Browse Topic: Reliability
Multi-UAV cooperative localization can utilize information fusion between nodes to improve localization accuracy and performance on the target. Distributed state fusion estimation methods have been heavily studied in recent years, but the final estimates in the research results do not converge towards the global optimum. This paper aims to make the state estimates of each individual in the UAV formation for the target converge and converge to reliable values. In this paper, we study a multi-UAV cooperative tracking method based on adaptive weighted fusion, which first evaluates the importance of each node in the UAV formation and the reliability of the local filtering estimation results, and then assigns the weights according to the reliability of the UAV’s local state estimation of the target in the whole at the current moment. Finally, this paper verifies through simulation experiments that the method can not only accomplish the state tracking of the target, but also that the state estimates of each node in the network converge to more accurate state estimates.
The gearbox is a key component of the mechanical transmission system, and its fault diagnosis is essential to the reliability of the equipment. However, obtaining fault samples under actual working conditions for gearbox fault diagnosis is challenging. In this paper, the rigid-flexible coupling dynamic simulation model of the gearbox is established, and the co-simulation of gear normal, crack, and breakage is carried out in the ADAMS and MATLAB environments. The comparison between the simulated and measured signals shows that the simulation method can accurately reflect the key characteristics, such as rotation frequency and meshing frequency, and verify its reliability and accuracy. The research results can provide effective data support for gearbox fault diagnosis and improve the operational safety of mechanical systems.
Aiming at the problem of insufficient modeling of spatio-temporal heterogeneity in road traffic accident prediction, a dual task machine learning framework integrating geographical environment, location attributes and time periodicity is proposed. The dataset used in this study was derived from traffic accident records of Nanchang during 2019–2023. Firstly, geographical identifiers are generated by rounding and aggregating latitude and longitude coordinates. At the same time, the location type is processed by a one-hot encoding, so as to carry out spatial clustering analysis of accident hotspots. Compared with the North-South pattern, the contribution of geographical features shows a strong East-West trend. The kernel density heatmap identified Zone A and zone B as dual core high-risk areas. Secondly, the sinusoidal/cosine function is used to encode the time feature circularly, which effectively captures the daily change of the accident. The quantitative analysis of random forest regression model showed that time characteristics accounted for 89.2% of the variance of accident frequency interpretation, significantly exceeding the contribution of geographical factors (10.2%) and location attributes (0.6%). After hyperparameter optimization, the accuracy of XGBoost classifier in predicting serious accidents is 75.97%, and the AUC value is 0.8412, which has strong robustness, and provides reliable support for dynamic risk assessment of traffic management system.
Medical device manufacturing is undergoing a structural shift. As devices become smaller with broader functionality, traditional approaches to assembling electronics are no longer sufficient. Increasingly, performance, durability, and reliability are dictated not just by design, but by how that design is manufactured.
Robotic manipulation remains one of the harder unsolved problems in automation engineering. Vision-based systems have matured considerably — object localization, pose estimation, and grasp planning from RGB-D data are now reliable enough for structured industrial environments. What vision cannot provide is contact information: whether a grasp is stable, whether a surface is beginning to slip, or how force is distributed across a fingertip during a hold. These signals are what close the control loop during manipulation, and without them, systems compensate through excessive grip force, conservative motion profiles, and large training datasets designed to paper over sensing uncertainty.
Traditional industrial robotics has been built on traditional premises: define the task precisely, program the motion, and repeat it with minimal variation. This model has delivered reliability, speed, and scale across multiple application domains.
A new DC-DC power converter is superior to previous designs and paves the way for more efficient, reliable, and sustainable energy storage and conversion solutions. The Kobe University development can efficiently interface with a wide range of energy sources while enhancing system stability and simplicity at an unprecedented efficiency.
Ultra-miniature sensors are enabling advanced procedures and treatments across a wide range of medical devices, from catheters and neuro interfaces to wearables. But as electromagnetic sensors get smaller, trade-offs begin to emerge — lower sensitivity, less tolerance for environmental influences, and greater susceptibility to interference — underscoring the need for robust testing to ensure accurate, reliable tracking.
Designing the next generation of wireless wearables, implantables, and real-time external or in-body monitoring devices is a complex challenge that requires highly reliable electronic components that greatly exceed the performance of standard commercial alternatives.
This SAE standard establishes the requirement for suppliers to plan a reliability program that satisfies the following three requirements: a The supplier shall ascertain customer requirements b The supplier shall meet customer requirements c The supplier shall assure that customer requirements have been met
This study investigates the use of the Overset mesh method for propeller simulations in OpenFOAM and compares it with the Arbitrary Mesh Interface (AMI) approach. While AMI is well validated for rotor aeroacoustics, it is limited in handling large relative motions and complex component interactions. In contrast, the Overset method enables flexible simulation of transition kinematics using overlapping grids, though its aeroacoustic capability in OpenFOAM has not been well established. A comparative analysis was conducted on a Joby-scale five-bladed propeller at an 80° tilt angle without a fairing, representing a transition-flight condition. Aerodynamic and acoustic predictions were obtained using hybrid DDES coupled with the Ffowcs Williams–Hawkings method. Results show that the Overset method provides improved agreement with experimental thrust and torque and captures stronger leading-edge vortices than AMI. Both methods resolve blade-vortex and blade-wake interactions. However, the Overset approach produces higher broadband noise due to stronger vortices and interpolation effects, while AMI yields smoother pressure fields and clearer tonal content. In the far field, AMI better matches experimental SPL trends. Overset shows larger first-BPF SPL errors (up to 22.5 dB vs. 5.7 dB for AMI), though OASPL differences remain small. Overall, Overset is less reliable for noise prediction and more computationally expensive.
