LAUSR

Accurate recognition of operating conditions helps to correctly control froth flotation. Most previous recognition methods mainly focus on the statistic feature but ignore the temporal information in the dynamic and changing flotation process. Actually, the temporal changing trend of froth appearance can reflect the change in flotation performance. Besides, the bubble size distribution (BSD) is a representative visual feature for characterizing froth appearance. Therefore, we propose to exploit the statistics and temporal correlation of BSD for condition recognition in this article. First, to sufficiently characterize the statistics of bubble sizes about the initial state of the froth video, we sample multiple froth images from a short video clip at the beginning of the video and then use a cumulative distribution function (CDF) to describe the BSD in these sampled images, which is regarded as the cumulative distribution statistic (CDS). Meanwhile, to obtain the temporal change trend of BSDs from the initial to the last moment of the froth video, we sample multiple sequential froth images from the whole video and use the CDF to describe the BSD of individual image and then the BSD sequences are input into the bidirectional long short-term memory (BiLSTM) to extract their temporal correlation, which is regarded as the cumulative distribution temporal correlation (CDTC). Additionally, we integrate texture and velocity features with our constructed CDS and CDTC features via a joint learning network to recognize operating conditions. Extensive experiments demonstrate that the proposed model using CDS and CDTC is more effective than other models with BSD features. Besides, compared to the models using the single CDS and CDTC, the accuracy of the proposed model is increased by 8.39% and 2.61%, respectively.