Signal Optimization and Control Strategy for New Energy Hy- brid Power Generation System Based on Deep Learning Fei Li Page: 1-8 AbstractFull Text Download: 1 times https://doi.org/10.6025/stj/2025/14/1/1-8 Abstract: By combining various renewable energy sources, new hybrid power generation technologies can significantly
improve energy utilization efficiency and reduce environmental impact. However, due to the uncertainty and
intermittent nature of this technology, signal optimization and control become particularly important. In this
study, we use advanced techniques to enhance the performance of hybrid power generation systems. Our
work involves utilizing deep learning to analyze and predict various factors, thereby finding the optimal
operating mode. The conclusions of this work will have a positive impact on future energy management and
dispatching. Additionally, the methods proposed in this article can provide valuable references for research in
other related fields.
Identification of Partial Discharge Types Based on Multifractal Detrended Fluctuation Analysis Xinbai Xue Page: 9-16 AbstractFull Text Download: 1 times https://doi.org/10.6025/stj/2025/14/1/9-16 Abstract: This paper proposes a method for identifying partial discharge types based on multi fractaldetrended fluc-
tuation analysis. This method transforms partial discharge signals through multi fractal transformation,
extracts the fractal features of the signals, and combines with detrended fluctuation analysis to accurately
identify partial discharge types. Advanced algorithms and techniques are used in this research to classify and
analyze different types of partial discharges, achieving significant results. Experimental results demonstrate
that this method exhibits high accuracy and reliability in identifying partial discharge types, providing a new
effective means for partial discharge monitoring and fault diagnosis.
Novel Encoding Model for Asymptotically Greater Compactness Bernardo Subercaseaux, Marijn J.H. Heule Page: 17-34 AbstractFull Text Download: 2 times https://doi.org/10.6025/stj/2025/14/1/17-34 Abstract: A packing k-coloring for a graph G = (V, E) is defined as a function that assigns colors from the set {1, ..., k} to
the vertices in V. This assignment must ensure that any two vertices u and v that share the same color c are
separated by a distance greater than c within the graph G. One of the key challenges in the area of packing
colorings is to ascertain the packing chromatic number of the infinite square grid. Prior research has estab-
lished that this number lies between 13 and 15. Our study enhances the lower limit to 14. Additionally, we
introduce a novel encoding method that offers asymptotically greater compactness compared to those
previously employed.
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