A Novel Radial Basis Function Neural Network with High Generalization Performance for Nonlinear Process Modelling

A radial basis function neural network (RBFNN), with a strong function approximation ability, was proven to be an effective tool for nonlinear process modeling. However, in many instances, the sample set is limited and the model evaluation error is fixed, which makes it very difficult to construct an optimal network structure to ensure the generalization ability of the established nonlinear process model. To solve this problem, a novel RBFNN with a high generation performance (RBFNN-GP), is proposed in this paper. The proposed RBFNN-GP consists of three contributions. First, a local generalization error bound, introducing the sample mean and variance, is developed to acquire a small error bound to reduce the range of error. Second, the self-organizing structure method, based on a generalization error bound and network sensitivity, is established to obtain a suitable number of neurons to improve the generalization ability. Third, the convergence of this proposed RBFNN-GP is proved theoretically in the case of structure fixation and structure adjustment. Finally, the performance of the proposed RBFNN-GP is compared with some popular algorithms, using two numerical simulations and a practical application. The comparison results verified the effectiveness of RBFNN-GP.

Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice

Background Plant variety identification is the one most important of agricultural systems. Development of DNA marker profiles of released varieties to compare with candidate variety or future variety is required. However, strictly speaking, scientists did not use most existing variety identification techniques for “identification” but for “distinction of a limited number of cultivars,” of which generalization ability always not be well estimated. Because many varieties have similar genetic backgrounds, even some essentially derived varieties (EDVs) are involved, which brings difficulties for identification and breeding progress. A fast, accurate variety identification method, which also has good performance on EDV determination, needs to be developed. Results In this study, with the strategy of “Divide and Conquer,” a variety identification method Conditional Random Selection (CRS) method based on SNP of the whole genome of 3024 rice varieties was developed and be applied in essentially derived variety (EDV) identification of rice. CRS is a fast, efficient, and automated variety identification method. Meanwhile, in practical, with the optimal threshold of identity score searched in this study, the set of SNP (including 390 SNPs) showed optimal performance on EDV and non-EDV identification in two independent testing datasets. Conclusion This approach first selected a minimal set of SNPs to discriminate non-EDVs in the 3000 Rice Genome Project, then united several simplified SNP sets to improve its generalization ability for EDV and non-EDV identification in testing datasets. The results suggested that the CRS method outperformed traditional feature selection methods. Furthermore, it provides a new way to screen out core SNP loci from the whole genome for DNA fingerprinting of crop varieties and be useful for crop breeding.

Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate

In this article, the authors develop the Particle Swarm Optimization algorithm (PSO) in order to optimise the BP network in order to elaborate an accurate dynamic model that can describe the behavior of the temperature and the relative humidity under an experimental greenhouse system. The PSO algorithm is applied to the Back-Propagation Neural Network (BP-NN) in the training phase to search optimal weights baded on neural networks. This approach consists of minimising the reel function which is the mean squared difference between the real measured values of the outputs of the model and the values estimated by the elaborated neural network model. In order to select the model which possess higher generalization ability, various models of different complexity are examined by the test-error procedure. The best performance is produced by the usage of one hidden layer with fourteen nodes. A comparison of measured and simulated data regarding the generalization ability of the trained BP-NN model for both temperature and relative humidity under greenhouse have been performed and showed that the elaborated model was able to identify the inside greenhouse temperature and humidity with a good accurately.

Robust Attention Deraining Network for Synchronous Rain Streaks and Raindrops Removal

<div>Synchronous Rain streaks and Raindrops Removal (SR3) is a very hard and challenging task, since rain streaks and raindrops are two wildly divergent real-scenario phenomena with different optical properties and mathematical distributions. As such, most of existing deep learning-based Singe Image Deraining (SID) methods only focus on one of them or the other. To solve this issue, we propose a new, robust and hybrid SID model, termed Robust Attention Deraining Network (RadNet) with strong robustenss and generalztion ability. The robustness of RadNet has two implications :(1) it can restore different degenerations, including raindrops, rain streaks, or both; (2) it can adapt to different data strategies, including single-type, superimposed-type and blended-type. Specifically, we first design a lightweight robust attention module (RAM) with a universal attention mechanism for coarse rain removal, and then present a new deep refining module (DRM) with multi-scales blocks for precise rain removal. The whole process is unified in a network to ensure sufficient robustness and strong generalization ability. We measure the performance of several SID methods on the SR3 task under a variety of data strategies, and extensive experiments demonstrate that our RadNet can outperform other state-of-the-art SID methods.</div>

Robust Attention Deraining Network for Synchronous Rain Streaks and Raindrops Removal

<div>Synchronous Rain streaks and Raindrops Removal (SR3) is a very hard and challenging task, since rain streaks and raindrops are two wildly divergent real-scenario phenomena with different optical properties and mathematical distributions. As such, most of existing deep learning-based Singe Image Deraining (SID) methods only focus on one of them or the other. To solve this issue, we propose a new, robust and hybrid SID model, termed Robust Attention Deraining Network (RadNet) with strong robustenss and generalztion ability. The robustness of RadNet has two implications :(1) it can restore different degenerations, including raindrops, rain streaks, or both; (2) it can adapt to different data strategies, including single-type, superimposed-type and blended-type. Specifically, we first design a lightweight robust attention module (RAM) with a universal attention mechanism for coarse rain removal, and then present a new deep refining module (DRM) with multi-scales blocks for precise rain removal. The whole process is unified in a network to ensure sufficient robustness and strong generalization ability. We measure the performance of several SID methods on the SR3 task under a variety of data strategies, and extensive experiments demonstrate that our RadNet can outperform other state-of-the-art SID methods.</div>

