A capillary electrophoretic method for the separation of four flavones in Crataegus pinnatifida is developed. The four flavones in Crataegus pinnatifida are separated on baseline within 15 min using 50mM borax buffer containing 15% acetonitrile and adjusted to pH 8.15 with phosphoric acid. The detection limits of vitexin-2″-rhamnoside, hyperside, rutin, and vitexin are 0.35, 0.30, 0.40 and, 0.29 µg/mL, respectively. The recovery of these flavones is as follows: vitexn-2″-rhamnoside 96.8%, hyperside 99.9%, rutin 97.1%, and vitexin 97.8%. The results are in accordance with those obtained in the high-performance liquid chromatography system. The content of flavones is higher in Crataegus pinnatifida leaves than in its fruits, and hyperside is not detected in either Crataegus pinnatifida fruits or flowers.

The cooling system directly impacts the power output of an engine. The cooling water flow and temperature in the heat exchange process outside the engine exhibit dynamic characteristics of nonlinear, time-varying, and solid coupling, precise control algorithms to optimize the coordination mechanism of the pump and valve with electronic control are crucial factors for the cooling system to handle thermal loads. This paper presented a feedforward compensation decoupling control and schedules the fuzzy proportional-integral-differential (FPID) controller optimized through an improved particle swarm algorithm for the engine cooling system. Primarily, construct the mathematical model of the cooling system’s heat exchange by gathering relevant data, and the feedforward compensation decouplers are designed to diminish the coupling extent of cooling water flow and temperature in the external cooling cycle. Next, create the whole control system model in MATLAB/Simulink and perform functional simulation by incorporating particle swarm optimization (PSO) algorithm with the dynamic adjustment strategy of stochastic inertia weight to optimize the FPID controller’s quantization and proportionality factors. Eventually, the approach’s feasibility is validated on the existing physical experimental platform. The findings demonstrate that this proposed improved control and optimization method is expected to enhance the water flow regulation characteristics as well as temperature control precision of the cooling water and strengthen the stability of the heat exchange efficiency of the cooling system.

Existing dehazing methods deal with real-world haze images with difficulty, especially scenes with thick haze. One of the main reasons is lacking real-world pair data and robust priors. To improve dehazing ability in real-world scenes, we propose a semi-supervised codebook learning dehazing method. The codebook is used as a strong prior to guide the hazy image recovery process. However, the following two issues arise when the codebook is applied to the image dehazing task: (1) Latent space features obtained from the coding of degraded hazy images suffer from matching errors when nearest-neighbour matching is performed. (2) Maintaining a good balance of image recovery quality and fidelity for heavily degraded dense hazy images is difficult. To reduce the nearest-neighbor matching error rate in the vector quantization stage of VQGAN, we designed the unit dual-attention residual transformer module (UDART) to correct the latent space features. The UDART can make the latent features obtained from the encoding stage closer to those of the corresponding clear image. To balance the quality and fidelity of the dehazing result, we design a haze density guided weight adaptive module (HDGWA), which can adaptively adjust the multi-scale skip connection weights according to haze density. In addition, we use mean teacher, a semi-supervised learning strategy, to bridge the domain gap between synthetic and real-world data and enhance the model generalization in real-world scenes. Comparative experiments show that our method achieves improvements of 0.003, 2.646, and 0.019 over the second-best method for the no-reference metrics FADE, MUSIQ, and DBCNN, respectively, on the real-world dataset URHI.

3D point cloud registration is a critical technology in the fields of visual measurement and robot automation processing. In actual large-scale industrial production, the accuracy of point cloud registration directly affects the quality of automated welding processes. However, most existing methods are confronted with serious challenges such as the failure of partial-to-partial point cloud model registration when facing robot automatic processing guidance and error analysis work. Therefore, this paper proposes a novel two-stage network architecture for point cloud registration, which aims at robot pose adjustment and visual guidance in the field of automated welding by using 3D point cloud data. Specifically, we propose a neighborhood-based multi-head attention module in the coarse registration stage. The neighborhood information of each point can be aggregated through focusing on different weight coefficients of multi-head inputs. Then the spatial structure features which is used to establish the overlapping constraint of point clouds are obtained based on above neighborhood information. In the fine registration stage, we propose the similarity matching removal module based on multiple attention fusion features to explore deeper features from different aspects. By using deep fusion features to guide the similarity calculation, the interference of non-overlapping points is removed to achieve the finer registration. Eventually, we compare and analyze the proposed method with the SOTA ones through several error metrics and overlap estimation experiments based on the ModelNet40 dataset. The test results indicate that our method, relative to other mainstream techniques, achieves lower error rates and the most superior accuracy of 98.61% and recall of 98.37%. To demonstrate the generalization performance of proposed algorithm, extensive experiments on the Stanford 3D Scanning Repository, 7-Scenes and our own scanning dataset using partially overlapping point clouds individually under clean and noisy conditions show the validity and reliability of our proposed registration network.

Failures of Unmanned Aerial Vehicle (UAV) can lead to property damage and casualties, making it imperative to preemptively diagnose failures. Most existing methods do not adequately address the temporal consistency of flight sequence data and often overlook the issue of data imbalance among various fault types. To address these shortcomings, this paper proposes an end-to-end model (CCTFANet) based on the Gated Recurrent Unit (GRU) that ensures temporal consistency of sequence data by maintaining overall causality of the model. Specifically, dual-path causal convolution is used for initial feature extraction from enhanced temporal signals to capture local features at different scales. Subsequently, a temporal feature fusion attention mechanism is introduced as an interactive bridge linking features of different scales, modeling channel dependencies. The GRU is employed for temporal modeling and global feature extraction of fused features to capture long-term dependencies, while maintaining temporal consistency of features in the final stage to ensure the overall causality of the model. This study, utilizing the ALFA dataset, employs time warping and dynamic step sliding window techniques to reconstruct the dataset and alleviate the data imbalance problem. The effectiveness and robustness of the proposed method are validated on the reconstructed dataset. Experimental results demonstrate that the fault diagnosis model can effectively identify UAV faults with a precision of 96.93%. Even under varying noise levels, the proposed method maintains high diagnostic accuracy.