Modern surface mount circuit board assemblies require more advanced defect detection methods. While deep learning algorithms have great potential for PCBA inspection, their detection accuracy in complex background situations is still limited. To overcome this problem, we propose a deep learning-based PCBA detection model (PCB-Net) to achieve accurate classification and localization of components on PCB. Firstly, for similar objects under complex background interference, this paper proposes a backbone network consisting of a generalized efficient aggregation network and a context converter to effectively extract local and global information. This integration aims to enhance the expressiveness of the network. Secondly, a multi-scale attention mechanism is designed to improve the feature extraction ability of the network on the target and suppress the interference of complex backgrounds. Finally, a C2fHB lightweight module was designed to improve the model's extraction of component features using the HorNet structure. Experimental results show that our proposed model is an effective PCBA detection method, as it can accurately detect tiny components in complex backgrounds, efficiently obtain component class and location information, and remain detection efficiency.

To meet the growing demands in the electronics manufacturing industry for smaller, more reliable, and flexible component packages, this research introduces a novel algorithm designed to automatically measure the geometric parameters of lead components. The algorithm can address the challenges associated with measuring these parameters in surface mount technology (SMT), overcoming the inefficiencies and inaccuracies of traditional manual measurement methods. This innovative approach combines an adaptive threshold segmentation algorithm with a multilevel grayscale range, enabling robust binary segmentation that adapts to variations in illumination. Additionally, by employing conditional filtering and state tracking, the algorithm facilitates the extraction of lead groups and individual leads, allowing for precise evaluation of component parameters through sub-pixel interpolated fitting. Extensive testing under various illumination conditions across multiple types of packaged and standard components has confirmed the algorithm's high accuracy and robustness. These results highlight its efficacy as a measurement solution suitable for industrial-grade applications.

This paper presents an improved version of the stochastic recurrent networks (STORN) for identification of non-linear state space systems with more complex model structures, including long short-term memory network (LSTM) and bidirectional gated recurrent unit (BiGRU). Both LSTM and BiGRU are recurrent neural networks with multiple state variables, which can store different information in the modeling of sequential data. Applying such a priori information into the prediction of data distribution can lead to better model performance. In this paper, several information fusion techniques are compared, and the effectiveness of the method is verified on three benchmark identification datasets. Our model outperforms the state-of-the-art baseline by 0.3 percent with 8 times fewer parameters.

Identification of nonlinear dynamic systems remains challenging nowadays. Although the nonlinear autoregressive with exogenous input (NARX) model is flexible to describe complex nonlinear behaviors, it is critical to select appropriate model terms to obtain a parsimonious description of the system. In this article, a variational Bayesian (VB) approach to the estimation of NARX systems is developed. A sparsity-inducing prior is introduced for model parameters, and the sparseness can be automatically determined by the weighting factor of such prior. The Bayesian model for the identification problem is constructed, and an iterative model pruning strategy is formulated to remove redundant terms and address the structure selection problem. Instead of the single-point estimation, the model parameters with their uncertainties are jointly estimated under the VB framework. Finally, one numerical example and several benchmark datasets are adopted to illustrate that the developed algorithm can work promisingly.

In this paper, we consider the identification problem for nonlinear state-space models with skewed measurement noises. The generalized hyperbolic skew Student’s t (GHSkewt) distribution is employed to describe the skewed noises and formulate the hierarchical model of the considered system. A unified framework for estimating unknown states and model parameters is presented based on expectation-maximization (EM) algorithm, in which the forward filtering backward simulation with rejection sampling (RS-FFBSi) is employed to efficiently estimate the smoothing densities of the hidden states, and optimization method is adopted to update model parameters. One numerical study and the electro-mechanical positioning system (EMPS) are employed to verify the effectiveness of the developed approach.