Estimates In Order Research Articles

Performance Comparison of Different Approaches for State Estimation Using RTU and Phasor Measurements

This paper compares various approaches to implement state estimation with conventional (RTU) measurements and phasor measurements. This study implements two-stage Hybrid State Estimation (HSE) in order to merge RTU and PMU measurements. Non-overlapping approach for area decomposition is applied in this paper. Centralized merging of PMU measurements in the process of HSE is presented to merge conventional measurements and phasor measurements effectively in a distributed network. Further, this study applies linear state estimation in a distributed network in case of full observability with PMU. Performance of state estimator in overall power system network and distributed network is compared for all approaches in terms of mean square error of estimated states and execution time of SE process. The effectiveness of the proposed approach is demonstrated with IEEE 14 bus system and IEEE 118 bus system.

A New Approach to the Threshold Autoregressive Models

Abstract Time series analyzing is very important tool for economic and financial system. However, recent developments show that financial systems are known in a structural change. Therefore, nonlinear time series have been analyzed for past decades because of these changes. In this paper, we consider Threshold Autoregressive (TAR) model. The most popular method for estimating the parameters and threshold value is least square (LS) method. However, LS method is not robust to the outliers and departures from normality. Therefore, we propose a robust version of estimation in order to provide robust results. JEL classification numbers: C01, C22, C13. Keywords: Threshold Autoregressive Model, Iterated Weighted Least Square, Skew Normal, Long Tailed Symmetric Distribution, Robustness.

Sensor Selection for Maneuvering Target Tracking in Wireless Sensor Networks With Uncertainty

Maneuvering target tracking in wireless sensor networks (WSNs) has been a prominent research topic over the past twenty years. In many situations, WSNs are vulnerable to natural or man-made interference, resulting in uncertainty problems. In this paper, we propose an information-quality-based sensor selection method with estimation feedback for maneuvering target tracking in uncertain WSNs. First, based on the Generalized Pseudo-Bayesian estimator of first order (GPB1) and the unscented information filter (UIF), a GPB1-UIF algorithm with estimation feedback is presented. Inspired by the theory of information quality, an information-quality metric framework is proposed for sensor selection in uncertain WSNs. And the information-theoretic selection metrics are redesigned based on consistency measuring operator. Moreover, without knowing the number of selected sensors a priori, a multi-objective optimization problem is considered to determine the final selection strategy. Simulation results demonstrate that the proposed approach outperform the previous sensor selection methods.

Robust Post-Hoc Multiple Comparisons: Skew t Distributed Error Terms

The pairwise comparisons or post-hoc methods are used for determining the source of the difference of group means in one-way ANOVA. These methods are mostly depend on normality assumption. However, nonnormal distributions are more prevalent than normal distribution. Therefore, robust estimation methods become very important tools in statistical analysis. In this paper, we assume that the distribution of the error terms is Azzalini's skew $t$ and obtain the robust estimators in order to make post-hoc tests in one-way ANOVA. We use maximum likelihood (ML) methodology and compare this methodology with some of robust estimators like M estimator, Wave estimator, trimmed mean and modified maximum likelihood (MML) methodology with Monte Carlo simulation study. Simulation results show that the proposed methodology is more preferable. We also compare power values of the test statistics and conclude that the test statistics based on the ML estimators are more powerful than the test statistics based on other methods.

Do the components of government expenditure affect the long-run dynamics of private investments? A heterogeneous panel analysis with a multifactor error structure

ABSTRACT Using data on 30 OECD countries over 1993–2019, this paper examines the ties between the components of government expenditure and the supply side of the private sector. We employ an ECM version of the novel dynamic common correlated effects estimator (DCCE) in order to account for nonstationarity, country-heterogeneity, and cross-sectional dependence. The estimates suggest that allocating public resources toward general public services and health positively affects the long-run dynamics of private investments. Conversely, rises in the expenditure on housing and social protection are associated with significant crowding-out effects.

