A Segmented Iterative Learning Scheme-Based Distributed Fault Estimation for Switched Interconnected Nonlinear Systems.

In this article, a distributed fault estimation (DFE) approach for switched interconnected nonlinear systems (SINSs) with time delays and external disturbances is proposed using a novel segmented iterative learning scheme (SILS). First, through the utilization of interrelated information among subsystems, a distributed iterative learning observer is developed to enhance the accuracy of fault estimation results, which can realize the fault estimation of all subsystems under time delays and external disturbances. Simultaneously, to facilitate rapid fault information tracking and significantly reduce sensitivity to interference, a new SILS-based fault estimation law is constructed by combining the idea of segmented design with the method of variable gain. Then, an assessment of the convergence of the established fault estimation methodology is conducted, and the configurations of observer gain matrices and iterative learning gain matrices are duly accomplished. Finally, simulation results are showcased to demonstrate the superiority and feasibility of the developed fault estimation approach.

A Semantic Segmentation Method Based on AS-Unet++ for Power Remote Sensing of Images.

In order to achieve the automatic planning of power transmission lines, a key step is to precisely recognize the feature information of remote sensing images. Considering that the feature information has different depths and the feature distribution is not uniform, a semantic segmentation method based on a new AS-Unet++ is proposed in this paper. First, the atrous spatial pyramid pooling (ASPP) and the squeeze-and-excitation (SE) module are added to traditional Unet, such that the sensing field can be expanded and the important features can be enhanced, which is called AS-Unet. Second, an AS-Unet++ structure is built by using different layers of AS-Unet, such that the feature extraction parts of each layer of AS-Unet are stacked together. Compared with Unet, the proposed AS-Unet++ automatically learns features at different depths and determines a depth with optimal performance. Once the optimal number of network layers is determined, the excess layers can be pruned, which will greatly reduce the number of trained parameters. The experimental results show that the overall recognition accuracy of AS-Unet++ is significantly improved compared to Unet.

Transmission Line-Planning Method Based on Adaptive Resolution Grid and Improved Dijkstra Algorithm.

An improved Dijkstra algorithm based on adaptive resolution grid (ARG) is proposed to assist manual transmission line planning, shorten the construction period and achieve lower cost and higher efficiency of line selection. Firstly, the semantic segmentation network is used to change the remote sensing image into a ground object-identification image and the grayscale image of the ground object-identification image is rasterized. The ARG map model is introduced to greatly reduce the number of redundant grids, which can effectively reduce the time required to traverse the grids. Then, the Dijkstra algorithm is combined with the ARG and the neighborhood structure of the grid is a multi-center neighborhood. An improved method of bidirectional search mechanism based on ARG and inflection point-correction is adopted to greatly increase the running speed. The inflection point-correction reduces the number of inflection points and reduces the cost. Finally, according to the results of the search, the lowest-cost transmission line is determined. The experimental results show that this method aids manual planning by providing a route for reference, improving planning efficiency while shortening the duration, and reducing the time spent on algorithm debugging. Compared with the comparison algorithm, this method is faster in running speed and better in cost saving and has a broader application prospect.

Accumulated X-ray irradiation induces miR-187-5p upregulation mediating fibrotic buccal mucosal fibroblasts activities via DKK2.

Background/purpose: With the wide application of X-rays in dental examinations, it is necessary to assess its potential risk and explore an effective indicator. This study aimed to analyze the response of miR-187-5p to X-ray irradiation and to evaluate its potential of predicting X-ray potential dangers. Materials and methods: Patients that had to receive dental X-ray examination were enrolled and the expression of miR-187-5p in the buccal mucosa swabs were detected by real-time quantitative polymerase chain reaction (PCR). The effect of miR-187-5p on fibrotic buccal mucosal fibroblasts (fBMFs) activities was assessed by cell migration and invasion and the expression of fibrosis markers. In mechanism, the interaction between miR-187-5p and DKK2 and their co-regulatory effects were also estimated. Results: Significant upregulation of miR-187-5p was observed in the patients who received over twice X-ray irradiation. miR-187-5p was found to regulate the luciferase and expression level of DKK2 in fBMFs. Moreover, miR-187-5p knockdown dramatically suppressed the migration and invasion of fBMFs and inhibited the expression of α-SMA, collagen I, and collagen II, three typical fibrosis markers. Silencing could reverse the inhibitory effect of miR-187-5p knockdown on fBMFs activities. Conclusion: Accumulated X-ray irradiation could induce miR-187-5p upregulation, which could regulate the fBMFs activities via modulating DKK2. miR-187-5p could serve as an indicator of X-ray examination dangers to prevent the potential risk during the accumulated application of X-ray irradiation in the dental examination.

A New Positioning Method for Climbing Robots Based on 3D Model of Transmission Tower and Visual Sensor.

With the development of robot technology and the extensive application of robots, the research on special robots for some complex working environments has gradually become a hot topic. As a special robot applied to transmission towers, the climbing robot can replace humans to work at high altitudes to complete bolt tightening, detection, and other tasks, which improves the efficiency of transmission tower maintenance and ensures personal safety. However, it is mostly the ability to autonomously locate in the complex environment of the transmission tower that limits the industrial applications of the transmission tower climbing robot. This paper proposes an intelligent positioning method that integrates the three-dimensional information model of transmission tower and visual sensor data, which can assist the robot in climbing and adjusting to the designated working area to guarantee the working accuracy of the climbing robots. The experimental results show that the positioning accuracy of the method is within 1 cm.

The analysis of decimation and interpolation in the linear canonical transform domain.

Decimation and interpolation are the two basic building blocks in the multirate digital signal processing systems. As the linear canonical transform (LCT) has been shown to be a powerful tool for optics and signal processing, it is worthwhile and interesting to analyze the decimation and interpolation in the LCT domain. In this paper, the definition of equivalent filter in the LCT domain have been given at first. Then, by applying the definition, the direct implementation structure and polyphase networks for decimator and interpolator in the LCT domain have been proposed. Finally, the perfect reconstruction expressions for differential filters in the LCT domain have been presented as an application. The proposed theorems in this study are the bases for generalizations of the multirate signal processing in the LCT domain, which can advance the filter banks theorems in the LCT domain.