Haiming Liu

Due to the complexity of multi-objective engineering problems, solutions obtained by many algorithms often exhibit poor distribution, and the algorithms tend to fall into local optima. To effectively alleviate these issues, an improved multi-objective moth–flame optimization algorithm (IETMFO) is proposed in this paper, with three core novelties: A hybrid mutation mechanism (integrating two mutation techniques) is first used to generate a new population, and then an indicator-based selection mechanism is adopted to produce a high-quality initial population, enhancing solution distribution. Enhanced Brownian motion is introduced as a local search strategy to reduce the risk of falling into local optima. An improved flame update mechanism is incorporated to maintain flame diversity, boosting the algorithm’s adaptability. The IETMFO is tested on 49 benchmark functions and 6 constrained engineering problems, and then compared with seven well-known algorithms (including NSGA-II, MOEA/D, and traditional MFO). The experimental results show the following: in benchmark function tests, IETMFO reduces the IGD value by an average of 32.7% and increases the HV value by an average of 28.5% compared with NSGA-II; on ZDT series functions, it outperforms the seven contrast algorithms in solution distribution uniformity; in the six engineering problems, its optimal solution proportion reaches 66.7%, and the risk of falling into local optima is reduced by 41.2%. These results demonstrate that the IETMFO achieves competitive performance in addressing multi-objective engineering problems.

Point cloud registration aims to find a rigid transformation that aligns two point clouds. Applications such as augmented reality (AR) and robot navigation often require real-time performance for point cloud registration algorithms. In this paper, we propose SGUformer, a novel point cloud registration method that achieves fast alignment by redesigning the feature extraction pipeline and employing a lightweight global feature extraction framework. The gating mechanism is utilized and local coordinates are embedded to enhance the representation of point-level features. To facilitate the extraction of global features, a transformer with 3D rotary position embedding is implemented, circumventing the need to compute relative position information, thereby improving computational efficiency. Furthermore, a part attention mechanism is designed to tackle outlier pollution issues. In the final registration stage, the registration results obtained from each patch pair, weighted by their respective confidence scores, are combined to vote and acquire a more robust final result. In the conducted experiment, the superior quality of features derived from the novel structure’s feature extractor enabled our method to attain a better Feature Matching Recall (FMR) in comparison to existing leading methodologies. Moreover, the implementation of the proposed registration method resulted in the highest recorded registration success rate, exceeding the second-best method by 0.8%. Additionally, our approach demonstrated a remarkable efficiency, being 26% faster than the alternative methods.

In electronic manufacturing, anomaly detection of surface mount devices (SMDs) through computer vision (CV) is an important task to control the production quality of SMDs. The difficulty of the detection is that some anomalous regions on the surfaces of SMDs are very minor and with variable shapes, which leads to poor detection efficiency. To solve this problem, based on the assumption that normal samples can be reconstructed more accurately than anomalous samples, a self-supervised image anomaly detection framework with a multiscale two-branch feature fusion strategy is proposed. Specifically, it adopts autoencoder (AE) as the basic framework, and to enhance the reconstruction error between input anomalous samples and the reconstructed ones, a self-supervised learning task of reconstructing images is introduced to have the model neglect the encoding of the suspected anomalous regions found by a contextual attention mask (CAM) module. Meanwhile, a multiscale feature fusion strategy is developed to fuse texture and structure features in the decoder to reconstruct samples. Moreover, a multilevel anomalous score criterion is proposed to enlarge the scores for the samples with very minor anomalies. At last, an SMD-capacitor anomaly detection dataset (SMDC-DET) is built to evaluate the proposed method. The experiments show that the proposed method achieves an average area under the curve (AUC) accuracy of 98.82%, much better when compared to the start-of-the-art existing anomaly detection methods.

This paper studies the coordination of a heterogenous flywheel energy storage matrix system aiming at simultaneous reference power tracking and state-of-energy balancing. It is first revealed that this problem is solvable if and only if the state-of-energy of all the flywheel systems synchronize to a common time-varying manifold governed by a nonautonomous dynamic system. Next, by treating this nonautonomous dynamic system as an external model, the coordination problem can be decoupled into two separate problems, namely, the global double layer estimation problem and the local tracking problem. Then, a distributed control scheme is proposed to solve the coordination problem by integrating the adaptive distributed observer approach and the certainty equivalence control method. Comprehensive case studies are provided to show the performance of the proposed control scheme.

In this paper, we discuss the process of low-volume and high-mix PCB assembly production in the electronic assembly industry and aim at an effective scheduling method to determine assembly sequence of different types of PCBs to reducing whole production time. The optimization problem can be modeled as an approximate traveling salesman problem (TSP) when considering the effect of machine setup time on whole production time. An optimization algorithm based on scatter search method is proposed to search for good solutions to minimize machine setup time. The simulation results show that the proposed algorithm can output good scheduling solutions.

This paper analyzes optimization algorithms of assembly time for a multi-head mounter. A step-by-step heuristic optimization algorithm is proposed to minimize the PCB assembly time for a multi-head mounter. By relaxing the restrictions on the problem and employing a flexible approach for feeder and head assignment, the algorithm reduces the assembly time by minimizing cycles of pick and placement, constructing the simultaneous pickup and implementing the nearest placement. The production results show that the proposed algorithm has good performance in practice.