Asymmetric Error Research Articles

Metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadings

The estimation of lateral strength in reinforced concrete (RC) columns subjected to cyclic loads is crucial in structural design. The failure of RC columns under lateral forces can lead to catastrophic structural collapses. This fact emphasizes the need for accurate assessments of their dynamic behavior. This paper proposes a data-driven model for estimating the lateral strength of RC columns. A historical dataset comprising 12 predictor variables and 247 samples has been compiled to train and validate of the proposed approach. The extreme gradient boosting machine (XGBoost) is employed to establish a predictive relationship between the lateral strength of RC columns and their influencing factors. Since model selection is critical for constructing a reliable prediction mode, this study relies on utilizing metaheuristic approaches, including Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony, and Ant Colony Optimization, to optimize the performance of the XGBoost model. Experimental results show that the integration of Ant Colony Optimization and XGBoost can help attain outstanding prediction accuracy with a correlation of determination (R2) of 0.95. Additionally, an asymmetric squared error loss function is utilized to reduce overestimations by 12 %. The newly developed method can be utilized in practical applications where reliable predictions of the lateral strength of RC columns under cyclic loads are required.

МОДУЛЬНЫЕ КОДЫ С СУММИРОВАНИЕМ С ПОСЛЕДОВАТЕЛЬНОСТЬЮ ВЕСОВЫХ КОЭФФИЦИЕНТОВ, ОБРАЗУЮЩЕЙ НАТУРАЛЬНЫЙ РЯД ЧИСЕЛ ЗА ИСКЛЮЧЕНИЕМ СТЕПЕНЕЙ ДВОЙКИ

The paper presents the results of studies on the characteristics of modular weight-based sum codes, where the sequence is formed by a natural series of numbers excluding powers of two. The consideration of the studied characteristics is advisable when developing discrete systems and their diagnostic support. Catalogs of the considered codes obtained using modules M = 4, 8, 16, 32, and 64 are provided. The choice of these specific modules is due to the fact that the number of check symbols in the codewords of the considered codes is small, which minimizes structural redundancy when constructing discrete systems and their diagnostic support, resulting in self-checking, controllable, and fault-tolerant structures. Modular weight-based sum codes detect all single errors with the number of data symbols . It is also demonstrated that the considered codes detect no fewer errors than the well-known modular sum codes of single digits (classical modular sum codes). As the module value increases, the gain in the tota number of errors detected by the modular weighted codes compared to classical modular codes significantly increases. Modular weighted codes are more effective at detecting multidirectional errors of even multiplicity in data vectors containing groups of distortions {0→1, 1→0} (symmetric errors) than classical modular sum codes. However, modular weight-based sum codes are less effective in handling asymmetric errors occurring in data vectors. In an experiment with test combinational circuits, it was shown that modular weighted codes with a module M = 4 do not detect more errors occurring at circuit outputs than classical modular codes with this module. Modular weight-based sum codes and a module M = 8 have slightly better characteristics. However, a general recommendation is to use modules that are powers of two starting from M = 16. Increasing the module allows for an increase in the number of detectable errors at the outputs of benchmarks, up to 100 % coverage. Modular weight-based sum codes, with a sequence of weight coefficients forming a natural series of numbers excluding powers of two, can be effectively used in the development of discrete systems and their diagnostic support.

Robust Matrix Completion with Heavy-Tailed Noise

This article studies noisy low-rank matrix completion in the presence of heavy-tailed and possibly asymmetric noise, where we aim to estimate an underlying low-rank matrix given a set of highly incomplete noisy entries. Though the matrix completion problem has attracted much attention in the past decade, there is still lack of theoretical understanding when the observations are contaminated by heavy-tailed noises. Prior theory falls short of explaining the empirical results and is unable to capture the optimal dependence of the estimation error on the noise level. In this article, we adopt an adaptive Huber loss to accommodate heavy-tailed noise, which is robust against large and possibly asymmetric errors when the parameter in the Huber loss function is carefully designed to balance the Huberization biases and robustness to outliers. Then, we propose an efficient nonconvex algorithm via a balanced low-rank Burer-Monteiro matrix factorization and gradient descent with robust spectral initialization. We prove that under merely a bounded second-moment condition on the error distributions, rather than the sub-Gaussian assumption, the Euclidean errors of the iterates generated by the proposed algorithm decrease geometrically fast until achieving a minimax-optimal statistical estimation error, which has the same order as that in the sub-Gaussian case. The key technique behind this significant advancement is a powerful leave-one-out analysis framework. The theoretical results are corroborated by our numerical studies. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Piecewise modified Prandtl-Ishlinskii model for precise nano-positioning

