Reliability Quantification Research Articles

Ultrasound Imaging of Lumbar Multifidus Morphology and Quality – A Prospective Study on Monitoring Acute Adaptations to Specific Exercise and on Reliability

OBJECTIVE: To test the reliability of a rehabilitative ultrasound imaging (RUSI) protocol for analyzing lumbar multifidus morphologic characteristics for use by clinicians, and to analyze the specificity of isolated lumbar extension resistance exercise (ILEX) DESIGN: Intrarater reliability and interrater (imaging and quantification) reliability. METHODS: A standardized RUSI protocol was developed and the reliability of the procedure and image analysis (intraclass correlations coefficient [ICC], standard error of the measurement [SEM], minimum detectable change [MDC%]) was analyzed. Lumbar multifidus RUSI measures of muscles thickness, cross-sectional area, and echogenicity were obtained before and after 1 ILEX set with uniform time under tension and range of motion (12°-42°). RESULTS: Intrarater (between-day) reliability was similar (ICC, 0.88-0.97; SEM, 0.14-5.08; MDC%: 11-20) compared to interrater (same day) imaging reliability (ICC, 0.91-0.99; SEM, 0.09-3.78; MDC%, 8-15) and interrater quantification reliability (ICC, 0.83-0.97; SEM, 0.07-2.80; MDC%: 6-25). There was a significant increase of lumbar multifidus muscle thickness (pre-ILEX, 2.94 cm; post-ILEX, 3.16 cm; P<.001) and lumbar multifidus cross-sectional area (pre-ILEX, 7.61 cm2; post-ILEX, 8.42 cm2; P<.001) but a nonsignificant effect on echogenicity (pre-ILEX, 73.12; post-ILEX, 72.05; P = .257). CONCLUSION: RUSI is a reliable and easy-to-use diagnostic tool for lumbar multifidus muscle quality and morphology in daily practice. The tool is sensitive to change as shown using ILEX. JOSPT Open 2024;2(4):354-363. Epub 7 August 2024. doi:10.2519/josptopen.2024.0253

Object and Pedestrian Detection on Road in Foggy Weather Conditions by Hyperparameterized YOLOv8 Model

Connected cooperative and automated (CAM) vehicles and self-driving cars need to achieve robust and accurate environment understanding. With this aim, they are usually equipped with sensors and adopt multiple sensing strategies, also fused among them to exploit their complementary properties. In recent years, artificial intelligence such as machine learning- and deep learning-based approaches have been applied for object and pedestrian detection and prediction reliability quantification. This paper proposes a procedure based on the YOLOv8 (You Only Look Once) method to discover objects on the roads such as cars, traffic lights, pedestrians and street signs in foggy weather conditions. In particular, YOLOv8 is a recent release of YOLO, a popular neural network model used for object detection and image classification. The obtained model is applied to a dataset including about 4000 foggy road images and the object detection accuracy is improved by changing hyperparameters such as epochs, batch size and augmentation methods. To achieve good accuracy and few errors in detecting objects in the images, the hyperparameters are optimized by four different methods, and different metrics are considered, namely accuracy factor, precision, recall, precision–recall and loss.

Building reliable AI for quantifying uncertainty in particulate matter predictions with deep learning

Predicting the concentrations of particulate matter (PM) has recently become crucial because of its significant impact on air pollution. As the adoption of artificial intelligence (AI) in this domain increases, ensuring the reliability of AI-driven predictions has become paramount. Although many previous studies have harnessed the power of AI for PM prediction, the inherent uncertainty in the results, which is influenced by spatial data imbalances and varying meteorological factors, poses a challenge. Addressing this uncertainty is central to building reliable AI systems. Therefore, we employed the Monte Carlo dropout (MCDO) approach, which is a technique that integrates dropout layers in neural networks during both the training and inference phases, to estimate the prediction uncertainty. Our objective was to address the challenge of prediction uncertainty in PM concentrations, with the ultimate aim of enhancing the reliability and trustworthiness of AI-driven predictions in air quality forecasting. By applying the MCDO approach to grids formed from multidimensional arrays of air quality and weather data, we obtained a 95% confidence interval for the prediction results, thereby demonstrating the trustworthiness of our model. Our evaluation revealed an R-square greater than 0.97 for PM10 and PM2.5, showcasing the robust and reliable predictive performance of the model. This study highlights not only the accuracy of this approach but also the critical role of quantifying uncertainty in building reliable AI systems. Our findings mark a significant step towards a holistic approach to predictive modeling in which reliability and uncertainty quantification are at the forefront.

