy disturbance observer has the advantage that it does not depend on model information. The convergence properties of the tracking error are analytically proven using Lyapunov’s theory. The obtained numerical results demonstrate the validity and the adaptive performance of the proposed control law in case the system is exposed to uncertainties and disturbances. Important remarks on the design process and performance benchmarks of the two observers are also demonstrated.

Study of designing regulator for temperature electrical resistance furnace using Kalman stochastic reconstructor

<span>Electric <span>resistance furnaces are the most popular and widely used industrial electro thermal equipment which continues to be the subject of many improvements. The aim of this paper is to control the temperature of electrical furnace for noisy thermocouple sensors. It can be assessed by observing some variables, which are very difficult to observe. Due to limitations, mainly the location of thermal sensors and their noises. In this case, the temperature measurement is trained with centered Gaussian white noise. The problem of accurate temperatures estimation for such sensors is solved using Kalman filter, which is an optimized estimator that provides a computationally efficient way to estimate system state. Thus, variables that are not directly measurable can be reconstructed from the algorithm. Kalman stochastic reconstructor (KSR). We cannot use with fixed parameters to control the temperature. For this reason, this paper comes up with a KSR approach based pole placement (PL) hybrid controller to realize an algorithm for the temperature control electrical furnace. Results based on Matlab simulation show that the improved algorithm has well produced an optimal estimate of the temperature. Evolving over time from noisy measurements. Hybrid algorithm KSR approach based PL give good performance compared to PL controllers.</span></span>

An efficient method for calculating system non-probabilistic reliability index

Collecting enough samples is difficult in real applications. Several interval-based non-probabilistic reliability methods have been reported. The key of these methods is to estimate system non-probabilistic reliability index. In this paper, a new method is proposed to calculate system non-probabilistic reliability index. Kriging model is used to replace time-consuming simulations, and the efficient global optimization is used to determine the new training samples. A refinement learning function is proposed to determine the best component (or performance function) during the iterative process. The proposed refinement learning function has considered two important factors: (1) the contributions of components to system nonprobabilistic reliability index, and (2) the accuracy of the Kriging model at current iteration. Two stopping criteria are given to terminate the algorithm. The system non-probabilistic index is finally calculated based on the Kriging model and Monte Carlo simulation. Two numerical examples are given to show the applicability of the proposed method.

Potential Effectiveness of Bicycle-Automatic Emergency Braking using the Washtenaw Area Transportation Study Data Set

In the United States, fatalities from vehicle–bicycle crashes have been increasing since 2010. A total of 857 cyclists were struck and killed in 2018 which is an increase from 623 fatalities in 2010. One promising countermeasure is Automatic Emergency Braking (AEB), which can help prevent and/or mitigate many vehicle–bicycle crashes. AEB is a vehicle-based system that can detect and mitigate an impending crash. The goal of this study was to elucidate U.S. vehicle–bicycle crashes and examine related factors to estimate AEB effectiveness. This study used a unique in-depth vehicle–bicycle crash study dataset collected under the collaboration of the Washtenaw Area Transportation Study (WATS) and the Toyota Collaborative Research Center conducted in southeast Michigan from 2011 to 2013. The WATS database provides in-depth investigations of vehicle–bicycle crashes in the United States. The characteristics of the WATS vehicle–bicycle crashes were validated against the Fatality Analysis Reporting System and the General Estimate System. The WATS database cases were examined to estimate the potential effectiveness of AEB to prevent or mitigate vehicle–bicycle collisions. In 60% of the WATS cases, cyclists were in the road for more than 1 s before impact. Assuming that a hypothetical AEB system requires a minimum of 1 s for detection and brake activation, these collisions would potentially be avoided or mitigated. However, for the remaining cases with less than 1 s of time to react (40% of cases), that AEB system would be challenged to avoid or mitigate the collision.

Fisher Information and Uncertainty Principle for Skew-Gaussian Random Variables

Fisher information is a measure to quantify information and estimate system-defining parameters. The scaling and uncertainty properties of this measure, linked with Shannon entropy, are useful to characterize signals through the Fisher–Shannon plane. In addition, several non-gaussian distributions have been exemplified, given that assuming gaussianity in evolving systems is unrealistic, and the derivation of distributions that addressed asymmetry and heavy–tails is more suitable. The latter has motivated studying Fisher information and the uncertainty principle for skew-gaussian random variables for this paper. We describe the skew-gaussian distribution effect on uncertainty principle, from which the Fisher information, the Shannon entropy power, and the Fisher divergence are derived. Results indicate that flexibility of skew-gaussian distribution with a shape parameter allows deriving explicit expressions of these measures and define a new Fisher–Shannon information plane. Performance of the proposed methodology is illustrated by numerical results and applications to condition factor time series.