Uncertainty quantification of phase transition quantities from cluster weighting calculations

In this work, we investigate how uncertainties in experimental input data influence the results of quantum cluster equilibrium calculations. In particular, we focus on the calculation of vaporization enthalpies and entropies of seven organic liquids, compare two computational approaches for their calculation, and investigate how these properties are affected by changes in the experimental input data. It is observed that the vaporization enthalpies and entropies show a smooth dependence on changes in the reference density and boiling point. The reference density is found to have only a small influence on the vaporization thermodynamics, whereas the boiling point has a large influence on the vaporization enthalpy but only a small influence on the vaporization entropy. Furthermore, we employed the Gauss--Hermite estimator in order to quantify the uncertainty in thermodynamic functions that stems from inaccuracies in the experimental reference data for the example of the vaporization enthalpy of (R)-butan-2-ol. We quantify the uncertainty as 30.95 · 10-3 kJ mol-1. In addition, we compare the convergence behavior and computational effort of the Gauss-Hermite estimator with the Monte Carlo approach and show the superiority of the former. Using this study, we present how uncertainty quantification can be applied to examples from theoretical chemistry.

Setting life cycle assessment (LCA) in a future-oriented context: the combination of qualitative scenarios and LCA in the agri-food sector

By combining qualitative scenarios and life cycle assessment (LCA), we place the latter in a larger context. This study outlines the importance of the integration of future perspectives into LCA, and also the significance of taking changes in the environment of technology into account, rather than just technological development itself. Accordingly, we focused on adapting the background system of an attributional LCA in the agri-food sector. The proposed technology was assumed not have evolved in the considered time horizon. In this context, the objectives of this paper were twofold: (i) to methodologically prove the applicability of integrating qualitative scenarios into LCA and (ii) to focus on changes in the background system, which is sometimes overlooked in the context of future-oriented LCA. This allowed to evaluate the future potential of different technologies, assessing their environmental impact under uncertain future developments. Methodologically, the qualitative information from scenarios was transformed into quantitative data, which was successively fed into the life cycle inventory (LCI) of the LCA approach. This point of integration into the second phase of LCA translates into future changes in the entire environment in which a technology is used. This means that qualitatively described scenario narratives need to be converted into value estimates in order to be incorporated into the LCA model. A key conclusion is that changes in the background of an LCA—the changing framework expressed through the inventory database—can be very important for the environmental impact of emerging technologies. This approach was applied to a food processing technology to produce apple juice. The proposed methodology enables technology developers to make their products future-proof and robust against socioeconomic development. In addition, the market perspective, if spelled out in the scenarios, can be integrated, leading to a more holistic picture of LCA with its environmental focus, while simultaneously empowering actors to make the right strategic decisions today, especially when considering the long investment cycles in the agri-food sector.

NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis.

In many scientific fields including neuroscience, climatology or physics, complex relationships can be described most parsimoniously by non-linear mechanics. Despite their relevance, many neuroscientists still apply linear estimates in order to evaluate complex interactions. This is partially due to the lack of a comprehensive compilation of non-linear methods. Available packages mostly specialize in only one aspect of non-linear time-series analysis and most often require some coding proficiency to use. Here, we introduce NoLiTiA, a free open-source MATLAB toolbox for non-linear time series analysis. In comparison to other currently available non-linear packages, NoLiTiA offers (1) an implementation of a broad range of classic and recently developed methods, (2) an implementation of newly proposed spatially and time-resolved recurrence amplitude analysis and (3) an intuitive environment accessible even to users with little coding experience due to a graphical user interface and batch-editor. The core methodology derives from three distinct fields of complex systems theory, including dynamical systems theory, recurrence quantification analysis and information theory. Besides established methodology including estimation of dynamic invariants like Lyapunov exponents and entropy-based measures, such as active information storage, we include recent developments of quantifying time-resolved aperiodic oscillations. In general, the toolbox will make non-linear methods accessible to the broad neuroscientific community engaged in time series processing.

Economic policy uncertainty and stock returns among OPEC members: evidence from feasible quasi-generalized least squares

We examine the predictive ability of economic policy uncertainty on stock returns of selected OPEC countries. In order to deal with certain statistical properties of the predictors, which include serial correlation, persistence, conditional heteroskedasticity, and endogeneity effects, wse utilize the Feasible Quasi-Generalized Least Squares (FQGLS) estimator in order to obtain accurate forecast estimates. As a precondition for forecast analysis, we conduct the predictability test, which shows that economic policy uncertainty is significant only for five countries, namely Kuwait, Nigeria, Saudi Arabia, United Arab Emirates, and Venezuela. Hence, we proceed with the main forecast analysis for only this set of countries. Our results are twofold. We first account for asymmetries in forecasting stock returns by comparing the forecast performance of the symmetric economic policy uncertainty-based predictive model with its asymmetric variant. On the other hand, we compare the performance of the best model from above with the standard ARFIMA model using an alternative forecast test. In both cases, we find that the asymmetric model yields the most accurate forecast returns for stock returns of the five countries. In essence, neglecting the role of asymmetries in forecasting stock returns can lead to bias results. Our findings are not only robust to different sample sizes (i.e., 50%, and 75%) and different forecast horizons (4, 8, and 12 months) but have important policy implications for policymakers and potential investors.

