Jun Su's research while affiliated with Hubei University of Technology and other places

Insulator defect detection plays a critical role in ensuring electrical equipment’s safe and stable operation, meeting the public’s demand for electricity consumption. However, extracting features of insulator defects poses challenges due to complex backgrounds, variations in target sizes leading to potential oversights, and low detection accuracy. We propose an improved YOLOv8n-based insulator defect detection model to achieve timely and precise real-time detection. Firstly, the TripletAttention Module is introduced to enhance the network’s ability to extract insulator defect features and reduce background interference in detection. Secondly, SCConv (Spatial and Channel Reconstruction Convolution) is utilized to redesign the detection head, proposing a more lightweight SC-Detect to replace the original one, thereby restricting feature redundancy and enhancing feature representation capability. Finally, Slim-neck based on GSConv is employed to reconstruct the neck structure, enabling the network to achieve lightweight while possessing relatively stronger feature extraction and perceptual capabilities. Experimental results demonstrate that the improved insulator defect detection network achieves an accuracy of 96.1%, a recall rate of 94.8%, a mAP@0.5 of 97.2%, and a mAP@0.5-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}0.95 of 72%, representing increases of 1.5%, 4.2%, 2.5%, and 6%, respectively. Additionally, the parameter count decreases by 22%, and computational load reduces by 39%, thereby meeting the high-precision and real-time requirements for outdoor insulator defect detection tasks.

The paper considers the speedy images processing in Wireless Multimedia Sensor Networks using the Residue Number System (RNS) and the method of arithmetic coding. The proposed method has a two-stage frame: firstly, the RNS transformation is run to divide the data and obtain residues, and secondly, the parallel compression of the resulting residues is provided by employing the arithmetic coding. Within the implementation of binary code transformation in RNS one, the hardware complexity for block conversion is evaluated for various modulo sets and the results are illustrated. Authors employed the arithmetic coding for residue compression to provide the optimum of compression degree in terms of entropy assessment as well as a reduction in image redundancy without loss of quality. A research algorithm is proposed to run an experiment presented by the residues carried out on test images and other types of files. As a result, an increase in the speed of image compression of about 2.5 times is achieved by processing the small data as well as providing the parallel operation of the compression residue units by RNS selected moduli. Finally, the existing and proposed methods are compared and it has been shown the last one provides a better compression ratio of more than twice.

Segmenting medical images is a principal component of computer vision. The UNet model framework has taken over as the standard framework for this activity across a wide range of medical picture segmentation applications. Due to convolutional neural networks (CNNs) convolution operation limitations, the model's global modeling ability is not absolutely perfect. Moreover, a single convolution operation cannot gather feature information at various scales, which will have an impact on the quality of the global feature extraction as well as the localization of local details. The DCELANM‐Net structure, which this article offers, is a model that ingeniously combines a Dual Channel Efficient Layer Aggregation Network (DCELAN) and a Micro Masked Autoencoder (Micro‐MAE). On the one hand, for the DCELAN, the features are more effectively fitted by deepening the network structure, which in turn can successfully learn and fuse the features, helping to locate the local feature information more accurately; and the utilization of each layer of channels is more effectively improved by widening the network structure and residual connections. We adopt Micro‐MAE as the learner of the model. In addition to being straightforward in its methodology, it also includes a self‐supervised learning method, which has the benefit of being incredibly scalable for the model. This scalable method enables the generalization of high‐volume models, and the models can show good scaling behavior. It is also shown that Micro‐MAE is a powerful and adaptable learner that we can incorporate it into our network design to improve the model's accuracy and stability for tasks involving medical picture segmentation. Superior metrics and good generalization are demonstrated by DCELANM‐Net on the datasets Kvasir‐SEG and CVC‐ClinicDB. In the experiments, we set DCELANM‐S and DCELANM‐L to represent different sizes of the model, and since DCELANM‐L has the best performance, DCELANM‐L is determined as the base model for all experiments, called DCELANM.

The assessment of temperature measurement errors by platinum resistance temperature detectors (RTD) was carried out. High measurement accuracy assured with their individual calibration, the voltage divider circuit for measuring resistance, the substitution method and the transitional measure. In this case, error due heating the RTDs by their operating current needs correction. The proposed method of correction of RTD’s error due to heating by the operating current decreased this error in two times. The residual error was estimated to be no more than 0.004°C.

The DCELANM-Net structure, which this article offers, is a model that ingeniously combines a Dual Channel Efficient Layer Aggregation Network (DCELAN) and a Micro Masked Autoencoder (Micro-MAE). On the one hand, for the DCELAN, the features are more effectively fitted by deepening the network structure; the deeper network can successfully learn and fuse the features, which can more accurately locate the local feature information; and the utilization of each layer of channels is more effectively improved by widening the network structure and residual connections. We adopted Micro-MAE as the learner of the model. In addition to being straightforward in its methodology, it also offers a self-supervised learning method, which has the benefit of being incredibly scaleable for the model.

Feature selection is the procedure of extracting the optimal subset of features from an elementary feature set, to reduce the dimensionality of the data. It is an important part of improving the classification accuracy of classification algorithms for big data. Hybrid metaheuristics is one of the most popular methods for dealing with optimization issues. This article proposes a novel feature selection technique called MetaSCA, derived from the standard sine cosine algorithm (SCA). Founded on the SCA, the golden sine section coefficient is added, to diminish the search area for feature selection. In addition, a multi-level adjustment factor strategy is adopted to obtain an equilibrium between exploration and exploitation. The performance of MetaSCA was assessed using the following evaluation indicators: average fitness, worst fitness, optimal fitness, classification accuracy, average proportion of optimal feature subsets, feature selection time, and standard deviation. The performance was measured on the UCI data set and then compared with three algorithms: the sine cosine algorithm (SCA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). It was demonstrated by the simulation data results that the MetaSCA technique had the best accuracy and optimal feature subset in feature selection on the UCI data sets, in most of the cases.

Network planning of multi-layer heterogeneous mobile networks with complex topology is an important task. In this paper the spectral and energy efficiency of integrated LTE/Wi-Fi technologies for 5G are improved. A method of adaptive formation of the structure of radio access level (RAL) with the provision of the required quality of service (QoS) and the possibility of broadband data transmission is proposed. The Voronoi tessellation is used for designing the RAL of 5G mobile networks for the placement of base stations, which allowed optimum delimiting the coverage area for each base station and provide users the cell interface services. To minimize interference there was proposed a method of dynamic frequency reuse for different sizes of Voronoi cells. Modelling shows the developed method is effective both at low and at high network load, however, at high load there is a slightly smaller gain in energy efficiency than at low load.