Piezoelectric ceramic, an indispensable micro-nano actuator, has advantages of small size, high sensitivity and strong controllability, which is widely applied in various fields like micro-nano processing, medicine, chemistry and materials. However, its nonlinear and asymmetric hysteresis features directly affect its positioning accuracy. The classical and generalized Prandtl-Ishlinskii models with linear symmetry operators are commonly used to deal with these issues, but they still face difficulties in asymmetric systems. Therefore, a piecewise modified Prandtl-Ishlinskii (PMPI) model is proposed to compensate nonlinear and asymmetric hysteresis in this paper. The new nonlinear operator and piecewise nonlinear envelope function are introduced into the PMPI model. Independent right and left parts are adopted to describe hysteresis increasing and decreasing stages, while the threshold and weight vectors correspond to them. Weight vectors are extended and a new diagonal matrix is constructed for avoiding excessive parameters and computation. Simulations and experiments are shown that the PMPI model has good compensation performance and effectiveness, which has better fitting, higher accuracy, fewer parameters and computation, and can be well applied to the precise nano-positioning systems with less nonlinear and asymmetric errors.

Finite time prescribed performance control for stochastic systems with asymmetric error constraint and actuator faults

This paper investigates the problem of finite time prescribed performance control (PPC) for a number of nonlinear stochastic systems with asymmetric error constraint, unknown control directions, and actuator faults. Firstly, instead of introducing the performance constraint function in the Lyapunov function, a new asymmetric error conversion function (AECF) is presented, which can successfully constrain the tracking errors into the specified asymmetric boundaries and eliminate the feasibility condition of requiring tracking errors to be bounded. Then, in iterative process, the unknown intermediate virtual controller is approximated by the fuzzy logic system (FLS), which makes each actual virtual controller to be reduced to only one item. Furthermore, the investigated finite time PPC strategy can fully compensate the faults impact on the systems and have only one adaptive parameter. In the end, the efficacy of investigated strategy is verified by inverted pendulum systems with stochastic disturbances.

Neural networks-based composite learning control for robotic systems with predefined time error constraints

This paper investigates the neural networks-based control problem for robotic systems with error constraints. By embedding a new predefined time performance function into an asymmetric barrier function, a predefined time constraints method is proposed with the following features: (1) it is a general approach that can handle asymmetric error constraints and can be directly applied to the predefined performance control for other high-order nonlinear systems without any changing; (2) both settling time and convergent compact set can be preassigned by the user; (3) the results are global i.e., the robot can start arbitrarily within the physical domain. By extending the regression operators with online data memory, a prediction error is constructed, by using which, a new neural networks based composite learning law is constructed to handle unknown dynamics. Compared with the traditional method such as gradient descent algorithm, σ-modification and ϵ-modification which cannot guarantee the convergence of the neural networks weights or can only force the weights to stay around preselected values, the neural networks guarantees that the weights converge to the region around the true values, in particular under a weak interval excitation (IE) condition rather than the typically stringent persistent excitation (PE) condition. The benefits and effectiveness of the proposed control are theoretically authenticated and experimentally validated.

A comprehensive approach to wind turbine power curve modeling: Addressing outliers and enhancing accuracy

This research presents a comprehensive approach to improving the accuracy of wind turbine power curve (WTPC) modeling. The WTPC is a critical tool for monitoring wind turbine performance and estimating wind power potential, but current models have limited ability to capture the complex relationship between wind speed and power output. To address these limitations, this study implements a dual-pronged refinement of WTPC modeling. First, an innovative data preprocessing technique is introduced, using a 97 % confidence interval around a KNN-estimated WTPC constructed using the Laplace distribution to meticulously eliminate prominent outliers. Second, a novel WTPC modeling approach based on quantile regression is adopted, accounting for the asymmetric error characteristics in the loss function. Four distinct quantile regression models are developed, including three tree-based algorithms - decision trees, random forests, and gradient boosting - and a deep learning-based quantile regression neural network. Comparative analysis against ten established parametric and nonparametric techniques confirms the superiority of the proposed models, with the decision tree quantile regression model achieving the lowest validation errors. The proposed techniques are validated on two real-world datasets from operational wind turbines in Turkey and China, demonstrating significant improvements in WTPC modeling accuracy compared to conventional methods. Overall, this study successfully presents a comprehensive modeling approach that addresses outliers and leverages quantile regression to significantly enhance WTPC accuracy.