Boundedness and concentration of random singular integrals defined by wavelet summability kernels

We use Cramér-Chernoff type estimates in order to study the Calderón-Zygmund structure of the kernels ∑I∈DaI(ω)ψI(x)ψI(y), and their concentration about the mean, where aI are subgaussian independent random variables and {ψI:I∈D} is a wavelet basis where D are the dyadic intervals in R. We consider both, the cases of standard smooth wavelets and the case of the Haar wavelet.

A Bus-Scheduling Method Based on Multi-Sensor Data Fusion and Payment Authenticity Verification

It is of great significance to ensure public transportation management capabilities by improving urban public transport services. One method is to solve the problems related to the quality of data submitted for public funding as well as the accuracy and transparency of the supervision and review processes; moreover, improving public-transportation-service systems is a viable method to solve such problems. With technological advancements and the application of new technologies such as automatic driving and multiple payment, it has gradually become difficult for user-data verification systems, based on the original single bus payment method, to cater to these new technologies. Diversified payment and complex management methods have highlighted the need for new verification methods. Firstly, in this paper, we constructed the Origin–Destination (OD) model of bus-passenger flows by using real-time transmission of passenger-multiple-payment data, on-board-video passenger flow detection data and vehicle real-time positioning data. On this basis, the bus waybill data of other intelligent bus systems and the wait data of bus stations were integrated, so as to establish the travel chain theory by matching passenger flow and the temporal and spatial distribution model. Then, an OD analysis of public-transport passenger flows could be carried out, with a detailed analysis of vehicle, station and line-passenger flow, and the travel characteristics of public transport passenger flow could be excavated. Then, according to the means-end chain theory, the spatiotemporal distribution of the passenger flow data was obtained to carry out an OD analysis of the passenger flow, so as to perform a refinement analysis of the vehicle, station, and passenger flow. Thereby, the characteristics of the passenger flow were explored. Subsequently, payment-authenticity-verification models were established for the data-validity assessment, video-data analysis, passenger-flow estimation, and early warnings in order to determine the authenticity of the payment data. Lastly, based on the multi-sensor passenger flow data fusion and the data authenticity verification models, combined with the application of new technologies such as the use of autonomous buses, the test was promoted. That is, by taking intelligent bus scheduling as the scenario, the research method was tested and verified with real-time passenger flow data according to historical data. The results showed that the method accurately predicted the passenger flow, and had a positive role in improving the efficiency of payment-data-authenticity verification. The application of the method can enhance the management and service quality of public transportation.

A Robust and Resilient State Estimation for Linear Systems

This article is concerned with the state estimation of linear dynamic systems when some sensors are corrupted by attackers. This problem is known as resilient state estimation (RSE), and aims to achieve, under some conditions, the estimation of the true state despite the malicious attacks on sensors. The state-of-art RSE methods provides a bound on estimation errors when external disturbance exists. However, it is shown in this article that the effect of the disturbance on estimation error may be larger than that for conventional observers, or even worse, resiliency may be lost for the disturbance that exceeds the bound. To resolve this issue, unknown input observer (UIO) mechanism is adopted in RSE for the purpose of estimating true plant state under both sensor attacks and external disturbance. Also achieved in this work is the method of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partial</i> state UIO synthesis, which relaxes the design requirement for full state UIO. In relation to resiliency, it is shown that 2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> redundant detectability is a necessary condition for robust and resilient state estimator in order to tolerate up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> sensor attacks. Numerical examples are given to validate the effectiveness of the proposed method.

