The forward kinematics of parallel robots is a challenging issue due to its highly coupled non‐linear relation among branch chains. This paper presents a novel approach to forward kinematics of parallel robots based on kernel extreme learning machine (KELM). To tackle with the forward kinematics solution of fully parallel robots, the forward kinematics solution of parallel robots is equivalently transformed into a machine learning model first. On this basis, a computational model combining sparrow search algorithm and KELM is then established, which can serve as both regression and classification. Based on SSA‐optimised KELM (SSA‐KELM) established in this study, a binary discriminator for judging the existence of the forward kinematics solution and a multi‐label regression model for predicting the forward kinematics solution are built to obtain the forward kinematics general solution of parallel robots with different structural configurations and parameters. To evaluate the proposed model, a numerical case on this dataset collected by the inverse kinematics model of a typical 6‐DOF parallel robot is conducted, followed by the results manifesting that the binary discriminator with the discriminant accuracy of 88.50% is superior over ELM, KELM, support vector machine and logistic regression. The multi‐label regression model, with the root mean squared error of 0.06 mm for the position and 0.15° for the orientation, outperforms the double‐hidden‐layer back propagation (2‐BP), ELM, KELM and genetic algorithm‐optimised KELM. Furthermore, numerical cases of parallel robots with different structural configurations and parameters are compared with state‐of‐the‐art models. Moreover, these results of numerical simulation and experiment on the host computer demonstrate that the proposed model displays its high precision, high robustness and rapid convergence, which provides a candidate for the forward kinematics of parallel robots.

In view of the influence of aliasing noise on the effectiveness and accuracy of bearing fault diagnosis, a bearing fault diagnosis algorithm based on the spatial decoupling method of modified kernel principal component analysis (MKPCA) and the residual network with deformable convolution (DC‐ResNet) is innovatively proposed. Firstly, the Gaussian noise with different signal‐to‐noise ratios (SNRs) is added to the data to simulate the different degrees of noise in the actual data acquisition process. The MKPCA is used to project the fault signal with different SNRs in the kernel space to reduce the data dimension and eliminate some noise effects. Finally, the DC‐ResNet model is used to further filter the noise effects and fully extract the fault features through the training of the preprocessed data. The proposed algorithm is tested on the Case Western Reserve University (CWRU) and Xi'an Jiaotong University and Changxing Sumyoung Technology Co., Ltd. (XJTU‐SY) bearing data sets with different SNR noise. The fault diagnosis accuracy can reach 100% within 30 min, which has better performance than most of the existing methods. The experimental results show that the algorithm has an excellent effect on accuracy and computation complexity under different noise levels.

Accurately identifying defect patterns in wafer maps can help engineers find abnormal failure factors in production lines. During the wafer testing stage, deep learning methods are widely used in wafer defect detection due to their powerful feature extraction capabilities. However, most of the current wafer defect patterns classification models have high complexity and slow detection speed, which are difficult to apply in the actual wafer production process. In addition, there is a data imbalance in the wafer dataset that seriously affects the training results of the model. To reduce the complexity of the deep model without affecting the wafer feature expression, this paper adjusts the structure of the dense block in the PeleeNet network and proposes a lightweight network WM‐PeleeNet based on the PeleeNet module. In addition, to reduce the impact of data imbalance on model training, this paper proposes a wafer data augmentation method based on a convolutional autoencoder by adding random Gaussian noise to the hidden layer. The method proposed in this paper has an average accuracy of 95.4% on the WM‐811K wafer dataset with only 173.643 KB of the parameters and 316.194 M of FLOPs, and takes only 22.99 s to detect 1000 wafer pictures. Compared with the original PeleeNet network without optimization, the number of parameters and FLOPs are reduced by 92.68% and 58.85%, respectively. Data augmentation on the minority class wafer map improves the average classification accuracy by 1.8% on the WM‐811K dataset. At the same time, the recognition accuracy of minority classes such as Scratch pattern and Donut pattern are significantly improved.

Target tracking has a wide range of applications in intelligent transportation, real‐time monitoring, human‐computer interaction and other aspects. However, in the tracking process, the target is prone to deformation, occlusion, loss, scale variation, background clutter, illumination variation, etc., which bring great challenges to realize accurate and real‐time tracking. Tracking based on Siamese networks promotes the application of deep learning in the field of target tracking, ensuring both accuracy and real‐time performance. However, due to its offline training, it is difficult to deal with the fast motion, serious occlusion, loss and deformation of the target during tracking. Therefore, it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time online. The broad learning system (BLS) has a simple network structure, high learning efficiency, and strong feature learning ability. Aiming at the problems of Siamese networks and the characteristics of BLS, a target tracking method based on BLS is proposed. The method combines offline training with fast online learning of new features, which not only adopts the powerful feature representation ability of deep learning, but also skillfully uses the BLS for re‐learning and re‐detection. The broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on, so as to change the selection of the Siamese networks search area, solve the problem that the search range cannot meet the fast motion of the target, and improve the adaptability. Experimental results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.

Design change is an inevitable part of the product development process. This study proposes an improved binary multi‐objective PSO algorithm guided by problem characteristics (P‐BMOPSO) to solve the optimisation problem of complex product change plan considering service performance. Firstly, a complex product multi‐layer network with service performance is established for the first time to reveal the impact of change effect propagation on the product service performance. Secondly, the concept of service performance impact (SPI) is defined by decoupling the impact of strongly associated nodes on the service performance in the process of change affect propagation. Then, a triple‐objective selection model of change nodes is established, which includes the three indicators: SPI degree, change cost, and change time. Furthermore, an integer multi‐objective particle swarm optimisation algorithm guided by problem characteristics is developed to solve the model above. Experimental results on the design change problem of a certain type of Skyworth TV verify the effectiveness of the established optimisation model and the proposed P‐BMOPSO algorithm.