Ziyan Zhu

Addressing the problems of complex forms, low structurization and insufficient reliability of automatic acquisition of professional knowledge sources in the manufacturing industry, this paper proposes an improved multi-person collaborative knowledge acquisition method for professional fields. Drawing on the quality control concept of “three reviews and three proofs” in the publishing industry and combining the characteristics of professional knowledge acquisition tasks, this method constructs a knowledge acquisition process with the collaborative participation of editors and professionals. This paper designs a quality assurance mechanism from three dimensions, namely personnel quality, process quality and result quality; introduces triangular fuzzy numbers to evaluate personnel quality; and establishes a process quality control model under multi-level inspection. Taking the knowledge acquisition project of CNC Machining Manual as an example, 39 professionals completed large-scale professional knowledge processing tasks within 60 working days. Compared with the traditional manual knowledge acquisition method, under similar workload conditions, the proposed method reduces the task completion time by approximately 40% and improves knowledge quality by approximately 10%. The research results show that this method can enhance the organization, inspectability and result stability of the complex professional knowledge acquisition process, and is suitable for constructing vertical domain knowledge bases with high quality requirements.

Camouflage design necessitates region-consistent colour and texture synthesis. However, practical datasets are frequently constrained by scarce paired supervision and cross-source distribution shift, which impede reliable generalisation across heterogeneous environments. To address this problem, we propose a region-feature-driven framework for camouflage generation from unpaired background sequences. The main framework-level contribution lies in constructing a decoupled sequence-conditioned regional representation and using it to drive a fused-background-first camouflage-generation pipeline under unpaired supervision. Specifically, each background sequence is encoded into a continuous region descriptor and combined with a discrete Environment × Season code; under unpaired supervision, this decoupled condition guides StyleGAN2-based fused-background synthesis via style modulation, after which a deterministic digital-camouflage renderer converts the synthesised fused background into a deployable pattern. On a stratified test split, the synthesised backgrounds achieve FID = 15.2 ± 0.5, KID = 4.7 ± 0.3 × 10⁻³, while also showing stable condition adherence under the tested settings. Auxiliary sequence-consistency proxy measures are PSNR = 34.56 dB, SSIM = 0.776, and LPIPS = 0.134. Under a paired forest-scenario detector protocol, the generated camouflage reduces mean image-best IoU from 0.716 to 0.400 for a one-stage detector and from 0.690 to 0.460 for a two-stage detector. Overall, these results support the effectiveness of the proposed condition design and transfer mechanism under the present experimental setting.

On-machine measurement (OMM) enables real-time dimensional feedback in production, yet accuracy is often degraded by thermal drift, sensor noise, and environmental disturbances. This motivates intelligent error-prediction methods to ensure reliable, high-precision machining. This study proposes a hybrid deep learning model integrating a Convolutional Neural Network (CNN), a Bidirectional Long Short-Term Memory (Bi-LSTM) network, and a Whale Optimization Algorithm (WOA) for precise OMM error prediction. Initially, raw measurement data underwent preprocessing to remove noise and outliers. Subsequently, we use a CNN to extract features and a Bi-LSTM to model time-dependent patterns. Finally, WOA optimizes model hyperparameters globally, further boosting predictive accuracy. Comparative experiments show that the proposed model reduces RMSE, MAE, and MAPE by approximately 53.58%, 54.96%, and 57.65%, respectively, while improving the R² score by about 11.17% over baseline methods. Results confirm the method’s superior nonlinear prediction capabilities, significantly enhancing machining accuracy and production efficiency, and demonstrating promising industrial application potential.