A new class of self‐normalising LMS algorithms

Many researchers and practitioners make heavy use of the least mean squares (LMS) algorithm as an efficient adaptive filter suitable for a multitude of problems. Despite being versatile and efficient, a drawback of this algorithm is that the adaptation rate, i.e. step-size, has to be chosen very carefully in order to get the desired result (optimum compromise between fast adaptation and low steady state error). This choice was simplified by the invention of the normalised LMS, which bounds the step-size and guarantees convergence. However, the optimum choice of the normalisation becomes non-trivial if the system to be approximated is part of a bigger, non-trivial model, e.g. cascaded filters or linear paths followed by nonlinearities. Such cases usually require approximations or worst-case estimates in order to yield a normalised update algorithm, which might result in sub-optimal performance. To counteract this problem, a new class of LMS algorithms which automatically choose their own normalisation terms, the so-called self normalising LMS, is introduced. The simulations show that this new algorithm not only outperforms state-of-the-art solutions in terms of steady state performance in a cascaded filter scenario but also converges just as fast as all other considered algorithms.

Predicting hydraulic jump characteristics in a gradually expanding stilling basin with roughness elements by Sugeno Fuzzy Logic

Abstract The hydraulic behavior of a hydraulic jump can be challenging to estimate in order to design gradually expanding stilling basins with roughness elements on the bed. In this study, five dependent variables were identified that comprise: (i) the sequent depth ratio; (ii) the relative length of the jump; (iii) the relative roller length of the jump; (iv) the relative energy dissipation; and (v) the water surface profile. This study undertook a set of formulations based on the regression analysis and Sugeno Fuzzy Logic (SFL) to predict these variables based upon experimental data. Results demonstrate that the trained SFLs predicted the behavior of the dependent variables with the Nash–Sutcliffe Coefficient (NSC) greater than 0.96 in the testing phase. In contrast, the NSC values for the regression models are greater than 0.79. The higher accuracy of SFL is attributed to its capability in managing uncertainty and imprecise data owing to water surface profile oscillations of the hydraulic jump. Also, the results indicate that the prediction residuals for SFL are homoscedastic for all hydraulic parameters investigated except for the water surface profile, the prediction residuals of which for the regression equations are heteroscedastic.

Well-posedness and long term behavior of supercritical wave equations driven by nonlinear colored noise on [formula omitted

This paper is concerned with the well-posedness and long term behavior of the non-autonomous random wave equations driven by nonlinear colored noise on Rn with n≤5. The drift nonlinearity has a supercritical growth exponent (n+2)/(n−2). We first prove the existence and uniqueness of solutions in the energy space by showing the non-concentration of energy via the Morawetz identity and the uniform Strichartz estimates. We then prove the existence and uniqueness of tempered pullback random attractors of the non-autonomous random dynamical system associated with the equation. The asymptotic compactness of solutions is obtained by the idea of energy equation due to Ball and the uniform tail-ends estimates in order to circumvent the difficulty caused by the lack of compactness of Sobolev embeddings on unbounded domains.

Autonomous Aerial Robot for High-Speed Search and Intercept Applications

In recent years, high-speed navigation and environment interaction in the context of aerial robotics has become a field of interest for several academic and industrial research studies. In particular, Search and Intercept (SaI) applications for aerial robots pose a compelling research area due to their potential usability in several environments. Nevertheless, SaI tasks involve a challenging development regarding sensory weight, onboard computation resources, actuation design, and algorithms for perception and control, among others. In this work, a fully autonomous aerial robot for high-speed object grasping has been proposed. As an additional subtask, our system is able to autonomously pierce balloons located in poles close to the surface. Our first contribution is the design of the aerial robot at an actuation and sensory level consisting of a novel gripper design with additional sensors enabling the robot to grasp objects at high speeds. The second contribution is a complete software framework consisting of perception, state estimation, motion planning, motion control, and mission control in order to rapidly and robustly perform the autonomous grasping mission. Our approach has been validated in a challenging international competition and has shown outstanding results, being able to autonomously search, follow, and grasp a moving object at 6 m/s in an outdoor environment.

