Search

The failure of insulators may seriously threaten the safe operation of the power system, where the state detection of high-voltage insulators is a must for the normal and safe operation of the power system. Based on the data of insulators in aerial images, this work explored an enhanced particle swarm algorithm to optimize the parameters of the support vector machine. A support vector machine model was therefore established for the identification of the normal and defective states of the insulators. This methodology works with the structure minimization principle of SVM and the characteristics of particle swarm fast optimization. First, the aerial insulator image was segmented as a target by way of the seed region growth based on double-layer cascade morphological improvements, and then, HOG features plus GLCM features were extracted as sample data. Finally, an ameliorated PSO-SVM classifier was designed to realize insulator state identification. Comparisons were made between PSO-SVM and conventional machine learning algorithms, SVM and Random Forest, and an optimization algorithm, Gray Wolf Optimization Support Vector Machine (GWO-SVM), and advanced neural network CNN. The experimental results showed that the performance of the algorithm proposed in this paper touched the top level, where the recognition accuracy rate was 92.11%, the precision rate 90%, the recall rate 94.74%, and the F1-score 92.31%.

Aiming at the common problems, such as noise pollution, low contrast, and color distortion in underwater images, and the characteristics of holothurian recognition, such as morphological ambiguity, high similarity with the background, and coexistence of special ecological scenes, this paper proposes an underwater holothurian target-detection algorithm (FA-CenterNet), based on improved CenterNet and scene feature fusion. First, to reduce the model's occupancy of embedded device resources, we use EfficientNet-B3 as the backbone network to reduce the model's Params and FLOPs. At the same time, EfficientNet-B3 increases the depth and width of the model, which improves the accuracy of the model. Then, we design an effective FPT (feature pyramid transformer) combination module to fully focus and mine the information on holothurian ecological scenarios of different scales and spaces (e.g., holothurian spines, reefs, and waterweeds are often present in the same scenario as holothurians). The co-existing scene information can be used as auxiliary features to detect holothurians, which can improve the detection ability of fuzzy and small-sized holothurians. Finally, we add the AFF module to realize the deep fusion of the shallow-detail and high-level semantic features of holothurians. The results show that the method presented in this paper yields better results on the 2020 CURPC underwater target-detection image dataset with an AP50 of 83.43%, Params of 15.90 M, and FLOPs of 25.12 G compared to other methods. In the underwater holothurian-detection task, this method improves the accuracy of detecting holothurians with fuzzy features, a small size, and dense scene. It also achieves a good balance between detection accuracy, Params, and FLOPs, and is suitable for underwater holothurian detection in most situations.

Considering the complex flow state of the duct flow field in the exhaust system, the structural parameters can significantly impact the internal flow field and noise. This paper takes the noise generated by the duct system under operating conditions as the research object, studies the mechanism of duct noise generation through theoretical analysis, numerical simulation and experimental test, and proposes an optimization design method, that is, to improve the duct structure by adding duct guide vanes. In order to maximize the optimization effect of the guide vane, a multiobjective optimization design of its profile is required, including the parametric expression of the guide vane profile, establishing the design variables and optimization objectives, and establishing the Kriging approximation model. The IMOCS algorithm is used to accurately and efficiently calculate the Pareto front solution to obtain the optimal profile of the duct guide vane and finally improve the noise-reduction performance of the duct system. This paper applies this design method to an integrated stove head duct to verify its accuracy, and prototype tests are conducted according to the optimization results. The test results show that the optimized integrated cooker has improved the outlet flow rate of the whole machine by 1.2 m3/min and reduced the noise by 2.3 dB. [ABSTRACT FROM AUTHOR]

The failure of insulators may seriously threaten the safe operation of the power system, where the state detection of high-voltage insulators is a must for the normal and safe operation of the power system. Based on the data of insulators in aerial images, this work explored an enhanced particle swarm algorithm to optimize the parameters of the support vector machine. A support vector machine model was therefore established for the identification of the normal and defective states of the insulators. This methodology works with the structure minimization principle of SVM and the characteristics of particle swarm fast optimization. First, the aerial insulator image was segmented as a target by way of the seed region growth based on double-layer cascade morphological improvements, and then, HOG features plus GLCM features were extracted as sample data. Finally, an ameliorated PSO-SVM classifier was designed to realize insulator state identification. Comparisons were made between PSO-SVM and conventional machine learning algorithms, SVM and Random Forest, and an optimization algorithm, Gray Wolf Optimization Support Vector Machine (GWO-SVM), and advanced neural network CNN. The experimental results showed that the performance of the algorithm proposed in this paper touched the top level, where the recognition accuracy rate was 92.11%, the precision rate 90%, the recall rate 94.74%, and the F1-score 92.31%. [ABSTRACT FROM AUTHOR]

The importance of unusual DNA structures in the regulation of basic cellular processes is an emerging field of research. Amongst local non-B DNA structures, G-quadruplexes (G4s) have gained in popularity during the last decade, and their presence and functional relevance at the DNA and RNA level has been demonstrated in a number of viral, bacterial, and eukaryotic genomes, including humans. Here, we performed the first systematic search of G4-forming sequences in all archaeal genomes available in the NCBI database. In this article, we investigate the presence and locations of G-quadruplex forming sequences using the G4Hunter algorithm. G-quadruplex-prone sequences were identified in all archaeal species, with highly significant differences in frequency, from 0.037 to 15.31 potential quadruplex sequences per kb. While G4 forming sequences were extremely abundant in Hadesarchaea archeon (strikingly, more than 50% of the Hadesarchaea archaeon isolate WYZ-LMO6 genome is a potential part of a G4-motif), they were very rare in the Parvarchaeota phylum. The presence of G-quadruplex forming sequences does not follow a random distribution with an over-representation in non-coding RNA, suggesting possible roles for ncRNA regulation. These data illustrate the unique and non-random localization of G-quadruplexes in Archaea. [ABSTRACT FROM AUTHOR]