This article proposes an adversarial reconstruction convolution neural network (ARCNN) for non-uniform illumination frontal face image recovery and recognition. The proposed ARCNN includes a reconstruction network and a discriminative network. The authors employ GAN framework to learn the reconstruction network in an adversarial manner. This article integrates gradient loss and perceptual loss terms, which are able to preserve the detailed and spatial structure image information, into the overall reconstruction loss function to constraint the reconstruction procedure. Experiments are conducted on the typical illumination-sensitive dataset, extended YaleB dataset. The reconstructed results demonstrate that the proposed ARCNN approach can remove the illumination and shadow information and recover natural uniform illuminated face image from non-uniform illuminated ones. Face recognition results on the extended YaleB dataset demonstrate that the proposed ARCNN reconstruction procedure can also preserve the discriminative information of face image for classification task.

This paper proposes a pitch synchronous based feature extraction method for speaker verification under noisy conditions. Before feature extraction, a pitch synchronous analysis procedure is employed. Each speech frame of the input utterance is divided into a periodic part and a non-periodic part by pitch synchronous analysis. Because that the nonperiodic part causes harmonic leakage of the speech frame, only the periodic part is preserved for concatenating feature extraction. Two dominant spectral and cepstral analysis methods, MFCC and LPCC, are employed to represent the features of each frame. Speaker verification experiments under noisy conditions are conducted on NTT speech database. The performances of pitch synchronous based MFCC (p-syn MFCC), pitch synchronous based LPCC (psyn LPCC), MFCC and LPCC are compared with each other. Experiments results indicate that the proposed pitch synchronous based feature extraction method is efficient for speaker verification under noisy conditions.

Beam pumping system which is widely used in petroleum enterprises of China is one of the most energy-consuming equipment. It is difficult to be modeled and optimized due to its complication and nonlinearity. To address this issue, a novel intelligent computing based method is proposed in this paper. It firstly employs the general regression neural network (GRNN) algorithm to obtain the best model of the beam pumping system, and secondly searches the optimal operation parameters with improved strength Pareto evolutionary algorithm (SPEA2). The inputs of GRNN include the number of punching, the maximum load, the minimum load, the effective stroke, and the computational pump efficiency, while the outputs are the electric power consumption and the oil yield. Experimental results show that there is good overlap between model estimations and unseen data. Then sixty-one sets of optimum parameters are found based on the obtained model. Also, the results show that, under the optimum parameters, more than 5.34% oil yield is obtained and more than 3.75% of electric power consumption is saved.