Smoothing Linear Multi-Target Tracking Using Integrated Track Splitting Filter

Multi-target tracking (MTT) is a challenging issue due to an unknown number of real targets, motion uncertainties, and coalescence behavior of sensor (such as radar) measurements. The conventional MTT systems deal with intractable computational complexities because they enumerate all feasible joint measurement-to-track association hypotheses and recursively calculate the a posteriori probabilities of each of these joint hypotheses. Therefore, the state-of-art MTT system demands bypassing the entire joint data association procedure. This research work utilizes linear multi-target (LM) tracking to treat feasible target detections followed by neighbored tracks as clutters. The LM integrated track splitting (LMITS) algorithm was developed without a smoothing application that produces substantial estimation errors. Smoothing refines the state estimation in order to reduce estimation errors for an efficient MTT. Therefore, we propose a novel Fixed Interval Smoothing LMITS (FIsLMITS) algorithm in the existing LMITS algorithm framework to improve MTT performance. This algorithm initializes forward and backward tracks employing LMITS separately using measurements collected from the sensor in each scan. The forward track recursion starts after the smoothing. Therefore, each forward track acquires backward multi-tracks that arrived from upcoming scans (future scans) while simultaneously associating them in a forward track for fusion and smoothing. Thus, forward tracks become more reliable for multi-target state estimation in difficult cluttered environments. Monte Carlo simulations are carried out to demonstrate FIsLMITS with improved state estimation accuracy and false track discrimination (FTD) in comparison to the existing MTT algorithms.

Amending the heston stochastic volatility model to forecast local motor vehicle crash rates: A case study of Washington, D.C.

Modelling crash rates in an urban area requires a swathe of data regarding historical and prevailing traffic volumes and crash events and characteristics. Provided that the traffic volume of urban networks is largely defined by typical work and school commute patterns, crash rates can be determined with a reasonable degree of accuracy. However, this process becomes more complicated for an area that is frequently subject to peaks and troughs in traffic volume and crash events owing to exogenous events – for example, extreme weather – rather than typical commute patterns. One such area that is particularly exposed to exogenous events is Washington, D.C., which has seen a large rise in crash events between 2009 and 2020. In this study, we adopt a forecasting model that embeds heterogeneity and temporal instability in its estimates in order to improve upon forecasting models currently used in transportation and road safety research. Specifically, we introduce a stochastic volatility model that aims to capture the nuances associated with crash rates in Washington, D.C. We determine that this model can outperform conventional forecasting models, but it does not perform well in light of the unique travel patterns exhibited throughout the COVID-19 pandemic. Nevertheless, its adaptability to the idiosyncrasies of Washington, D.C. crash rates demonstrates its ability to accurately simulate localised crash rates processes, which can be further adapted in public policy contexts to form road safety targets.

The prediction of product return rates with ensemble machine learning algorithms

There may not always be actual data available for planning. Predicted data are used especially for future planning. Due to errors in such planning based on prediction, many products enter the reverse logistics network without completing the shelf life. Especially in textile sector, because of fashion, it is the most important point of planning to be able to make accurate estimates in order to avoid unnecessary resource utilization and to provide minimum cost. It is difficult to establish a mathematical model because the prediction problems in real life have multivariate structure and unknown parameters. Most of the studies in literature have been based on time series prediction. But due to changing fashion and demands of consumers, there are significant differences between demand forecasts and real data. So, in the problems with unknown parameters and multivariate structure, Ensemble Machine Learning (EML) methods are preferred recently because they give more accurate results than other prediction methods. Unlike other studies, the product return rate in textile sector has been predicted with the Stacking and Vote algorithms from EML methods in this paper. In this direction, it is aimed to concentrate on the returns of the products sold with the preferences of the customers and to predict the returns more accurately. In this way, the consumer information obtained as a result of the analyzes can provide more accurate planning in avoiding unnecessary production, transportation and storage activities, reducing costs, resource utilization and environmental pollution. In addition, it is one of the main aims of the study to contribute to the literature by determining the parameters that can be used in predicting the return rates. Highest performance results were obtained with Stacking algorithm. The obtained results were given comparatively and the correlation coefficient of 86.07% was reached.

CAPITAL STRUCTURE AND FINANCIAL CRISIS: EVIDENCE USING GMM ESTIMATION

The Financial crisis (FC) displayed a crucial impact on the financial markets, incredibly decreasing security issuance by companies. A standout amongst the outcome of the interruption of the capital furthermore lending markets created by financial crisis might have been enhancing the level of debt in firm capital structures. Therefore, this paper uses Dynamic Panel Data (GMM) Estimator in order to investigate the impact financial crisis on capital structure over a sample of 15 cement firms that are recorded on Istanbul Stock Exchange from 2005 to 2015. Financial debt is utilized as evaluation of dependent variables (DV) while, Size, Growth, Tangibility and Profitability are used in assessment of independent variables (IV). The results indicate that there is connection between firm’s capital structure and financial crisis. Furthermore; the results express that firm size, tan, growth, NDTS are positive associated with leverage while profitability and leverage are negatively related.