Search

To improve the estimation accuracy of spectral reflectance from the given trichromatic value, a new two-step k-nearest neighbors locally weighted linear regression method is proposed. The algorithm has good local learning ability and can take into account the similarity of colorimetric and spectral reflectance space. The simulated and practical imaging experiments were carried out with Munsell matte and glossy dataset, respectively. Experimental results show that the mean root mean square error values of the spectral reflectance estimated by our model in simulated RGB, practical imaging Adobe RGB. and raw RGB data experiments are 0.00731, 0.01519, and 0.01453, respectively, and the mean color difference values under CIE standard illuminant D65 are 0.380, 1.311, and 1.180, respectively. In addition, we showed the calculation time cost of various models in the practical experiment. The calculation time of one sample for the proposed method is 0.094 s. The proposed method is better than several state-of-the-art methods in terms of comprehensive estimation performance and running efficiency. [ABSTRACT FROM AUTHOR]

To achieve the goal of carbon neutrality, the demand for energy saving by the residential sector has witnessed a soaring increase. As a promising paradigm to monitor and manage residential loads, the existing studies on non-intrusive load monitoring (NILM) either lack the scalability of real-world cases or pay unaffordable attention to identification accuracy. This paper proposes a high accuracy, ultra-sparse sample, and real-time computation based NILM method for residential appliances. The method includes three steps: event detection, feature extraction and load identification. A wavelet decomposition based standard deviation multiple (WDSDM) is first proposed to empower event detection of appliances with complex starting processes. The results indicate a false detection rate of only one out of sixteen samples and a time consumption of only 0.77 s. In addition, an essential feature for NILM is introduced, namely the overshoot multiple (which facilitates an average identification improvement from 82.1% to 100% for similar appliances). Moreover, the combination of modified weighted K-nearest neighbors (KNN) and overshoot multiples achieves 100% appliance identification accuracy under a sampling frequency of 6.25 kHz with only one training sample. The proposed method sheds light on highly efficient, user friendly, scalable, and real-world implementable energy management systems in the expectable future. [ABSTRACT FROM AUTHOR]

Accurate capacity estimation is essential in the management of lithium-ion batteries, as it guarantees the safety and dependability of battery-powered systems. However, direct measurement of battery capacity is challenging due to the unpredictable working conditions and intricate electrochemical characteristics, which complicates the identification of battery degradation. In this work, through in-depth analysis of battery aging data, an incremental slope (IS) aided feature extraction method is proposed to obtain universal multidimensional features that adapt to different working conditions. With the extracted features, a simple multilayer perceptron (MLP) is used to achieve high-precision capacity estimation. Furthermore, a feature matching based transfer learning (FM-TL) method is proposed to automatically adapt the capacity estimation across different types of batteries that are cycled under various working conditions. 158 batteries covering five material types and 15 working conditions are used to validate the proposed method. Results suggest that the MLP model can provide an accurate capacity estimation, where the overall mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) are limited to 1.22% and 1.61%, respectively. Furthermore, compared with the traditional fine-tuning method, the overall MAPE and RMSPE under various transfer learning application scenarios respectively decrease by up to 78.23% and 75.31%, indicating that the FM-TL method is promising to construct a reliable transfer learning path, which improves the accuracy and reliability of capacity estimation when applied to various target domains. [Display omitted] • A novel extraction ideal based on the aiding of incremental slope is proposed. • A novel feature matching method is proposed to construct reliable and accurate transfer learning path. • 142 batteries with different material types and cycled under various working conditions are used for verification. • The overall MAPE and RMSPE can respectively decrease by up to 78.23% and 75.31% with the proposed method. [ABSTRACT FROM AUTHOR]