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This paper proposes a new method of flash qualitative identification (FQI) to qualitatively identify a certain target component from a mixture within half a second by disusing the analytical column, which is a time-consuming unit in current chromatography instruments. First, a Noised Spectrum Identification (NSI) model was constructed for the data set generated directly by diode array detector (DAD) without the process in an analytical column. Then, a method called vector error algorithm (VEA) was proposed to generate an error according to the DAD data set for a mixture and a specific spectrum for the target component to be identified. A criterion based on the error generated by the VEA is used to give a judgement of whether the specific spectrum exists in the DAD data set. Several simulations demonstrate the high performance of the FQI method, and an experiment for three known materials was carried out to validate the effectiveness of this method. The results show that the NSI model concurs with the real experiment result; therefore, the error generated by the VEA was an effective criterion to identify a specific component qualitatively, and the FQI method could finish the identification task within half a second., CHROMATOGRAPHY has been developed as a set of laboratory techniques that are widely applied in the quality control (QC) of mixtures such as herbal medicine, grape wine, petroleum, judicial expertise, [...]

The purpose of this research is to compare clustering methods and pick up the optimal clustered approach for rural building energy consumption data. Research undertaken so far has mainly focused on solving specific issues when employing the clustered method. This paper concerns Yushan island resident's time-series electricity usage data as a database for analysis. Fourteen algorithms in five categories were used for cluster analysis of the basic data sets. The result shows that Km_Euclidean and Km_shape present better clustering effects and fitting performance on continuous data than other algorithms, with a high accuracy rate of 67.05% and 65.09%. Km_DTW is applicable to intermittent curves instead of continuous data with a low precision rate of 35.29% for line curves. The final conclusion indicates that the K-means algorithm with Euclidean distance calculation and the k-shape algorithm are the two best clustering algorithms for building time-series energy curves. The deep learning algorithm can not cluster time-series-building electricity usage data under default parameters in high precision. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop a framework to understand building energy usage pattern finding using data mining algorithms. Developing advanced techniques and requirements for carbon emission reduction provides higher demands for building energy efficiency. Research conducted so far has mainly focused on total energy consumption data clusters instead of time-series curve peculiarity. This research adopts the time-series cluster algorithm k-shape and the ARM Apriori method to study the simulation database generated by the official restaurant energy model. These advanced data mining techniques can discover potential information hidden in a big database that has not been identified by people. The results show that the restaurant time-series energy consumption curve can be clustered into four type patterns: Invert U, M, Invert V, and Multiple M. Each mode has its own variation characteristics. Two aspects for the solution of intensity and peak shift are proposed, achieving energy savings and focusing on different curve modes. The conclusion shows that the combination of time-series clustering and the ARM algorithm work flow can successfully discover the building operation pattern. Some solutions focusing on restaurant energy usage issues have been proposed, and future investigations should pay more attention to building area-influenced factors. [ABSTRACT FROM AUTHOR]