A multi-stage low-illuminance image enhancement network based on attention mechanism was proposed to solve the problem that the details of low-illuminance images are lost due to the overlapping of image contents and large brightness differences in some regions during the enhancement process of low-illuminance images. At the first stage， an improved multi-scale fusion module was used to perform preliminary image enhancement. At the second stage， the enhanced image information of the first stage was cascaded with the input of this stage， and the result was used as the input of the multi-scale fusion module in this stage. At the third stage， the enhanced image information of the second stage was cascaded with the input of the this stage， and the result was used as the input of the multi-scale fusion module in this stage. In this way， with the use of multi-stage fusion， not only the brightness of the image was improved adaptively， but also the details were retained adaptively. Experimental results on open datasets LOL and SICE show that compared to the algorithms and networks such as MSR （Multi-Scale Retinex） algorithm， gray Histogram Equalization （HE） algorithm and RetinexNet （Retina cortex Network）， the proposed network has the value of Peak Signal-to-Noise Ratio （PSNR） 11.0% to 28.9% higher， and the value of Structural SIMilarity （SSIM） increased by 6.8% to 46.5%. By using multi-stage method and attention mechanism to realize low-illuminance image enhancement， the proposed network effectively solves the problems of image content overlapping and large brightness difference， and the images obtained by this network are more detailed and subjective recognizable with clearer textures.

Concerning the lack of flexibility in adversarial training of Deep Convolutional Generative Adversarial Network （DCGAN） and the problems of inflexible optimization and unclear convergence state of Binary Cross-Entropy loss （BCE loss） function used in DCGAN， an improved algorithm of Generative Adversarial Network （GAN） based on arbitration mechanism was proposed. In this algorithm， the proposed arbitration mechanism was added on the basis of DCGAN. Firstly， the network structure of the proposed improved algorithm was composed of generator， discriminator， and arbiter. Secondly， the adversarial training was conducted by the generator and discriminator according to the training plan， and the abilities to generate images and verify the authenticity of images were strengthened according to the characteristics learned from the dataset respectively. Thirdly， the arbiter was generated by the generator and the discriminator after the last round of adversarial training and metric score calculation module， and the adversarial training results of the generator and the discriminator were measured by this arbiter and fed back into the training plan. Finally， a wining limit was added to the network structure to improve the stability of model training， and the Circle loss function was used to replace the BCE loss function， which made the model optimization process more flexible and the convergence state more clear. Experimental results show that the proposed algorithm has a good generation effect on the architectural and face datasets. On the Large-scale Scene UNderstanding （LSUN） dataset， the proposed algorithm has the Fréchet Inception Distance （FID） index decreased by 1.04% compared with the DCGAN original algorithm； on the CelebA dataset， the proposed algorithm has the Inception Score （IS） index increased by 4.53% compared with the DCGAN original algorithm. The images generated by the proposed algorithm have better diversity and higher quality.

Multiway Principal Component Analysis (MPCA) has been widely used to monitor multivariate batch process. In MPCA method, the batch data are transformed as a vector in high-dimensional space, resulting in large computation, storage space and loss of important information inevitably. A new batch process fault diagnosis method based on the two-Dimensional Principal Component Analysis (2DPCA) was presented. Essentially, every batch data was presented as a second order vector, or a matrix. In this case, 2DPCA could be used to deal with the two-dimensional batch data matrix directly instead of performing vectorizing procedure with low memory and storage requirements. In addition, 2DPCA was used to model with the covariance average of all the batches, which accurately reflected the different faults and enhanced the accuracy of fault diagnosis to a certain extent. The monitoring results of an industrial example show that the 2DPCA method outperforms the conventional MPCA.