ANALISIS DATA PANEL TRANSMISI HARGA BERAS DI PROVINSI LAMPUNG

The aims of this research are to examine the transmission of rice prices from the producer (miller) level to the consumer vertically, and to analyze the factors that influence determination of rice princes at the producer (miller) level in Lampung Province. The type of method applied in this research was descriptive research with quantitative approach. Data analysis was carried out using panel data from January 2020 to December 2022 fron 15 regencies/cities in Lampung Province. In addition, the data in this research was also analyzed by using Asymmetric Error Correction Model (AECM) and Ordinary Least Squares (OLS). The results obtained in this research indicate that a symmetrical transmission pattern of rice prices in the short term and an asymmetrical transmission pattern in the long term are occured in Lampung Province. Factors that influence the price of rice at the producer (miller) level in Lampung Province are the consumer price of rice, the price of imported rice, rice consumption, and the producer price of corn.Key words: economic valuation, travel cost method, visitor satisfaction

Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning.

Long waiting time in outpatient departments is a crucial factor in patient dissatisfaction. We aim to analytically interpret the waiting times predicted by machine learning models and provide patients with an explanation of the expected waiting time. Here, underestimating waiting times can cause patient dissatisfaction, so preventing this in predictive models is necessary. To address this issue, we propose a framework considering dissatisfaction for estimating the waiting time in an outpatient department. In our framework, we leverage asymmetric loss functions to ensure robustness against underestimation. We also propose a dissatisfaction-aware asymmetric error score (DAES) to determine an appropriate model by considering the trade-off between underestimation and accuracy. Finally, Shapley additive explanation (SHAP) is applied to interpret the relationship trained by the model, enabling decision makers to use this information for improving outpatient service operations. We apply our framework in the endocrinology metabolism department and neurosurgery department in one of the largest hospitals in South Korea. The use of asymmetric functions prevents underestimation in the model, and with the proposed DAES, we can strike a balance in selecting the best model. By using SHAP, we can analytically interpret the waiting time in outpatient service (e.g., the length of the queue affects the waiting time the most) and provide explanations about the expected waiting time to patients. The proposed framework aids in improving operations, considering practical application in hospitals for real-time patient notification and minimizing patient dissatisfaction. Given the significance of managing hospital operations from the perspective of patients, this work is expected to contribute to operations improvement in health service practices.

Identification of the Asymmetric Transmission Error and Gear Mesh Dynamic Parameters using Full-Spectrum Responses in a Geared-Rotor System

A dominant source of vibration in geared-rotor systems are the gear mesh fault parameters. They include the asymmetric transmission error, phases of transmission error, the gear mesh stiffness, the gear mesh damping and the gear runouts. The present work deals with the experimental identification of aforementioned parameters. A mathematical model of geared-rotor system has been developed using Lagrangian dynamics. Equations of motion are transformed into frequency domain using the full-spectrum response analysis. These transformed equations are used to develop an identification algorithm based on least-squares fit to estimate the transmission error and gear mesh dynamic parameters. The system identification algorithm is initially verified using numerical simulations. The robustness of the algorithm is checked by introducing white Gaussian noise in the simulated responses. A geared-rotor experimental rig was developed and used to measure responses at gear locations in two orthogonal directions. Measured responses are transformed in frequency domain using the full-spectrum analysis and used in the present novel identification algorithm to identify the gear parameters. The identified parameters are validated by comparing the numerically generated full-spectrum response using experimentally estimated parameters and that from the experimental rig. Conflict of Interest Statement The authors declare no conflicts of interest.

Asymmetric SVPWM and Error Suppression Method for Current Reconstruction of T-Type Three-Level Inverter

Asymmetric SVPWM and Error Suppression Method for Current Reconstruction of T-Type Three-Level Inverter

Predicting Compressive Strength of High-Performance Concrete Using Hybridization of Nature-Inspired Metaheuristic and Gradient Boosting Machine

This study proposes a novel integration of the Extreme Gradient Boosting Machine (XGBoost) and Differential Flower Pollination (DFP) for constructing an intelligent method to predict the compressive strength (CS) of high-performance concrete (HPC) mixes. The former is employed to generalize a mapping function between the mechanical property of concrete and its influencing factors. DFP, as a metaheuristic algorithm, is employed to optimize the learning phase of XGBoost and reach a fine balance between the two goals of model building: reducing the prediction error and maximizing the generalization capability. To construct the proposed method, a historical dataset consisting of 400 samples was collected from previous studies. The model’s performance is reliably assessed via multiple experiments and Wilcoxon signed-rank tests. The hybrid DFP-XGBoost is able to achieve good predictive outcomes with a root mean square error of 5.27, a mean absolute percentage error of 6.74%, and a coefficient of determination of 0.94. Additionally, quantile regression based on XGBoost is performed to construct interval predictions of the CS of HPC. Notably, an asymmetric error loss is used to diminish overestimations committed by the model. It was found that this loss function successfully reduced the percentage of overestimated CS values from 47.1% to 27.5%. Hence, DFP-XGBoost can be a promising approach for accurately and reliably estimating the CS of untested HPC mixes.

