Abstract

The variable nature of wind, including wind speed, direction, barometric pressure, and air temperature, presents significant challenges for accurately predicting wind power output. This paper addresses the issue of contextual prediction accuracy, ...

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Motivated by a sequential choice of foreign production strategies (CFPSs) in an actual decision for entering the lesser-known foreign markets, this article proposes two online models to analyze an optimal online strategy where the objective is to minimize ...

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In this study, we aim to comprehensively explore the application of principal component analysis (PCA) and independent component analysis (ICA), considering their practical utility. We compare these two methods theoretically and practically, using both ...

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The nonnegative representation-based classification only imposes an overall nonnegative constraint on the representation coefficients but fails to apply differentiated penalties on the coefficients of different classes, which inevitably limits its ...

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The nonlinear partial differential equations considered in this study are important in modeling complex wave phenomena extending from hydrodynamics via ocean engineering to plasma physics, fluid dynamics, and other mediums. The Rosenau equation provides ...

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Effective proportional–integral–derivative controller tuning is critical for attaining high-performance operation in direct current motor systems, especially when traditional approaches fail under nonlinear or uncertain dynamics. This study conducted a ...DC motors power many everyday systems, from robots to electric vehicles. To control them effectively, engineers use PID controllers, but tuning these controllers can be difficult. This study compared seven computer algorithms ...

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The nonnegative representation-based classification (NRC) method has attracted increasing attention in the field of face recognition. Building upon collaborative representation (CR), NRC incorporates a nonnegative constraint on the representation ...

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Accurate time-series classification (TSC) remains a fundamental challenge in deep learning due to the complexity and variability of temporal patterns. While recurrent neural networks (RNNs) such as LSTM and GRU have shown promise in modeling sequential ...

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Using topological summary tools such as persistence landscapes have greatly enhanced the practical usage of topological data analysis to analyze large-scale, noisy, and complex datasets. A central element of persistence landscape usage involves computing ...

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This study adopts the quantile regression method to analyze the influencing factors of low-income, middle-income, and high-income groups at the national, urban, and rural levels in China, respectively, at the quantiles of 0.05, 0.20, 0.50, 0.80, and 0.95. ...