Green mine construction is the main melody of mining development and problems such as safe production, energy saving and consumption reduction need to be solved urgently. The working conditions of the mill are complex in the process of grinding. Aiming at the problems existing in the feature extraction and load prediction of the mill, a signal-processing method based on adaptive chirp mode decomposition (ACMD) and a standardized variable distance classifier (SVD) is proposed. Firstly, the recursive framework of the ACMD method is used to obtain the initial frequency of mill vibration signals. Secondly, the initial frequency is used to reconstruct the high-resolution component of the mill vibration signal through the iterative frame in the ACMD method. The frequency corresponding to the frequency domain peak of the reconstructed signal is then selected as the mill load feature vector. Finally, with consideration to the influence of standard deviation and standardized variable factors on the feature vectors, a standardized variable distance classifier is proposed. The feature vectors of the mill load are input into the SVD model for training, and the state types of the mill load are obtained. The method is applied to the grinding experiment and the results show that the frequency-domain features obtained by the mill vibration signal-processing method based on ACMD-SVD are obvious, which has high accuracy in the identification of mill load types, and provides a new idea for the extraction of mill load features and prediction of the mill load.

The article serves as a follow-up to the theses of EU Fishing Fleet Adjustment to Marine Bioresources — Regulations and Finance(Prawo Morskie XXIX).

The law regarding the subject is contentious and sparks off varying interpretation in administrative practice and case-law, including that of the Supreme Administrative Court. The article criticizes this portion of law and formulates conclusions de lege ferenda.

The article is an attempt to evaluate accuracy of Marx’s predictions and to present some reasons for Marx’s ineffectiveness as a forecaster. The article discusses contemporary research on forecasting, uses the results to Marx, and analyses the dialectic aspect of laws in order to explain forecasting weaknesses of Marx. The author of Capital turns out to be – in P.E. Tetlock’s typology – a ‘hedgehog’, i.e.: a bad forecaster, who uses questionable methods to defend his predictions at all costs.