The layout of LED lamps plays an important role in indoor visible light communication systems, especially the uniformity of received optical power on the communication plane. In order to search an optimized layout that provides better communication performance, an improved artificial fish swarm algorithm (IAFSA) is proposed. In IAFSA, an artificial fish denotes the position of an LED that includes a pair of abscissa and ordinate. The order method based on exchanging list during the action of preying increases the population diversity, and adopt of an adaptive moving during the action of random move enhances the ability of global search and search efficiency, which ensures the convergence of IAFSA. Simultaneously, we establish objective function based on the mean square error of received optical power distribution for optimizing layout. Comparing the square layout, rectangular layout and circular layout with the IAFSA optimized layout, the power uniformity augments increases by 39.8%, 54.4% and 40.1%, respectively. The experiment results show that IAFSA is a feasible and valid intelligent optimization algorithm, and it can realize speedy optimized layout.

This study presents a novel Bayesian-K Nearest Neighbor (B-KNN) fusion algorithm aimed at mitigating dynamic positioning errors in navigation systems that utilize visible light communication. The proposed fusion methodology leverages a fingerprint database constructed from the received signal strength data acquired by a mobile receiver. Through the selection of the nearest navigation path points for clustering and the utilization of a Bayesian algorithm with Gaussian fitting, the posterior probability of each cluster is computed to facilitate the prediction of the location of navigation path points via fingerprint matching. The results indicate that 89.8% of simulations exhibit a dynamic positioning error below 1 m within a 20 m × 5 m × 4 m model, achieving a success rate of 89.8%. Comparative analysis reveals that the B-KNN fusion algorithm enhances dynamic positioning accuracy by 68.3% while reducing computational overhead by approximately 48.3% in contrast to conventional KNN algorithms. Experimental assessments demonstrate that in scenarios involving ambient light interference within a 2 m × 1 m × 1.5 m model, the majority (95%) of dynamic positioning errors exceed 9 cm, with a significant portion surpassing 10 cm. Conversely, under conditions devoid of ambient light interference, only 4% of errors exceed 9 cm, with 14% falling below 5 cm, indicative of a marked 39.81% improvement in accuracy.

Controllability reflects the ease that a process can be controlled in practical operating environment. However, an unclear influence between the HEN synthesis and the control structure selection has been not investigated for the work of controllability of heat exchanger network (HEN). To address this challenge, this paper proposes a multi-objective optimization method by considering both the quantitative measures of economic and controllability, i.e., minimizing the total annual cost (TAC) and the relative gain array number (RGAn). This method is developed using a HEN synthesis procedure where a model-based superstructure is employed to involve the set of the network configuration alternatives and all the potential control structures. The effects of minimum approach temperature (ΔT_min) on the multi-objective optimization problem are investigated to distinguish the consistent and opposite variations of TAC and RGAn. The consistent change enables us to solve the single objective optimization problem for economical HEN design as well as for taking controllability into account. The opposite change prompts the Pareto front of the two objectives in order to develop a trade-off strategy. Results indicate that this method helps in the determination of the relationship-based nature between network configuration and control structure to yield a HEN design with economic and controllability considerations.

In recent years, substantial progress has been achieved in addressing cross-domain fault diagnosis problems through knowledge transfer strategies. However, traditional studies usually assume that target data are provided to facilitate domain adaptation throughout the training process, which is often infeasible in practical engineering applications, where fault data are often scarce. Therefore, extracting general and effective diagnostic leveraged knowledge from the source domain and applying it to unknown target tasks are crucial for intelligent cross-domain fault detection. To this end, this paper introduces a curvature-sensitive regulation strategy (CSRS) for cross-domain generalization of domain-invariant features in bearing fault diagnosis. To ensure high diagnostic accuracy in previously unobserved domains, this study proposes a model that converges to a smooth region with low loss values, allowing for the extraction of domain-invariant features, distinguishing features from source domains that may differ, which can be maintained in new target domains without requiring the presence of fault data. The effectiveness, superiority, and robustness of the CSRS method have been confirmed in generalization tasks across various fault diagnosis domains.

Microalgae have attracted great research interest as a feedstock for producing a wide range of end-products. However, recent studies show that the tight processing integration technology for microalgae-based biorefinery makes production less economical and even has a negative impact on sustainability. In this study, a new two-tier superstructure optimization design methodology is proposed to locate the optimal processing pathway. This model is developed based on the decomposition strategy and the relationship-based investigation, coupling an outer-tier structure with an inner-tier structure, wherein the outlet flows of the middle stages is relaxed and then an appropriate level of redundancy for designing the processing is provided. Two scenarios are developed to compare the most promising biorefinery configurations under two different design option favors. By solving the mixed integer nonlinear programming model with the objective functions of maximizing the yield of the desired products and maximizing the gross operating margin, the optimization results obtained show the ability of this framework to provide the promising configurations and cost-effectiveness of microalgae-based biorefinery. Compared with Scenario 1, the optimized solutions in Scenario 2 feature a gross operating margin increase up to 27.09% and an increase in product yield up to 25.00%. The proposed method improves the original huge computing scale and ensures economics without simplifying the processing pathways.

Improving mass transfer in gas diffusion layers is critical to achieving high-performance proton-exchange membrane fuel cells (PEMFCs). Leaks through the interface between the gas and the membrane electrode assembly frame have been widely investigated, and the controllability of the cathode gas diffusion has not been achieved in most studies. In this study, we develop a structural parameter to investigate the controllability of the gas diffusion mechanism in the cathode in order to improve upon the design and performance of PEMFCs. This parameter accounts for the cathode gas diffusion layer porosity and carbon loading inside the catalyst layer. It is comprehensively calculated to relax the two segments’ distribution along three directions of the coordinate axis. The experimental and simulation results show that the obtained values of the parameter vary and change during voltage stabilization. According to the results, regardless of the materials in the cathode gas diffusion layer, the same steady-state voltage is obtained when the parameter is fixed. The cell could be controllably operated for a wide range of diffusion layer thicknesses by selecting the optimal parameter.