Distributed Resource Allocation in Dispersed Computing Environment Based on UAV Track Inspection in Urban Rail Transit

With the rapid development of urban rail transit,the existing track detection has some problems such as low efficiency and insufficient detection coverage,so an intelligent and automatic track detectionmethod based onUAV is urgently needed to avoid major safety accidents.At the same time,the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices.As a result,the Dispersed Computing(DCOMP)architecture enables collaborative computing between devices in the Internet of Everything(IoE),promotes low-latency and efficient cross-wide applications,and meets users’growing needs for computing performance and service quality.This paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection tracks.Furthermore,the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational constraints.The Markov Decision Process(MDP)model is employed to capture the connection between the dispersed computing resource allocation strategy and the system environment.Subsequently,a method based on Double Deep Q-Network(DDQN)is introduced to derive the optimal policy.Simultaneously,an experience replay mechanism is implemented to tackle the issue of increasing dimensionality.The experimental simulations validate the efficacy of the method across various scenarios.

High-speed railway track components inspection framework based on YOLOv8 with high-performance model deployment

Railway inspection poses significant challenges due to the extensive use of various components in vast railway networks,especially in the case of high-speed railways.These networks demand high maintenance but offer only limited inspection windows.In response,this study focuses on developing a high-performance rail inspection system tailored for high-speed railways and railroads with constrained inspection timeframes.This system leverages the latest artificial intelligence advancements,incorporating YOLOv8 for detection.Our research introduces an efficient model inference pipeline based on a producer-consumer model,effectively utilizing parallel processing and concurrent computing to enhance performance.The deployment of this pipeline,implemented using C++,TensorRT,float16 quantization,and oneTBB,represents a significant departure from traditional sequential processing methods.The results are remarkable,showcasing a substantial increase in processing speed:from 38.93 Frames Per Second(FPS)to 281.06 FPS on a desktop system equipped with an Nvidia RTX A6000 GPU and from 19.50 FPS to 200.26 FPS on the Nvidia Jetson AGX Orin edge computing platform.This proposed framework has the potential to meet the real-time inspection requirements of high-speed railways.

Railway Track Verification Using a Forward-Looking SAR GPR

It is a challenge to evaluate the conditions of railway track without interruption of regular traffic. In this paper, the authors introduce the detection of cavities under the railway substructure by using forward-looking ground penetrating radar （FLGPR）. Main advantages of FLGPR are that such a system can illuminate a large area and can stand off a long distance over its down-looking counterpart. Two methods, frequency wave-number （F-W） synthetic aperture imaging （SAI） and beam-forming by delay and sum （DAS）, are applied to process the collected data. Analysis and measuring show that the distinct radar image of the cavity beneath the substructure 1.2 m deep can be formed by these two methods.

A parametric design method of nanosatellite close-range formation for on-orbit target inspection

This paper proposes an efficient design method for nano satellites formation flying near a large space target to perform ultra-close inspection missions.A parametric model for periodic relative motion between two satellites is firstly proposed through a detailed analysis of the relative orbital dynamics.It is proved that the existing periodic solutions of satellite relative motion such as in-plane 2:1 elliptic and circular periodic relative orbits both belong to the ellipsoid family of periodic relative orbits.The motion planes and their locations and orientations of the general periodic relative orbits are then determined as the analytic functions of the initial relative states.The maximal and minimal distances from the relative orbit to the origin are further analytically calculated too.A formation design algorithm is then proposed for optimal observation of feature points of the target considering various requirements of collision avoidance and observable distance by using this parametric model.Numerical examples about target inspection are introduced to quantitively evaluate and verify the models and methods.The simulation results are well consistent with the theoretical predictions,showing that the design proposed can be potentially applied for future practical on-orbit service missions.