Resilient data‐driven asymmetric bipartite consensus for nonlinear multi‐agent systems against DoS attacks

AbstractIn this article, we study an unified resilient asymmetric bipartite consensus (URABC) problem for nonlinear multi‐agent systems with both cooperative and antagonistic interactions under denial‐of‐service (DoS) attacks. We first prove that the URABC problem is solved by stabilizing the neighborhood asymmetric bipartite consensus error. Then, we develop a distributed compact form dynamic linearization method to linearize the neighborhood asymmetric bipartite consensus error. By using an attack compensation mechanism to eliminate the adverse effects of DoS attacks and an extended discrete state observer to enhance the robustness against unknown dynamics, we finally propose a distributed resilient data‐driven adaptive control (DDAC) algorithm to solve the URABC problem. A numerical example validates the proposed results.

Monetary policy, threshold of profitability and dynamics of private investment in the West African Economic and Monetary Union

This study investigates the interactions between optimal monetary policy, investment profitability thresholds, and private sector investment dynamics within the West African Economic and Monetary Union. Analyzing data from 2000 to 2021, it employs advanced econometric models (linear, asymmetric, and threshold effect dynamic panel error correction models) to provide a nuanced understanding of the investment environment in the union. The findings reveal the confluence of low returns and high borrowing costs for private investment. Notably, this study suggests that simply pursuing an expansionary monetary policy does not offer a sufficient solution. Instead, it proposes a more accommodative stance by the Central Bank, featuring lower policy rates, to alleviate the negative impact of high credit costs on private investment and ultimately stimulate economic activity.

Toward Patient-Centered Drug Approval for Treatment of Rare Diseases

ObjectivesThis commentary seeks to improve the design and analysis of trials undertaken to obtain approval of drugs for treatment of rare diseases. MethodsMethodological analysis reveals that use of hypothesis testing in the Food and Drug Administration drug approval process is harmful. Conventional asymmetric error probabilities bias the approval process against approval of new drugs. Hypothesis testing is inattentive to the relative magnitudes of losses to patient welfare when types 1 and 2 errors occur. Requiring the sample size to be large enough to guarantee the specified statistical power particularly inhibits the development of new drugs for treating rare diseases. Rarity of a disease makes it difficult to enroll the number of trial subjects needed to meet the statistical power standards for drug approval. ResultsUse of statistical decision theory in drug approval would overcome these serious deficiencies of hypothesis testing. Sample size would remain relevant to drug approval, but the criterion used to evaluate sample size would change. Rather than judging sample size by statistical power, the Food and Drug Administration could require a sample to be large enough to provide a specified nearness to optimality of the approval decision. ConclusionsUsing nearness to optimality to set sample size and making approval decisions to minimize distance from optimality would particularly benefit the evaluation of drugs for treatment of rare diseases. It would enable a dramatic reduction in sample size relative to current norms, without compromising the clinical informativeness of trials.

Variations of Interstellar Gas-to-dust Ratios at High Galactic Latitudes

Interstellar dust at high Galactic latitudes can influence astronomical foreground subtraction, produce diffuse scattered light, and soften the UV spectra of quasars. In a sample of 94 sight lines toward quasars at high latitude and low extinction, we evaluate the interstellar “gas-to-dust ratio” N H/E(B − V), using hydrogen column densities (H i and H2) and far-IR (FIR) estimates of dust reddening. In the Galactic plane, this ratio is 6.0 ± 0.2 (in units of 1021 cm−2 mag−1). On average, recent Planck estimates of E(B − V) in low-reddening sight lines are 12% higher than those from Schlafly & Finkbeiner, and N H I exhibits significant variations when measured at different radio telescopes. In a sample of 51 quasars with measurements of both H i and H2 and 0.01 ≤ E(B − V) ≲ 0.1, we find mean ratios 10.3 ± 0.4 (gas at all velocities) and 9.2 ± 0.3 (low-velocity only) using Planck E(B − V) data. High-latitude H2 fractions are generally small (2%–3% on average), although nine of 39 sight lines at ∣b∣ ≥ 40° have f H2 of 1%–17%. Because FIR-inferred E(B − V) is sensitive to modeled dust temperature T d and emissivity index β, gas-to-dust ratios have large, asymmetric errors at low E(B − V). The ratios are elevated in sight lines with high-velocity clouds, which contribute N H but little reddening. In Complex C, the ratio decreases by 40% when high-velocity gas is excluded. Decreases in dust content are expected in low-metallicity gas above the Galactic plane, resulting from grain destruction in shocks, settling to the disk, and thermal sputtering in hot halo gas.

