Coal mine monitoring video image data is characterized by overall dark and blurry, low contrast, poor illumination, and a large amount of noise. The quality of the data directly affects the accuracy of the recognition algorithm, multi-scale decomposition method with noise suppression and structure protection is the core of the detail enhancement algorithm. The existing detail enhancement method based on L0 norm minimization only utilizes local structural information, and it is difficult to effectively filter the noise existing in the video data. Aiming at the existing problems, a coal mine monitoring data detail enhancement algorithm based on L0 norm and low rank analysis was proposed and achieved good results.

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In this paper, a robust kalman filter is designed for the uncertainty time-varying discrete systems with state delay in process and output matrices combined with the possibility of missing measurements. The uncertainties are expected in the process, output and white noise covariance matrices. A formula for a candidate upper bound on the actual state estimation error variances for all admissible parameter uncertainties and possible missing measurements is obtained. The filter parameters are optimized to give a minimal upper bound on the state estimation error covariance for all admissible uncertainties and missing measurements.

With the increasing development of online collaborative platforms, there emerge massive subjective texts. However, due to the massive negative news about eroticism, violence, extremity and corruption, as well as the influences of agitators and provocateurs, it is quite likely that Internet users can be turned from conscious individuals into unconscious groups, which contributes to the accumulation of public negative sentiment. In this work, we focus on the identification of sentiment and especially negative sentiment. Specifically, we introduce sentiment layer to the basic LDA topic model to map the texts into a lower dimensional space of topics and sentiment. Besides, we also consider the sentiment dictionary based sentiment feature word extraction method. By feeding the feature words into Support Vector Machine (SVM) classifier, we get the sentiment tendency of texts. Our experiments prove the efficiency of proposed method.

In this paper, a robust finite-horizon kalman filter for discrete time-varying