<p>The supporting role of financial development on the real economy is unquestionable. How to improve the profitability of commercial banks, allocate more resources efficiently and quickly to the key links of development, and urge financial institutions to better serve the real economy, is our breakthrough in economic development bottlenecks important node. Based on the data of 16 listed commercial banks in 2018, this paper uses factor analysis to calculate the profitability scores of each bank, discusses how commercial banks can better serve the real economy, and proposes corresponding policy recommendations.</p>
Singing-voice separation is a separation task that involves a singing voice and musical accompaniment. In this paper, we propose a novel, unsupervised methodology for extracting a singing voice from the background in a musical mixture. This method is a modification of robust principal component analysis (RPCA) that separates a singing voice by using weighting based on gammatone filterbank and vocal activity detection. Although RPCA is a helpful method for separating voices from the music mixture, it fails when one single value, such as drums, is much larger than others (e.g., the accompanying instruments). As a result, the proposed approach takes advantage of varying values between low-rank (background) and sparse matrices (singing voice). Additionally, we propose an expanded RPCA on the cochleagram by utilizing coalescent masking on the gammatone. Finally, we utilize vocal activity detection to enhance the separation outcomes by eliminating the lingering music signal. Evaluation results reveal that the proposed approach provides superior separation outcomes than RPCA on ccMixter and DSD100 datasets.
This paper proposes a new teaching method for the Python language programming course, which can better enable students to understand and use in the background of big data. Python is an open-source programming language with a community-based model. In this paper, firstly, various functions are described in Python Language. Additionally, different application areas are presented in this paper, such as transportation logistics, urban management, biomedical field, smart power grid, energy field, and commercial field. Finally, bank customer churn as case-based teaching is introduced can improve the students’ confidence in their future studies.
Accurately predicting the remaining lifespan of lithium-ion batteries is critical for the efficient and safe use of these devices. Predicting a lithium-ion battery’s remaining lifespan is challenging due to the non-linear changes in capacity that occur throughout the battery’s life. This study proposes a fused prediction model that employs a multimodal decomposition approach to address the problem of non-linear fluctuations during the degradation process of lithium-ion batteries. Specifically, the capacity attenuation signal is decomposed into multiple mode functions using successive variational mode decomposition (SVMD), which captures capacity fluctuations and a primary attenuation mode function to account for the degradation of lithium-ion batteries. The hyperparameters of the long short-term memory network (LSTM) are optimized using the tuna swarm optimization (TSO) technique. Subsequently, the trained prediction model is used to forecast various mode functions, which are then successfully integrated to obtain the capacity prediction result. The predictions show that the maximum percentage error for the projected results of five unique lithium-ion batteries, each with varying capacities and discharge rates, did not exceed 1%. Additionally, the average relative error remained within 2.1%. The fused lifespan prediction model, which integrates SVMD and the optimized LSTM, exhibited robustness, high predictive accuracy, and a degree of generalizability.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.