Search

Back-end optimization plays a key role in eliminating the accumulated error in Visual Simultaneous Localization And Mapping (VSLAM). Existing back-end optimization methods are usually premised on the Gaussian noise assumption which does not always hold true due to the non-convex nature of the image and the fact that non-Gaussian noises are often encountered in real scenes. In view of this, we propose a back-end optimization method based on Multi-Convex combined Maximum Correntropy Criterion (MCMCC). A MCMCC-based cost function is first tailored for nonlinear back-end optimization in the context of VSLAM and the optimization problem is solved through Levenberg–Marquardt algorithm iteratively. Then, the proposed method is applied to ORB-SLAM3 to test its performance on public indoor and outdoor datasets. The real time performance is also validated using a RaceBot platform in real indoor and outdoor environments. In addition, the reprojection error is statistically analyzed to demonstrate the non-Gaussian characteristics in the back-end optimization process. Finally, the suggestion parameters are also provided through experiments for further study. • The MCMCC-based cost function for VSLAM back-end optimization is designed and the optimization problem is then solved through Levenberg-Marquardt method iteratively. To our knowledge, this is the first time a ITL-based cost function is considered in nonlinear back-end optimization. • The proposed method is implemented in the visual mode in ORB-SLAM3 and a comprehensive analysis is performed by comparing the proposed method with other widely used robust kernel function-based methods in the context of VSLAM on EuRoC, TUM and KITTI datasets. In addition, the method is also tested in real environment. • The reprojection error in back-end is analyzed from the prospective of stochastic statistics to provide an insight on the back-end problem in non-Gaussian setting in VSLAM. • The suggestion parameters are provided as guidance for both indoor applications and outdoor applications. [ABSTRACT FROM AUTHOR]

With the era of big data, data-driven models are increasingly vital to just-in-time decision support in pollution emission management and planning. This article aims to evaluate the usability of the proposed data-driven model to monitor NOx emission from a coal-fired boiler process using easily measured process variables. As the emission process is highly complex, process variables interact with each other, and they cannot guarantee that all the variables in the actual operation obey the Gaussian distributions. As conventional principal component analysis (PCA) can only extract variance information, a novel data-driven model is proposed, called survival information potential-based PCA (SIP-PCA) model, is proposed in this work. First, an improved PCA model is established based on the SIP performance index. SIP-PCA can extract more information in the latent space from the process variables following the non-Gaussian distributions. Then, the control limits for fault detection are determined based on the kernel density estimation method. Finally, the proposed algorithm is successfully applied to a real NOx emission process. By monitoring the operation of process variables, possible failures can be detected as soon as possible. Fault isolation and system reconstruction can be implemented in time, preventing NOx emissions from exceeding its standard. [Display omitted] • Survival information potential based PCA (SIP-PCA) for fault detection is proposed. • The SIP-PCA method does not have any distribution assumptions and it is robust. • The SIP-PCA algorithm is successfully applied to the real NOx emission detection. • The timeliness and effectiveness of the NOx emission fault are detected. • SIP-PCA outperforms conventional algorithms. [ABSTRACT FROM AUTHOR]

The lack of tourism-specific tacit knowledge can prevent locals from fully participating in rural tourism development. Using a longitudinal case analysis, we explore how tacit knowledge transfer to locals can be facilitated and their capabilities can be enhanced. We find that experienced migrant workers from tourism firms can provide tacit knowledge specific to tourism operations. However, the absence of knowledge-related transaction activities and the lack of cross-group trust make the "migrant-local" knowledge transfer spontaneous, fragmented, and ineffective. We empirically demonstrate that an influential third party's intervention can promote tacit knowledge transfer by bearing transaction costs, initiating knowledge transfer plans, and coordinating relationships among participants. Finally, we summarize a generalized process by which different stakeholders jointly formalize tacit knowledge transfer practices, including identification, negotiation, implementation, and valuation. Overall, this study contributes to the theory and practice of knowledge management in rural tourism development. [ABSTRACT FROM AUTHOR]