This study addresses the prevalent challenges of inefficiency and suboptimal quality in indoor 3D scene generation and rendering by proposing a parameter-tuning strategy for 3D Gaussian Splatting (3DGS). Through a systematic quantitative analysis of various performance indicators under differing resolution conditions, threshold settings for the average magnitude of spatial position gradients, and adjustments to the scaling learning rate, the optimal parameter configuration for the 3DGS model, specifically tailored for indoor modeling scenarios, is determined. Firstly, utilizing a self-collected dataset, a comprehensive comparison was conducted among COLLI-SION-MAPping (abbreviated as COLMAP (V3.7), an open-source software based on Structure from Motion and Multi-View Stereo (SFM-MVS)), Context Capture (V10.2) (abbreviated as CC, a software utilizing oblique photography algorithms), Neural Radiance Fields (NeRF), and the currently renowned 3DGS algorithm. The key dimensions of focus included the number of images, rendering time, and overall rendering effectiveness. Subsequently, based on this comparison, rigorous qualitative and quantitative evaluations are further conducted on the overall performance and detail processing capabilities of the 3DGS algorithm. Finally, to meet the specific requirements of indoor scene modeling and rendering, targeted parameter tuning is performed on the algorithm. The results demonstrate significant performance improvements in the optimized 3DGS algorithm: the PSNR metric increases by 4.3%, and the SSIM metric improves by 0.2%. The experimental results prove that the improved 3DGS algorithm exhibits superior expressive power and persuasiveness in indoor scene rendering.

To improve the current indoor positioning algorithms, which have insufficient positioning accuracy, an ultra-wideband (UWB) positioning algorithm based on the Levenberg–Marquardt algorithm with improved Kalman filtering is proposed. An alternative double-sided two-way ranging (ADS-TWR) algorithm is used to obtain the distance from the UWB tag to each base station and calculate the initial position of the tag by the least squares method. The Levenberg–Marquardt algorithm is used to correct the covariance matrix of the Kalman filter, and the improved Kalman filtering algorithm is used to filter the initial position to obtain the final position of the tag. The feasibility and effectiveness of the algorithm are verified by MATLAB simulation. Finally, the UWB positioning system is constructed, and the improved Kalman filter algorithm is experimentally verified in LOS and NLOS environments. The average X-axis and the Y-axis positioning errors in the LOS environment are 6.9 mm and 5.4 mm, respectively, with a root mean square error of 10.8 mm. The average positioning errors for the X-axis and Y-axis in the NLOS environment are 20.8 mm and 18.0 mm, respectively, while the root mean square error is 28.9 mm. The experimental results show that the improved algorithm has high accuracy and good stability. At the same time, it can effectively improve the convergence speed of the Kalman filter.

Surface subsidence induced by coal mining poses severe threats to global ecosystems and infrastructure. A critical challenge in subsidence prediction lies in the sensitivity of existing probabilistic integral parameter inversion methods to gross errors, leading to unstable predictions and compromised reliability. To address this limitation, we propose the IGGIII-BFGS algorithm that integrates robust estimation with unconstrained optimization method, enhancing resistance to gross errors during parameter inversion. Through systematic comparison of four robust estimation methods (Huber, L1, Geman–McClure, IGGIII) fused with BFGS, the IGGIII-BFGS method demonstrated superior stability and accuracy, reducing relative errors in key parameters (subsidence factor q, horizontal displacement coefficient b, and tangent of major influence angle tan β) to near-zero levels. Validation on the Huainan mining case study showed that the IGGIII-BFGS method achieved a 25.8% reduction in subsidence RMSE compared to standard BFGS, with predicted curves exhibiting strong agreement with field measurements. This advancement enables precise forecasting of subsidence and horizontal displacement, which hold significant value for the sustainable development of the surface ecological environment and social stability.

Business process outcome prediction plays an essential role in business process monitoring. It continuously analyzes completed process events to predict the executing cases’ outcome. Most of the current outcome prediction focuses only on the activity information in historical logs and less on the embedded and implicit knowledge that has not been explicitly represented. To address these issues, this paper proposes a Deep Latent Factor Model Predictor (DLFM Predictor) for uncovering the implicit factors affecting system operation and predicting the final results of continuous operation cases based on log behavior characteristics and resource information. First, the event logs are analyzed from the control flow and resource perspectives to construct composite data. Then, the stack autoencoder model is trained to extract the data’s main feature components for improving the training data’s reliability. Next, we capture the implicit factors at the control and data flow levels among events and construct a deep implicit factor model to optimize the parameter settings. After that, an expansive prefix sequence construction method is proposed to realize the outcome prediction of online event streams. Finally, the proposed algorithm is implemented based on the mainstream framework of neural networks and evaluated by real logs. The results show that the algorithm performs well under several evaluation metrics.

Relying only on the cost function to calculate the optimal alignment between the model and the log may cause the alignment results to fail to accurately reflect the deviation between the actual process and the modeled process. In order to solve this problem, an optimal alignment selection strategy combining conformance log probability distribution and the cost function is proposed. Firstly, a log probability automata is constructed based on the conformance event log. Then, all possible execution sequences of the model are obtained and pre-aligned with the non-conformance log based on the cost function. Finally, all asynchronous moving left and right associative event pairs in pre-alignment are determined, and the average confidence value of the direct following dependency relationship of these event pairs is obtained according to the log probability automata. The concept of credibility value is proposed by combining the average confidence value with the pre-aligned cost value, and the alignment with the highest credibility value is considered as the optimal alignment. In the evaluation section, a large number of alignment results are evaluated, and the results show that compared with the existing two alignment methods that only based on cost function, the average alignment accuracy of the proposed method is improved by 5.55% and 10.97% respectively under various noises, and the alignment accuracy is the most stable.