Multi-Level Fusion Temporal–Spatial Co-Attention for Video-Based Person Re-Identification

A convolutional neural network can easily fall into local minima for insufficient data, and the needed training is unstable. Many current methods are used to solve these problems by adding pedestrian attributes, pedestrian postures, and other auxiliary information, but they require additional collection, which is time-consuming and laborious. Every video sequence frame has a different degree of similarity. In this paper, multi-level fusion temporal–spatial co-attention is adopted to improve person re-identification (reID). For a small dataset, the improved network can better prevent over-fitting and reduce the dataset limit. Specifically, the concept of knowledge evolution is introduced into video-based person re-identification to improve the backbone residual neural network (ResNet). The global branch, local branch, and attention branch are used in parallel for feature extraction. Three high-level features are embedded in the metric learning network to improve the network’s generalization ability and the accuracy of video-based person re-identification. Simulation experiments are implemented on small datasets PRID2011 and iLIDS-VID, and the improved network can better prevent over-fitting. Experiments are also implemented on MARS and DukeMTMC-VideoReID, and the proposed method can be used to extract more feature information and improve the network’s generalization ability. The results show that our method achieves better performance. The model achieves 90.15% Rank1 and 81.91% mAP on MARS.

Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM

Aiming at the problems of weak generalization ability and long training time in most fault diagnosis models based on deep learning, such as support vector machines and random forest algorithms, one intelligent diagnosis method of rolling bearing fault based on the improved convolution neural network and light gradient boosting machine is proposed. At first, the convolution layer is used to extract the features of the original signal. Second, the generalization ability of the model is improved by replacing the full connection layer with the global average pooling layer. Then, the extracted features are classified by a light gradient boosting machine. Finally, the verification experiment is carried out, and the experimental result shows that the average training and diagnosis time of the model is only 39.73 s and 0.09 s, respectively, and the average classification accuracy of the model is 99.72% and 95.62%, respectively, on the same and variable load test sets, which indicates that the diagnostic efficiency and classification accuracy of the proposed model are better than those of other comparison models.

Rainfall prediction optimization model in ten-day time step based on sliding window mechanism and zero sum game

Rainfall is a precious water resource, especially for Shenzhen with scarce local water resources. Therefore, an effective rainfall prediction model is essential for improvement of water supply efficiency and water resources planning in Shenzhen. In this study, a deep learning model based on zero sum game (ZSG) was proposed to predict ten-day rainfall, the regular models were constructed for comparison, and the cross-validation was performed to further compare the generalization ability of the models. Meanwhile, the sliding window mechanism, differential evolution genetic algorithm, and discrete wavelet transform were developed to solve the problem of data non-stationarity, local optimal solutions, and noise filtration, respectively. The k-means clustering algorithm was used to discover the potential laws of the dataset to provide reference for sliding window. Mean square error (MSE), Nash–Sutcliffe efficiency coefficient (NSE) and mean absolute error (MAE) were applied for model evaluation. The results indicated that ZSG could better optimize the parameter adjustment process of models, and improved generalization ability of models. The generalization ability of the bidirectional model was superior to that of the unidirectional model. The ZSG-based models showed stronger superiority compared with regular models, and provided the lowest MSE (1.29%), NSE (21.75%), and MAE (7.5%) in the ten-day rainfall prediction.

A 6DoF Pose Estimation Dataset and Network for Multiple Parametric Shapes in Stacked Scenarios

Most industrial parts are instantiated from different parametric templates. The 6DoF (6D) pose estimation tasks are challenging, since some part objects from a known template may be unseen before. This paper releases a new and well-annotated 6D pose estimation dataset for multiple parametric templates in stacked scenarios donated as Multi-Parametric Dataset, where a training set (50K scenes) and a test set (2K scenes) are obtained by automatical labeling techniques. In particular, the test set is further divided into a TEST-L dataset for learning evaluation and a TEST-G dataset for generalization evaluation. Since the part objects from the same template are regarded as a class in the Multi-Parametric Dataset and the number of part objects is infinite, we propose a new 6D pose estimation network as our baseline method, Multi-templates Parametric Pose Network (MPP-Net), aiming to have sufficient generalization ability for parametric part objects in stacked scenarios. To our best knowledge, our dataset and method are the first to jointly achieve 6D pose estimation and parameter values prediction for multiple parametric templates. Many experiments are conducted on the Multi-Parametric Dataset. The mIoU and Overall Accuracy of foreground segmentation and template segmentation on the two test datasets exceed 99.0%. Besides, MPP-Net achieves 92.9% and 90.8% on mAP under the threshold of 0.5cm for translation prediction, achieves 41.9% and 36.8% under the threshold of 5∘ for rotation prediction, and achieves 51.0% and 6.0% under the threshold of 5% for parameter values prediction, on the two test set, respectively. The results have shown that our dataset has exploratory value for 6D pose estimation and parameter values prediction tasks.