Chuanlong Li

Deep neural network (DNN) was applied in sonar image target recognition tasks, but it is very difficult to obtain enough sonar images that contain a target; as a result, the direct use of a small amount of data to train a DNN will cause overfitting and other problems. Transfer learning is the most effective way to address such scenarios. However, there is a large domain gap between optical images and sonar images, and common transfer learning methods may not be able to effectively handle it. In this paper, we propose a transfer learning method for sonar image classification and object detection called the texture feature removal network. We regard the texture features of an image as domain-specific features, and we narrow the domain gap by discarding the domain-specific features, and hence, make it easier to complete knowledge transfer. Our method can be easily embedded into other transfer learning methods, which makes it easier to apply to different application scenarios. Experimental results show that our method is effective in side-scan sonar image classification tasks and forward-looking sonar image detection tasks. For side-scan sonar image classification tasks, the classification accuracy of our method is enhanced by 4.5% in a supervised learning experiment, and for forward-looking sonar detection tasks, the average precision (AP) is also significantly improved.

With the development of sonar technology, sonar images have been widely used to detect targets. However, there are many challenges for sonar images in terms of object detection. For example, the detectable targets in the sonar data are more sparse than those in optical images, the real underwater scanning experiment is complicated, and the sonar image styles produced by different types of sonar equipment due to their different characteristics are inconsistent, which makes it difficult to use them for sonar object detection and recognition algorithms. In order to solve these problems, we propose a novel sonar image object-detection method based on style learning and random noise with various shapes. Sonar style target sample images are generated through style transfer, which enhances insufficient sonar objects image. By introducing various noise shapes, which included points, lines, and rectangles, the problems of mud and sand obstruction and a mutilated target in the real environment are solved, and the single poses of the sonar image target is improved by fusing multiple poses of optical image target. In the meantime, a method of feature enhancement is proposed to solve the issue of missing key features when using style transfer on optical images directly. The experimental results show that our method achieves better precision.

Among biological networks, co-expression networks have been widely studied. One of the most commonly used pipelines for the construction of co-expression networks is weighted gene co-expression network analysis (WGCNA), which can identify highly co-expressed clusters of genes (modules). WGCNA identifies gene modules using hierarchical clustering. The major drawback of hierarchical clustering is that once two objects are clustered together, it cannot be reversed; thus, re-adjustment of the unbefitting decision is impossible. In this paper, we calculate the similarity matrix with the distance correlation for WGCNA to construct a gene co-expression network, and present a new approach called the k-module algorithm to improve the WGCNA clustering results. This method can assign all genes to the module with the highest mean connectivity with these genes. This algorithm re-adjusts the results of hierarchical clustering while retaining the advantages of the dynamic tree cut method. The validity of the algorithm is verified using six datasets from microarray and RNA-seq data. The k-module algorithm has fewer iterations, which leads to lower complexity. We verify that the gene modules obtained by the k-module algorithm have high enrichment scores and strong stability. Our method improves upon hierarchical clustering, and can be applied to general clustering algorithms based on the similarity matrix, not limited to gene co-expression network analysis.

Compared with conventional zero-shot learning (ZSL), generalized ZSL (GZSL) is more challenging because the test instances may come from seen and unseen classes. The most existing GZSL methods learn a visual-semantic mapping function to bridge the knowledge transfer from seen to unseen classes by using semantic information and other labeled training data. However, these methods often suffer from severe performance degradation because they ignore similar structures between different classes. To solve these problems, we propose a GZSL method that transforms GZSL problems to conventional supervised learning ones by synthesizing pseudo features for unseen classes. This technique has two key aspects. The first one is the synthesis strategy; the proposed strategy directly synthesizes the pseudo features of unseen classes contrary to current synthesis-based methods, which synthesize pseudo instances. Our method regards the combination of N features of instances as the pseudo features. These N features belong to N different classes that are similar to unseen ones. This synthesis strategy is in line with the cognitive style of human beings. The second key aspect is that we preserve the similar structures between seen and unseen classes. Inspired by the center loss method, we assign each semantic vector as the center of deep features in the training stage. This way preserves the similar structures between the classes. Such preservation can be beneficial for improving classification accuracy. The experimental results on four benchmark datasets demonstrate that our model outperforms state-of-the-art methods for the GZSL. The source code is available at https://github.com/guizilaile23/SPF-GZSL.

When side-scan sonars collect data, sonar energy attenuation, the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations occur, which lead to an uneven gray level in side-scan sonar images. Therefore, gray scale correction is needed before further processing of side-scan sonar images. In this paper, we introduce the causes of gray distortion in side-scan sonar images and the commonly used optical and side-scan sonar gray scale correction methods. As existing methods cannot effectively correct distortion, we propose a simple, yet effective gray scale correction method for side-scan sonar images based on Retinex given the characteristics of side-scan sonar images. Firstly, we smooth the original image and add a constant as an illumination map. Then, we divide the original image by the illumination map to produce the reflection map. Finally, we perform element-wise multiplication between the reflection map and a constant coefficient to produce the final enhanced image. Two different schemes are used to implement our algorithm. For gray scale correction of side-scan sonar images, the proposed method is more effective than the latest similar methods based on the Retinex theory, and the proposed method is faster. Experiments prove the validity of the proposed method.

Constrained dynamic matrix control often needs to solve a nonlinear optimization problem with constraints. The traditional solution method is highly dependent on the prediction model and is easy to fall into the local minimum. To solve this problem, an improved differential evolution algorithm is applied to solve the optimal control law, and the optimization problem with constraints is dealt with by setting specific mutation operation and penalty function. Comparing with the traditional constraint dynamic matrix control with original dynamic differential evolution algorithm, the effectiveness of the improved method proposed in this paper is verified. The proposed method solves a class of dynamic matrix control optimization problems with constraints, and has the advantages of faster convergence and no static error. A three-tank liquid level system with constraints is considered to illustrate the effectiveness of the proposed approach.