Aiming at the problems that the high-capacity steganography model based on encoding-decoding network has weak robustness and can not resist noise attack and channel compression, a high-capacity robust image steganography scheme based on encoding-decoding network was proposed. In the proposed scheme, encoder, decoder and discriminator based on Densely connected convolutional Network (DenseNet) were designed. The secret information and the carrier image were jointly encoded into a steganographic image by the encoder, the secret information was extracted by the decoder, and the discriminator was used to distinguish between carrier images and steganographic images. A noise layer was added between the encoder and the decoder; Dropout, JPEG compression, Gaussian blur, Gaussian noise and salt and pepper noise were used to simulate a real environment with various kinds of noise attacks. The steganographic image output by the encoder was processed by different kinds of noise and decoded by the decoder. Through training the model, the secret information could be extracted from the noise-processed steganographic image by the decoder, so that the noise attacks could be resisted. Experiment results show that the steganographic capacity of the proposed scheme reaches 0.45 - 0.95 bpp on 360×360 pixel images, and the relative embedding capacity is improved by 2.04 times compared to the suboptimal robust steganographic scheme; the decoding accuracy reaches 0.72 - 0.97, and compared with the steganography without noise layer, the average decoding accuracy is improved by 44 percentage points. The proposed scheme not only guarantees high embedding quantity and high coding image quality, but also has stronger anti-noise capability.
Train assisted driving depends on the real-time detection of train operating environment. There are abundant small targets in the images of train operating environment. Compared with large and medium targets, small targets with the proportion of less than 1% of original image have problems of high missed detection and poor detection accuracy due to low resolution. Therefore, a target detection algorithm based on improved YOLOv3 in train operating environment was proposed, namely YOLOv3-TOEI (YOLOv3-Train Operating Environment Image). Firstly, k-means clustering algorithm was used to optimize the anchor to speed up the convergence of the network. Then, dilated convolution was embedded in DarkNet-53 to expand the receptive field, and Dense convolutional Network (DenseNet) was introduced to obtain richer low-level details of the image. Finally, the unidirectional feature fusion structure of original YOLOv3 was improved to bidirectional and adaptive feature fusion structure, which realized the effective combination of deep and shallow features and improved the detection effect of the network on multi-scale targets (especially small targets). Experimental results show that compared with original YOLOv3 algorithm, YOLOv3-TOEI algorithm has the mean Average Precision (mAP)@0.5 reached 84.5%, which increased by 12.2%, and the Frames Per Second (FPS) of 83, verifying that this algorithm has better detection ability of small targets in images of train operating environment.
Aiming at the universal No-Reference Image Quality Assessment (NR-IQA) algorithms, a new NR-IQA algorithm based on the saliency deep features of the pseudo reference image was proposed. Firstly, based on the distorted image, the corresponding pseudo reference image of the distorted image generated by ConSinGAN model was used as compensation information of the distorted image, thereby making up for the weakness of NR-IQA methods: lacking real reference information. Secondly, the saliency information of the pseudo reference image was extracted, and the pseudo saliency map and the distorted image were input into VGG16 netwok to extract deep features. Finally, the obtained deep features were merged and mapped into the regression network composed of fully connected layers to obtain a quality prediction consistent with human vision.Experiments were conducted on four large public image datasets TID2013, TID2008, CSIQ and LIVE to prove the effectiveness of the proposed algorithm. The results show that the Spearman Rank-Order Correlation Coefficient (SROCC) of the proposed algorithm on the TID2013 dataset is 5 percentage points higher than that of H-IQA (Hallucinated-IQA) algorithm and 14 percentage points higher than that of RankIQA (learning from Rankings for no-reference IQA) algorithm. The proposed algorithm also has stable performance for the single distortion types. Experimental results indicate that the proposed algorithm is superior to the existing mainstream Full-Reference Image Quality Assessment (FR-IQA) and NR-IQA algorithms, and is consistent with human subjective perception performance.