This study highlights that rotor-rotor interactions can significantly modify both tonal and broadband noise characteristics. Continued investigation into these mechanisms is vital to developing reliable noise prediction methodologies and establishing design strategies that balance propulsion efficiency with acoustic acceptability for AAM vehicles. This work sought to answer what physical mechanisms contribute to the increased dominance of broadband noise in eVTOL-scale rotors. By characterizing the tip vortex of a single rotor, it was found that rotor-rotor interaction is highly dependent on two factors: Blade-vortex phasing and interaction duration. Characteristic vortex time scales can be correlated with increased noise. Interactions that generate increased noise have a non-linear relationship with rotor positioning. The interaction-generated noise is highly directive. This work aims to elucidate the dominant source noise mechanisms of rotor-rotor interaction noise by characterizing blade tip vortices using PIV.
Patching vulnerabilities in safety-critical domains such as automotive and aerospace is costly and complex. A small code modification can trigger a complete rebuild, producing a binary with widespread changes. This inflates patch size, complicates regression testing, and makes over-the-air (OTA) updates inefficient, as traditional binary patches often replace large portions of the executable. We present a binary rewriting–based experiment that shows the feasibility of a patch that updates only the affected bytes by computing the impact of a code change at the binary level. This produces minimal, localized patches rather than regenerated executables. The preliminary experiment shows that a single source change, which leads to thousands of modified bytes after recompilation, can be captured with only a few bytes using our method. For automotive and aerospace systems, this technique reduces patch size, conserves bandwidth, and minimizes disruption to certified software, offering a promising direction for efficient and reliable vulnerability remediation.
Oil churning and windage power losses in dip-lubricated gearboxes can significantly affect overall transmission efficiency, particularly at high rotational speeds. As modern gearbox systems are pushed toward higher efficiency and reliability, understanding and predicting these losses becomes increasingly important. In addition to energy dissipation, the associated multiphase flow phenomena—such as oil splashing, thin film formation along gear surfaces, and aeration of the sump—strongly influence lubrication effectiveness, heat transfer, and component durability. Capturing these effects requires a robust numerical strategy that can resolve both power loss mechanisms and multiphase flow dynamics with sufficient accuracy. In this study, a single spur gear is numerically analyzed under varying oil depths and rotational speeds to quantify total power loss and investigate oil flow patterns. The computational approach employs a volume-of-fluid multiphase framework, and the predictions are systematically validated against experimental data from the OSU Lab. Validation is carried out in two stages: first, by comparing the simulated oil free-surface shapes with experimental flow visualizations for various operating conditions; and second, by comparing total power loss across a range of rotational speeds and immersion depths. The findings confirm that qualitative comparisons of oil behavior show good agreement with experimental observations including splash generation, oil streak formation, and gear surface wetting. Furthermore, predicted power loss trends align with experiments, exhibiting exponential growth with RPM and a transition toward quadratic scaling as oil depth increases. Overall, this work highlights the capability of the numerical framework to predict both churning losses and multiphase flow behavior in gear lubrication systems, providing a foundation for future gearbox design and optimization.
The timing of video recordings, along with the spatial positioning of objects, is a fundamental parameter for calculating the speed time history. If the task involves determining the average speed of an object moving at approximately constant speed, it may be acceptable to average the speed over several to a dozen frames, using the fps (frames per second) parameter as the basic time unit.. However, if the objective is to compute speed from individual frames, the reliability of the timing becomes crucial. Without access to DVR hardware documentation, proprietary algorithms, or software – and considering the frequent hardware modifications and software updates - the most effective way to solve the problem is through a reverse-engineering approach. This study discusses several aspects of timing analysis, including: (1) making a test recording of a calibrated LED lightboard; (2) analyzing the relationship between the lightboard time and the presentation time stamp (pts) extracted from the file metadata; (3) investigating frame skipping and frame timing errors due to frame rate changes; (4) modeling the composite motion of the rolling shutter and the lightboard LEDs; (5) identifying the DVR’s actual frame capture rate; and (6) compensating the timing of the evidentiary recording. Establishing the timing scheme of the test recording enables reliable speed analysis based on two or three adjacent frames of the evidentiary recording, as well as the determination of the velocity time history over a short segment of the recording.
In a milestone for scalable quantum technologies, scientists from Boston University, UC Berkeley, and Northwestern University have reported the world’s first electronic–photonic–quantum system on a chip, according to a study published in Nature Electronics. The system combines quantum light sources and stabilizing electronics using a standard 45-nanometer semiconductor manufacturing process to produce reliable streams of correlated photon pairs — a key resource for emerging quantum technologies. The advance paves the way for mass-producible “quantum light factory” chips and large-scale quantum systems built from many such chips working together.
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