High-performance repetition cat code using fast noisy operations

Bosonic cat qubits stabilized by two-photon driven dissipation benefit from exponential suppression of bit-flip errors and an extensive set of gates preserving this protection. These properties make them promising building blocks of a hardware-efficient and fault-tolerant quantum processor. In this paper, we propose a performance optimization of the repetition cat code architecture using fast but noisy CNOT gates for stabilizer measurements. This optimization leads to high thresholds for the physical figure of merit, given as the ratio between intrinsic single-photon loss rate of the bosonic mode and the engineered two-photon loss rate, as well as an improved scaling below threshold of the required overhead, to reach an expected level of logical error rate. Relying on the specific error models for cat qubit operations, this optimization exploits fast parity measurements, using accelerated low-fidelity CNOT gates, combined with fast ancilla parity-check qubits. The significant enhancement in the performance is explained by: 1- the highly asymmetric error model of cat qubit CNOT gates with a major component on control (ancilla) qubits, and 2- the robustness of the repetition cat code error correction performance in presence of the leakage induced by fast operations. In order to demonstrate these performances, we develop a method to sample the repetition code under circuit-level noise that also takes into account cat qubit state leakage.

Banking regulation and bank credit delivery in selected Sub-Saharan African countries: Symmetric and asymmetric causal linkages

This study aimed to empirically determine the symmetric and asymmetric causal relationship between banking regulation and bank credit delivery using a context of 23 sub-Saharan African countries from 1995 to 2017. The estimated symmetric and asymmetric error correction-based multivariate panel Granger-causality models allowed for both long-run and short-run causal relationships. The results generally supported the existence of long-run symmetric and asymmetric causality between banking regulatory measures, bank credit delivery and two intermittent variables, namely, economic growth and inflation. In the short run, bidirectional symmetric causality existed between banking entry barriers and bank credit delivery, as well as a one-way symmetric causal flow running from bank credit delivery to banking capital regulations. Moreover, positive shocks to bank credit delivery Granger caused negative shocks to banking entry barriers, activity restrictions or capital regulations, while negative shocks to mixing of banking and commerce restrictions Granger caused positive or negative shocks to bank credit delivery. The findings highlight that policymakers should take into consideration not only the symmetric but also the asymmetric causal effects of reforms aimed at enhancing banking regulation and such reforms should be well-targeted and well-designed for them to stimulate bank credit delivery.

International Oil Price and Domestic Refinery Price: Empirical Evidence for Greece

Fuel prices and the pronounced fluctuations in the international oil market are among the most widely discussed topics worldwide. Recent international developments, including energy crises, have resulted in a substantial surge in global oil prices, leading to price increases across all countries and in all petroleum products. This paper investigates the correlation between international oil prices and refining costs in Greece. More specifically, this study focuses on how rapidly refinery prices respond to potential increases or decreases in the international oil price, commonly known as the ’Rockets and Feathers’ phenomenon. The investigation assesses the reaction of diesel and gasoline refinery prices to fluctuations in the international oil price and explores the potential presence of asymmetry. The econometric model employed is based on an asymmetric error correction model, utilizing weekly data spanning from 2013 to 2022 for Greece. According to our findings, both diesel and 95-octane gasoline prices exhibit an asymmetric response to international oil prices. Notably, refinery prices react more promptly to price increases than to decreases in the global oil price. This study holds particular significance, as this delay is reflected in final retail prices, indicating the possible existence of pricing asymmetries. Additionally, the issue of oil purchase contracts arises, which guarantee a fixed price regardless of the prevailing international oil price at the time and, consequently, do not justify such fluctuations in refinery prices. These findings could prove valuable for competition policy within Greece’s vertical oil market.

Quantum Cost Optimization Algorithm for Entanglement-based Asymmetric Quantum Error Correction

Quantum Cost Optimization Algorithm for Entanglement-based Asymmetric Quantum Error Correction