Recognition and Simulation of Exercise Mode Based on Energy Consumption Model

Sports energy consumption is a quantitative reflection of physical exercise effect. Combined with different sports modes and students’ physical characteristics, the calculation model of sports energy consumption is put forward. Firstly, the relationship between students’ age, height, weight, gender, and energy consumption is analyzed by using multiple linear regression method, and a linear acceleration model is proposed by combining different exercise methods. The relationship between the integral value of acceleration and energy consumption is analyzed, and a linear integral model based on different motion modes is proposed. Based on the kinetic energy theorem, the student movement energy expenditure is estimated. This paper proposes a human movement recognition method based on hybrid features, which mostly can represent the curve of the second generation wavelet transform edge thinning, and from the edge and texture features of the optimal said human posture, the statistical characteristic of the second generation wavelet transform is subtly trained as image characteristics, learning and recognition of human movement. Then, the motion recognition algorithm is tested, which can effectively identify the common movement patterns of primary and middle school students. Finally, the linear relationship between the estimation results of the model and the calculation results of Meijer is analyzed. The analysis results show that the linear acceleration model proposed in this paper can estimate the energy consumption of primary and middle school students’ motion relatively accurately.

Application of Random Forest and Hidden Markov Models for Automatic and Fast Classification of Power Quality Signals

In this paper, wavelet transform, namely the maximal overlap discrete Wavelet Transform (MODWT) and the second generation Wavelet Transform (SGWT) have been implemented. These wavelet transforms are applied to get selected features of the signals. Features are used as inputs to two types of classifiers namely, Hidden Markov Model (HMM) classifiers and the Random Forest (RF) classifier in the both absence and presence of Noise to evaluate the efficiency. The classification accuracy (CA) calculated using these classifiers clearly shows that the RF classifiers is a better classifier then the HMM classifier as it possess higher recognition rate at all levels of noise along with the pure PQ signals. Another important property of RF classifier is the proper classification of large number of class of both slow and the fast disturbances.

The Reciprocating Compressor Fault Analysis Based on ORSGWT

It can be found that the redundant second generation wavelet function had more accurate analysis ability compared with the other wavelets and wavelet packets through simulation signals analyses. The analyses of the industrial signals may be more difficult due to its complex character. The de-noising ability of the redundant second generation wavelet to the industrial noise was confirmed by comparing with wavelet packets. But the signal after de-noising still expresses confused and makes analysis difficult. The optimized redundant second generation wavelet transform (ORSGWT) method was established with Newton interpolation and scale thresholds. Then the fault signals of valve block gap being processed with ORSGWT method were smoother and more apparent about the fault characters comparing with the normal state signals.

Corrigendum

Corrigendum to “N Li, R Zhou and XZ Zhao (2011) Mechanical faulty signal denoising using a redundant non-linear second-generation wavelet transform. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225 (4): 799–808. Original DOI: 10.1243/09544062JMES2410 .” This corrigendum is offered as a means to correct following errors.