Selective assembly is an important step during the manufacturing process for a complex product to meet precision requirements. The high precision requirement of complex product often results in producing a large number of surplus components in selective assembly process current practices. In this paper, we develop a comprehensive model which permits the integration of machining process parameters design, variation analysis and fault diagnosis, process adjustment and control strategy, process capability index calculation and matchable degree calculation. The model can be applied to evaluate and improve product, process and system design at early development stages, or support the improvement of matchable degree for a complex production selective assembly process. In this model, we have introduced a navel concept of general matchable degree for a multiple components selective assembly process. The implementation procedure and functional modules of the proposed model are given in details with an industrial case presented to illustrate the implementation of the proposed method.

As an advanced detection technique, electrical resistive tomography (ERT) has been applied to detect the solid–liquid two-phase flow velocity based on available ERT measurements. The flow velocity computation by ERT must depend on the relative algorithms, including both the cross-correlation (CC) principle and convolutional neural networks (CNNs). However, these two types of algorithms have poor accuracy and generalization under complex measuring conditions and various flow patterns. To address this issue, in this paper, a hybrid network is proposed that combines a CNN with a reproducing kernel-based support vector machine (RKSVM) technique. The features hidden in ERT measurements are extracted using the CNN, and then the flow velocity is computed by the RKSVM in a high-dimensional feature space. According to the ERT measurements in an actual experimental platform, the results show that the hybrid network has higher accuracy and generalization ability for flow velocity computation compared with the existing CC, RKSVM, and CNN methods.

It is a great challenge for wireless sensor network to provide enough information for targets localization due to the limits on application environment and its nature, such as energy, communication, and sensing precision. In this paper, a multiple targets localization algorithm with sparse information (MTLSI) was proposed using compressive sensing theory, which can provide targets position with incomplete or sparse localization information. It does not depend on extra hardware measurements. Only targets number detected by sensors is needed in the algorithm. The monitoring region was divided into a plurality of small grids. Sensors and targets are randomly dropped in grids. Targets position information is defined as a sparse vector; the number of targets detected by sensor nodes is expressed as the product of measurement matrix, sparse matrix, and sparse vector in compressive sensing theory. Targets are localized with the sparse signal reconstruction. In order to investigate MTLSI performance, BP and OMP are applied to recover targets localization. Simulation results show that MTLSI can provide satisfied targets localization in wireless sensor networks application with less data bits transmission compared to multiple targets localization using compressive sensing based on received signal strengths (MTLCS-RSS), which has the same computation complexity as MTLIS.

Electrical tomography sensors have been widely used for pipeline parameter detection and estimation. Before they can be used in formal applications, the sensors must be calibrated using enough labeled data. However, due to the high complexity of actual measuring environments, the calibrated sensors are inaccurate since the labeling data may be uncertain, inconsistent, incomplete, or even invalid. Alternatively, it is always possible to obtain partial data with accurate labels, which can form mandatory constraints to correct errors in other labeling data. In this paper, a semi-supervised fuzzy clustering algorithm is proposed, and the fuzzy membership degree in the algorithm leads to a set of mandatory constraints to correct these inaccurate labels. Experiments in a dredger validate the proposed algorithm in terms of its accuracy and stability. This new fuzzy clustering algorithm can generally decrease the error of labeling data in any sensor calibration process.

Current methods for analyzing dynamic brain connectivity assume global synchrony, overlooking the inherent asynchrony and hierarchical nature of neural processes, thereby leading to incomplete or biased interpretations of brain dynamics. To address these limitations, we propose Spider-Brain, a spike-inspired dynamic graph structural learning (GSL) framework that models the brain as an evolving system with complex temporal organization. Spider-Brain incorporates a spike-inspired selective activation mechanism in which node updates occur through region-specific, event-triggered propagation, effectively capturing heterogeneous temporal responses across brain regions. By combining a multiscale temporal sampling strategy with a selective state-space model (SSM), the framework encodes both short-term neural fluctuations and long-range temporal dependencies, enabling explicit modeling of hierarchical temporal structure. Experiments on two real-world psychiatric datasets show that Spider-Brain consistently outperforms state-of-the-art baselines in disorder classification and provides interpretable activation patterns. The framework also uncovers disorder-specific connectivity and regional activation signatures that align with established clinical and neuroscientific findings. This demonstrates its ability to identify interpretable patterns of brain connectivity and temporal activity. Our code is available at https://github.com/mjliu99/Spider-Brain.