Robust Physiological Metrics From Sparsely Sampled Networks

Physiological and biochemical networks are highly complex, involving thousands of nodes as well as a hierarchical structure. True network structure is also rarely known. This presents major challenges for applying classical network theory to these networks. However, complex systems generally share the property of having a diffuse or distributed signal. Accordingly, we should predict that system state can be robustly estimated with sparse sampling, and with limited knowledge of true network structure. In this review, we summarize recent findings from several methodologies to estimate system state via a limited sample of biomarkers, notably Mahalanobis distance, principal components analysis, and cluster analysis. While statistically simple, these methods allow novel characterizations of system state when applied judiciously. Broadly, system state can often be estimated even from random samples of biomarkers. Furthermore, appropriate methods can detect emergent underlying physiological structure from this sparse data. We propose that approaches such as these are a powerful tool to understand physiology, and could lead to a new understanding and mapping of the functional implications of biological variation.

Interaction Boundary Determination of Renewable Energy Sources to Estimate System Strength Using the Power Flow Tracing Strategy

System strength is an important concept in the integration of renewable energy sources (RESs). However, evaluating system strength is becoming more ambiguous due to the interaction of RESs. This paper proposes a novel scheme to define the actual interaction boundaries of RESs using the power flow tracing strategy. Based on the proposed method, the interaction boundaries of RESs were identified at the southwest side of Korea Electric Power Corporation (KEPCO) systems. The test results show that the proposed approach always provides the identical interaction boundaries of RESs in KEPCO systems, compared to the Electric Reliability Council of Texas (ERCOT) method. The consistent boundaries could be a guideline for power-system planners to assess more accurate system strength, considering the actual interactions of the RESs.

Compound Channel’s Cross-section Shape Effects on the Kinetic Energy and Momentum Correction Coefficients

Since accurate estimation of the flow kinetic energy (α) and momentum (β) is not easily possible in compound channels, determining their accurate correction coefficients is an important task. This paper has used the “flood channel facility (FCF)” data and the “conveyance estimate system (CES)” model (which is 1D, but considers a term related to the secondary flow) to study how the floodplain width and the main channel wall slope and asymmetry affect the values of α and β. Results have shown that their maximum values at the highest floodplain width are, respectively, 1.36 and 1.13 times of those at the lowest case; an increase in the slope increased their maximum values by 1.05 and 1.01 times, respectively. The mean of error values showed that the CES model estimated the values α and β more accurately than the flow discharge. The maximum differences between the estimated and experimental values were 12.14% for α and 4.3% for β; for the flow discharge, it was 24.4%.

Examining the phenomenon of rounding in analysts' EPS forecasts: evidence from Singapore

PurposePrior studies have documented the phenomenon of rounding of analysts' earnings per share (EPS) forecasts in the USA. From the outset, it is unclear if analysts following Singapore firms also similarly engage in the rounding of their EPS forecasts. This study aims to investigate the extent to which analysts engage in rounding of EPS forecasts of firms listed on the Singapore Exchange.Design/methodology/approachThe author conducted his analysis on a sample of analyst EPS forecasts of companies listed on the Singapore Stock Exchange, downloaded from the International Brokers Estimate System (I/B/E/S). This sample consists of 24,219 annual EPS forecasts announced from June 2011 to September 2019. These forecasts were made for 285 unique firms by 48 unique analysts.FindingsThe author finds that there is substantial rounding of EPS forecasts, with 9.59% of EPS forecasts examined ending in five- or ten-cent intervals. In supplementary analysis, the author further finds that the level of rounding was comparable across two periods under examination, from 2011 to 2015 and from 2016 to 2019. The author also finds that there was substantial rounding even for forecasts of relatively large magnitudes (i.e. US$1.00 and above).Originality/valueThis study is the first to examine the rounding of analysts' EPS forecasts of Singapore firms. It extends the literature on analyst EPS forecasts and highlights how the phenomenon of rounding of analyst EPS forecasts of US firms extends to Singapore.