In recent years, deep learning (DL) has been widely studied using various methods across the globe, especially with respect to training methods and network structures, proving highly effective in a wide range of tasks and applications, including image, speech, and text recognition. One important aspect of this advancement is involved in the effort of designing and upgrading neural architectures, which has been consistently attempted thus far. However, designing such architectures requires the combined knowledge and know-how of experts from each relevant discipline and a series of trial-and-error steps. In this light, automated neural architecture search (NAS) methods are increasingly at the center of attention; this paper aimed at summarizing the basic concepts of NAS while providing an overview of recent studies on the applications of NAS. It is worth noting that most previous survey studies on NAS have been focused on perspectives of hardware or search strategies. To the best knowledge of the present authors, this study is the first to look at NAS from a computer vision perspective. In the present study, computer vision areas were categorized by task, and recent trends found in each study on NAS were analyzed in detail.

This paper describes the development of an in-pipe inspection robot system designed for large-diameter water pipes. The robot is equipped with a Magnetic Flux Leakage (MFL) sensor module. The robot system is intended for pipes with diameters ranging from 900 mm to 1200 mm. The structure of the in-pipe inspection robot consists of the front and rear driving parts, with the inspection module located centrally. The robot is powered by 22 motors, including eight wheels with motors positioned at both the bottom and the top for propulsion. To ensure that the robot’s center aligns with that of the pipeline during operation, lifting units have been incorporated. The robot is equipped with cameras and LiDAR sensors at the front and rear to monitor the internal environment of the pipeline. Pipeline inspection is conducted using the MFL inspection modules, and the robot’s driving mechanism is designed to execute spiral maneuvers while maintaining contact with the pipeline surface during rotation. The in-pipe inspection robot is configured with wireless communication modules and batteries, allowing for wireless operation. Following its development, the inspection robot underwent driving experiments in actual pipelines to validate its performance. The field test bed used for these experiments is approximately 1 km in length. Results from the driving experiments on the field test bed confirmed the robot’s ability to navigate various curvatures and obstacles within the pipeline. It is posited that the use of the developed in-pipe inspection robot can reduce economic costs and enhance the safety of inspectors when examining aging pipes.

The objective of this study is to evaluate the fatigue life and strength of a GFRP bogie frame for urban subway train. The bogie frame was made of a GEP224 glass fiber/epoxy 4-harness satin woven composite. In order to obtain the S-N curve for a composite material applied to composite bogie frame, the tension-compression fatigue test was done with different stacking sequence of warp-direction, fill-direction, and 0°/90°-direction, respectively. The fatigue test was conducted with stress ratio (R) of -1, frequency of 5Hz and endurance limit of 107. The fatigue strength of bogie frame was evaluated by Goodman diagram according to the JIS E 4207. The results show that fatigue life and strength of a GFRP composite bogie have satisfied the requirements of JIS E 4207. Also, the GFRP composite bogie frame had a good fatigue performance in comparison with conventional metal bogie frame based on SM490A, considering its weight

The fatigue strength and life of a GFRP composite bogie frame for urban subway trains were evaluated in this study. The GEP224 glass fabric/epoxy was selected as the structural material of a composite bogie frame to save its weight. In order to obtain the fatigue characteristics of the GEP224 glass/epoxy composite material, the tension–compression fatigue test was done with three different stacking sequences of warp direction, fill direction, and warp/fill direction, respectively. A fatigue test was conducted with a stress ratio, R of 0.1 and −1, and up to endurance limit of 10⁷ with frequency of 5 Hz. The fatigue strength of the GFRP composite bogie frame was evaluated on the basis of the S-N curve and Goodman diagrams obtained from GEP224 glass/epoxy composites according to the JIS E 4207 and UIC 515/615-4. The result shows that fatigue strength of the GFRP composite bogie has satisfied the design requirements. Also, the GFRP composite bogie frame had a good fatigue performance in comparison with conventional metal bogie frame, considering its weight.