RESUMEN
Multivariate statistical monitoring methods are proven to be effective for the dynamic tobacco strip manufacturing process. However, the traditional methods are not sensitive enough to small faults and the practical tobacco processing monitoring requires further root cause of quality issues. In this regard, this study proposed a unified framework of detection-identification-tracing. This approach developed a dissimilarity canonical variable analysis (CVA), namely, it integrated the dissimilarity analysis concept into CVA, enabling the description of incipient relationship among the process variables and quality variables. We also adopted the reconstruction-based contribution to separate the potential abnormal variable and form the candidate set. The transfer entropy method was used to identify the causal relationship between variables and establish the matrix and topology diagram of causal relationships for root cause diagnosis. We applied this unified framework to the practical operation data of tobacco strip processing from a tobacco factory. The results showed that, compared with traditional contribution plot of anomaly detection, the proposed approach cannot only accurately separate abnormal variables but also locate the position of the root cause. The dissimilarity CVA proposed in this study outperformed traditional CVA in terms of sensitiveness to faults. This method would provide theoretical support for the reliable abnormal detection and diagnosis in the tobacco production process.
RESUMEN
This paper proposes an accurate short-term prediction model of bike-sharing demand with the hybrid TCN-GRU method. The emergence of shared bicycles has provided people with a low-carbon, green and healthy way of transportation. However, the explosive growth and free-form development of bike-sharing has also brought about a series of problems in the area of urban governance, creating a new opportunity and challenge in the use of a large amount of historical data for regional bike-sharing traffic flow predictions. In this study, we built an accurate short-term prediction model of bike-sharing demand with the bike-sharing dataset from 2015 to 2017 in London. First, we conducted a multidimensional bike-sharing travel characteristics analysis based on explanatory variables such as weather, temperature, and humidity. This will help us to understand the travel characteristics of local people, will facilitate traffic management and, to a certain extent, improve traffic congestion. Then, the explanatory variables that help predict the demand for bike-sharing were obtained using the Granger causality with the entropy theory-based MIC method to verify each other. The Multivariate Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model were integrated to build the prediction model, and this is abbreviated as the TCN-GRU model. The fitted coefficient of determination R2 and explainable variance score (EVar) of the dataset reached 98.42% and 98.49%, respectively. Meanwhile, the mean absolute error (MAE) and root mean square error (RMSE) were at least 1.98% and 2.4% lower than those in other models. The results show that the TCN-GRU model has strong efficiency and robustness. The model can be used to make short-term accurate predictions of bike-sharing demand in the region, so as to provide decision support for intelligent dispatching and urban traffic safety improvement, which will help to promote the development of green and low-carbon mobility in the future.
RESUMEN
A naked-eye fluorescent chemodosimeter based on rhodamine-pyridazine conjugate L was synthesized and characterized. L exhibited high selectivity and excellent sensitivity in both absorbance and fluorescence detection of Cu(2+) in aqueous solution with a broad pH span (1-10). The detection limit of the probe was shown to be up to 0.336 ppm. A simple paper test-strip system for the rapid monitoring of Cu(2+) was developed, indicating its convenient use in environmental samples. Furthermore, fluorescence imaging experiments of Cu(2+) in living MGC803 cells demonstrated its value of practical applications in biological systems.