Parallel assembly sequence planning (PASP) greatly impacts on efficiency of assembly process. In traditional methods, large scale of matrix calculation still limits efficiency of PASP for complex products. A novel PASP method is proposed to address this issue. To avoid matrix calculation, the synchronized assembly Petri net (SAPN) is firstly established to describe the precedence relationships. Associated with the SAPN model, the PASP process can be implemented via particle swarm optimization based on bacterial chemotaxis (PSOBC). Characterized by an attraction-repulsion phase, PSOBC not only prevents premature convergence to a high degree, but also keeps a more rapid convergence rate than standard particle swarm optimization (PSO) algorithm. Finally, feasibility and effectiveness of the proposed method are verified via a case study. With different assembly parallelism degrees, optimization results show that assembly efficiency of the solution calculated by PSOBC method is 9.0, 4.2, and 3.1 better than the standard PSO process.

On active bending structures, the actuation direction and the excitation field direction are not the same. Simple lumped parameter models are inadequate to describe the relationship between output displacement and input field. In this paper, a dynamic distributed parameter model is presented to describe the system dynamics of a galfenol bending actuator. To consider nonlinearities and hysteresis in bending, a nonlinear magnetomechanical model is developed to characterize the hysteretic magnetostriction generated by the galfenol layer. A dynamic real-time control strategy is proposed to compensate for hysteresis. A nonlinear inverse filter is constructed to linearize the hysteresis based on the proposed distributed parameter model. In order to increase the calculation efficiency, a new iteration method is proposed to calculate the filter. The iteration stepsize of the input field can be adaptively updated according to the inverting error. Simulation results show that significant enhancement of convergence efficiency can be achieved by using the proposed method compared with the existing fixed step size method. Experiments have been conducted to verify the real-time control strategy.

Non-intrusive load monitoring is a significant advancement in energy conservation and smart electricity usage as it enables the identification of load status and equipment type without the need for extensive sensing instruments. As one of the important steps of non-intrusive load monitoring, non-intrusive load identification plays a crucial role in the correct identification of electrical appliances. In this paper, a novel approach is presented to amalgamate multiple appliance features into a new identification feature, which is then used for appliance recognition through an improved lightweight deep learning model. This addresses the issue of low accuracy when using a single feature for appliance identification. The core concept involves initially capturing the current data of an appliance during steady state operation and fusing the image features of the appliance encoded by Gramian summation angular field, Gramian difference angular field, and Markov transition field into new recognition features for each appliance using average weighting. Subsequently, a shufflenetv2 lightweight deep learning model based on the squeeze -and-excitation module is used to mine the constructed load feature information for the load classification task. The method is then experimentally validated on the Self-test Dataset, PLAID, and WHITED dataset, resulting in recognition accuracies of 100%, 98.214%, and 99.745%, respectively. These results demonstrate the effectiveness of the proposed method in significantly improving recognition performance compared to the original approach.