Optimization Design of Electrodes for Anode-Supported Solid Oxide Fuel Cells via Genetic Algorithm

Porous electrode is the critical component of solid-oxide fuel cells (SOFCs) and provides a functional material backbone for multi-physicochemical processes. Model based electrode designs could significantly improve SOFC performance. This task is usually performed via parameter studies for simple case and assumed property distributions for graded electrodes. When nonlinearly coupled multiparameters of electrodes are considered, it could be very difficult for the model based parameter study method to effectively and systematically search the design space. In this research, the optimization approach with a genetic algorithm is demonstrated for this purpose. An anode-supported proton conducting SOFC integrated with a fuel supply system is utilized as a physical base for the model development and the optimization design. The optimization results are presented, which are difficult to obtain for parametric study method

WCCNet: Wavelet-integrated CNN with Crossmodal Rearranging Fusion for Fast Multispectral Pedestrian Detection

Multispectral pedestrian detection achieves better visibility in challenging conditions and thus has a broad application in various tasks, for which both the accuracy and computational cost are of paramount importance. Most existing approaches treat RGB and infrared modalities equally, typically adopting two symmetrical CNN backbones for multimodal feature extraction, which ignores the substantial differences between modalities and brings great difficulty for the reduction of the computational cost as well as effective crossmodal fusion. In this work, we propose a novel and efficient framework named WCCNet that is able to differentially extract rich features of different spectra with lower computational complexity and semantically rearranges these features for effective crossmodal fusion. Specifically, the discrete wavelet transform (DWT) allowing fast inference and training speed is embedded to construct a dual-stream backbone for efficient feature extraction. The DWT layers of WCCNet extract frequency components for infrared modality, while the CNN layers extract spatial-domain features for RGB modality. This methodology not only significantly reduces the computational complexity, but also improves the extraction of infrared features to facilitate the subsequent crossmodal fusion. Based on the well extracted features, we elaborately design the crossmodal rearranging fusion module (CMRF), which can mitigate spatial misalignment and merge semantically complementary features of spatially-related local regions to amplify the crossmodal complementary information. We conduct comprehensive evaluations on KAIST and FLIR benchmarks, in which WCCNet outperforms state-of-the-art methods with considerable computational efficiency and competitive accuracy. We also perform the ablation study and analyze thoroughly the impact of different components on the performance of WCCNet.Comment: Submitted to TPAM