This paper investigates certain novel switching sequences involving division of active vector time for space vectorbased pulsewidth modulation (PWM) generation for a voltage source inverter. This paper proposes two new sequences, and identifies all possible sequences, which result in the same average switching frequency as conventional space vector PWM (CSVPWM) at a given sampling frequency. This paper brings out a method for designing hybrid PWM techniques involving multiple sequences to reduce line current ripple. The three proposed hybrid PWM techniques (three-zone PWM, five-zone PWM and sevenzone PWM) employ three, five and seven different sequences, respectively, in every sector. Each sequence is employed in a spatial region within the sector where it results in the lowest rms current ripple over the given sampling period. The proposed techniques lead to a significant reduction in THD over CSVPWM at high line voltages. The five-zone technique results in the lowest THD among real-time techniques with uniform sampling, while the seven-zone technique is the best among real-time techniques with twin sampling rates. The superior harmonic performance of the proposed techniques over CSVPWM and existing bus-clamping PWM techniques is established theoretically as well as experimentally.

The fallacies of a paper by Wang et al. concerning the stabilisation of a class of second-order underactuated systems are pointed out.

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Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the building energy consumption. In order to obtain better building energy consumption prediction accuracy, an extreme deep learning approach is presented in this paper. The proposed approach combines stacked autoencoders (SAEs) with the extreme learning machine (ELM) to take advantage of their respective characteristics. In this proposed approach, the SAE is used to extract the building energy consumption features, while the ELM is utilized as a predictor to obtain accurate prediction results. To determine the input variables of the extreme deep learning model, the partial autocorrelation analysis method is adopted. Additionally, in order to examine the performances of the proposed approach, it is compared with some popular machine learning methods, such as the backward propagation neural network (BPNN), support vector regression (SVR), the generalized radial basis function neural network (GRBFNN) and multiple linear regression (MLR). Experimental results demonstrate that the proposed method has the best prediction performance in different cases of the building energy consumption.