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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. [ABSTRACT FROM AUTHOR]

The present study describes the development of a wearable device designed to assist those who work in an unstructured posture. In the manufacturing sector, industrial accidents have been steadily on the rise due to poor work environments and excessive workloads imposed on workers. Against this backdrop, the present study aimed to analyze various types of work, especially those performed in unstructured postures by heavy industry workers, who are frequently exposed to high workloads and poor work environments. Based on the analysis results, an attempt was made to develop a shoulder muscle-assistive wearable device capable of assisting a wearer who is working using their shoulder muscles. Various types of unstructured posture work are performed in heavy industries, including activities such as the welding and grinding of ship components and plant structures. They are typically conducted in narrow spaces with limited postures, causing many workers to suffer muscle fatigue. In the present study, as the first step of developing a shoulder muscle-assistive wearable device, different working scenarios were simulated, and the corresponding motion data and required torque values were estimated using motion capture devices. The obtained motion data and required torque values were reflected in the design of the wearable device. The main structural body of the shoulder muscle-assistive wearable device was made of a carbon fiber-reinforced composite to be lightweight. This shoulder muscle-assistive wearable device was designed to fully cover the range of motion for workers working in unstructured postures while generating the torque required for a given job, thereby enhancing the muscular endurance of the workers. The gravity compensation module of the designed shoulder muscle-assistive wearable device generates a support force of 4.47 Nm per shoulder. The shoulder muscle assistive wearable device was developed to provide support for approximately 30% of the shoulder joint's maximum torque generated in overhead tasks. This shoulder muscle-assistive wearable device is expected to contribute to improving the productivity of field workers, while reducing the occurrence of musculoskeletal injuries arising from the aging of the working-age population. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]