Cracks are common defects in concrete structures and could lead to structural failure. This study provides a solution to support Structural Health Monitoring of concrete bridges from 2D images to a 3D model and then to Virtual Reality (VR) visualisation. To accomplish this, two concrete bridges in the Mediterranean Sea region were inspected, and image data were collected. This data was subsequently annotated manually for cracks through the VGG tool and then analysed via the 3DF Zephyr software. From a Sparse point cloud to a Dense point cloud and then a 3D mesh, all were smoothly constructed using the mentioned software, with better visualisation of the annotated cracks in the final 3D model of the bridges. In the end, the 3D model .ply file was converted to .glb format, which is supported by the majority of the VR glasses apps, making the virtual inspection possible through mobile devices.
This paper addresses the robust control of uncertain sampled-data systems. Conventional shift-operator models become numerically ill-conditioned at high sampling frequency. To overcome this difficulty, the Delta-operator framework is adopted. It preserves mathematical consistency between continuous-time and discrete-time representations. An adaptive sliding-mode control (SMC) strategy and a generalized discrete-time reaching law are proposed within this framework. The reaching law unifies various reaching dynamics. A dual-layer adaptive uncertainty envelope is constructed to reject complex disturbances. The first layer compensates for the slowly-varying mean of the lumped disturbance; the second layer bounds the fast residual. A Lyapunov-based analysis proves closed-loop stability. The sliding-mode variable converges to a quasi-sliding mode (QSM) band in finite steps. The framework applies to systems with matched and projected unmatched disturbances. Simulations on a switched reluctance motor (SRM) and an inverted pendulum-cart (IPC) indicate that the strategy achieves robust tracking precision, rapid convergence, and substantial chattering reduction.
Single-event transients (SETs) in space radiation environments can propagate through the control loop of DC-DC converters, causing duty-cycle anomalies and output-voltage perturbations, thereby degrading the power-supply stability of spacecraft electronic systems. This paper investigates the SET sensitivity and hardening design of the control loop in a peak current-mode-controlled Boost converter, with particular focus on the error-amplifier output node, which is highly susceptible to SETs. First, a circuit-level simulation model is employed to analyze the SET propagation mechanism in the Boost control loop. The error-amplifier output node is identified as the SET-sensitive node, and the effects of load conditions and particle strike timing on output-voltage perturbations are investigated. Based on these analyses, a loop-synchronous-regulation-based SET hardening method is proposed. During an SET event, the current-loop summing node is synchronously regulated to follow the disturbed voltage-loop node, thereby maintaining the voltage difference at the comparator inputs and the duty-cycle signal nearly unchanged, and effectively suppressing SET propagation through the control loop. Based on a commercial 180 nm process, the proposed hardening circuit is verified by simulation in a Boost converter with typical specifications of VIN = 3.7 V, VOUT = 7.6 V, and IOUT = 100 mA. Under an LET of 100 MeV·cm2/mg, the hardening circuit suppresses the output-voltage fluctuation to within 1.78 mV, achieving a mitigation capability of 90.19 %. The proposed method effectively enhances the SET tolerance of the Boost converter and provides a circuit-level design reference for SET mitigation in DC-DC converters.
Landmine contamination remains a critical threat in post-conflict zones, necessitating rapid, accurate, and autonomous surveying solutions. This paper presents a new Deep Convolutional Neural Network (DCNN) framework in identifying landmines under the ground with aided aerial RGB and thermal image data taken by Unmanned Aerial Vehicles (UAVs). The model bypasses the often-required role of direct human interaction and has fewer demands on the direct ability to generate detections to autonomously detect buried landmines. The suggested system is based on a Dual-Modality Cross-Attention Fusion (DCAF) module to merge the RGB and the thermal feature. The model attained the overall accuracy of 97.89 %, precision of 97.4 %, recall of 98.1 %, and F1-score of 97.7 % on a variety of simulated terrains, such as grasslands, muddy areas, rubbles, and cratered disposals. Also, the system shows a temporal consistency index of 96.5 % and a mean IoU of 93.1 % surpassing the RGB-only and thermal-only baselines.
In this article an attempt has been made to analyze the Marshall-Olkin inverse log-logistic simple step-stress accelerated life test under the cumulative exposure model. The scale parameter of the Marshall-Olkin inverse log-logistic distribution is assumed to be a log-linear function of the stress level. A comprehensive simulation study is conducted for various sample sizes at different stress levels with the optimum time to assess the performance of the maximum likelihood estimators via mean squared error, asymptotic variance, coverage probability, and confidence interval width. It is also concluded that increasing the difference between stress levels improves estimation performance when one stress level is fixed. However, the variations in parameter estimates among the examined stress combinations are not significant. For illustration purposes, one real data set is also examined.