Water level prediction is an auxiliary decision support for flood warning work. For accurate water level prediction and providing scientific basis for natural disaster prevention, a prediction model combining Modified Gray Wolf Optimization (MGWO) algorithm and Temporal Convolutional Network (TCN) was proposed, namely MGWO-TCN. In view of the shortage of premature and stagnation in the original Gray Wolf Optimization (MGWO) algorithm, the idea of Differential Evolution (DE) algorithm was introduced to extend the diversity of the grey wolf population. The convergence factor during update and the mutation operator during mutation of the grey wolf population were improved to adjust the parameters in the adaptive manner, thereby improving the convergence speed and balancing the global and local search capabilities of the algorithm. The proposed MGWO algorithm was used to optimize the important parameters of TCN to improve the prediction performance of TCN. The proposed prediction model MGWO-TCN was used for river water level prediction, and the Root Mean Square Error (RMSE) of the model’s prediction results was 0.039. Experimental results show that compared with the comparison model, the proposed MGWO-TCN has better optimization ability and higher prediction accuracy.
Concerning the problems of the difficulties in obtaining, the limitation of labels, and the high cost of labeling of shale gas reservoir data, a Multi-standard Active query Multi-label Learning (MAML) algorithm was proposed. First of all, with the consideration of the informativeness and representativeness of the samples, the preliminary processing was performed on the samples. Secondly, the sample richness constraints including attribute differences and label richness were added, on this basis, the valuable samples were selected and the labels of these samples were queried. Finally, a multi-label learning algorithm was used to predict the labels of the remaining samples. Through experiments on eleven Yahoo datasets, the MAML algorithm was compared with popular multi-label learning algorithms and active learning algorithms, and the superiority of the MAML algorithm was proved. Then, the experiments were extended to four real shale gas well logging datasets. In these experiments, compared with the multi-label learning algorithms: Multi-Label Multi-Label K-Nearest Neighbor (ML-KNN), BackPropagation for Multi-Label Learning (BP-MLL), multi-label learning with GLObal and loCAL label correlation (GLOCAL) and active learning by QUerying Informative and Representative Examples (QUIRE), the MAML algorithm improved the average prediction accuracy of comprehensive quality of shale gas reservoirs by 45 percentage points, 68 percentage points, 68 percentage points, and 51 percentage points, respectively. The practicability and superiority of the MAML algorithm in the prediction of shale gas reservoir sweet spots are fully proved by these experimental results.
In the production of printing industry, using You Only Look Once version 4 (YOLOv4) directly to detect printing defect targets has low accuracy and requires a large number of training samples. In order to solve the problems, a defect target detection method for printed matter based on Siamese-YOLOv4 was proposed. Firstly, a strategy of image segmentation and random parameter change was used to enhance the dataset. Then, the Siamese similarity detection network was added to the backbone network, and the Mish activation function was introduced into the similarity detection network to calculate the similarity of image blocks. After that, the regions with similarity below the threshold were regarded as the defect candidate regions. Finally, the candidate region images were trained to achieve the precise positioning and classification of defect targets. Experimental results show that, the detection precision of the proposed Siamese-YOLOv4 model is better than those of the mainstream target detection models. On the printing defect dataset, the Siamese-YOLOv4 network has the detection precision for satellite ink droplet defect of 98.6%, the detection precision for dirty spot of 97.8%, the detection precision for print lack of 93.9%; and the mean Average Precision (mAP) reaches 96.8%, which is 6.5 percentage points,6.4 percentage points, 14.9 percentage points and 10.6 percentage points higher respectively than the YOLOv4 algorithm, the Faster Regional Convolutional Neural Network (Faster R-CNN) algorithm, the Single Shot multibox Detector (SSD) algorithm and the EfficientDet algorithm. The proposed Siamese-YOLOv4 model has low false positive rate and miss rate in the defect detection of printed matter, and improves the detection precision by calculating similarity of the image blocks through the similarity detection network, proving that the proposed defect detection method can be applied to the printing quality inspection and therefore improve the defect detection level of printing enterprises.
Using random projection acceleration technology to project the Singular Value Decomposition (SVD) of higher dimensional matrices onto a lower subspace can reduce the time consumption of SVD. The singular value random projection compression operator was defined to replace the singular value compression operator, then it was used to improve the Fixed Point Continuation (FPC) algorithm and got FPCrp algorithm. Lots of experiments were conducted on the original algorithm and the improved one. The results show that the random projection technology can reduce more than 50% time consumption of the FPC algorithm, while maintaining its robustness and precision. The modified matrix completion algorithm based on random projection technology is effective in solving large scale problems.