A Reliability Quantification Method for Deep Reinforcement Learning-Based Control

A Reliability Quantification Method for Deep Reinforcement Learning-Based Control

Modeling Reliability Analysis for the Branch-Based Irrigation Water Demands Due to Uncertainties in the Measured Surface Runoff

This study aims to model the uncertainty and reliability quantification of estimating the planning irrigation water demands in the multi-canal irrigation zone, named the RA_IWD_Canal model. The proposed RA_IWD_Canal could estimate the zone-based and branch-based water demands and quantify their uncertainties and reliabilities via the weighted frequency quantile curves. The historical planning irrigation water demands and measured surface runoff from 2019 to 2024 in the Zhudong irrigation zone are utilized in the model development and application. Using the proposed RA_IWD_Canal model, the estimated branch-based irrigation water demands exhibit a significant variation (on average, from 0.02 m3/s to 1.7 m3/s) in time and space attributed to uncertainties in the historical gauged surface runoff. Also, the Zhudong Canal zone is demonstrated to be sufficiently supplied irrigation water subject to existing introduced water demands with a high reliability of 0.85; instead, the associated branches have considerable difficulty achieving the expected irrigation efficiency based on the desired water requirements with low reliability (nearly 0.25). To keep all branches in the irrigation zone consistent in irrigation efficiency, the probabilistic-based water demands could be introduced via the proposed RA_IWD_Canal model with the desired reliability.

Quantifying the Reliability Performance of a Guided Wave-Based SHM System Using Piezoelectric Wafer Actuator Sensors

The adoption of structural health monitoring systems can bring important benefits to the aerospace industry. However, the current implementation of SHM technologies in today's aerospace industry remains limited. One of the primary obstacles hindering the adoption of SHM technologies is the challenge associated with assessing the reliability of SHM systems. In aerospace applications, SHM hardware is exposed to harsh environments, including high or low temperatures, vibrations, moisture, and radiation. Consequently, the long-term performance of SHM systems is adversely affected. This study delves into various issues related to the evaluation of SHM system reliability and performance quantification, placing special emphasis on guided wave-based SHM using Piezoelectric Wafer Actuator Sensors. We present experimental and numerical modeling examples designed to illustrate the detrimental effects of aging at the unitary sensor and sensor network levels. Conclusions are drawn regarding the current challenges in quantifying system reliability, emphasizing relevant performance metrics such as the probability of detection, localization, and false call rates.

Metamodeling-based reliability analysis framework for activated sludge processes

The reliability of activated sludge processes will be adversely affected by alterations in wastewater production and pollutant loading foreseen due to population growth, urbanization, and climate change, as well as the tendency to amend environmental regulations to mandate stricter effluent quality standards to alleviate water pollution. Until now, there was no framework capable of effectively managing these multifaceted challenges in reliability analysis. Previous attempts conducted a low number of simulations leading to insufficient statistical significance to properly validate reliability quantification. A metamodeling-based reliability analysis framework for the activated sludge process is introduced to cope with alterations in wastewater production and pollutant loading, assesses the reliability under different effluent regulations, and leverages metamodels to conduct extensive simulation work, to estimate the reliability. All metamodels produced high-resolution results, enabling reliability estimation after 100 000 simulations. The framework effectively assessed the annual failure rates of various activated sludge facility designs under four regulations, demonstrating the impact of stricter effluent quality standards. Integrating metamodels for reliability analysis greatly lowers computational costs, making the framework a time and resource-efficient choice for quick decision-making in facility design.

Reliability analysis and comparison of ring-PUF based on probabilistic models

—To address the issue of maladaptive reliability theory models in delay-based physical unclonable functions (PUF) of ring classes, this paper begins by analyzing the relationship between four key transistor parameters (channel length, channel width, threshold voltage, and gate oxide thickness) and delay sensitivity. Then establishes reliability quantification formulas for various PUF types, including ring oscillator PUF, k-out-of-1 PUF, Loop PUF, transient effect ring oscillator PUF, and duty cycle PUF. This analysis and comparison are conducted using the inverter delay model and probabilistic statistical methods to ensure fairness at the theoretical level. Finally, the proposed model is validated through simulation experiments, along with further analysis and comparison of ring-PUF. The results demonstrate that delay sensitivity can be utilized to assess the extent of delay impact when process parameters fluctuate. In terms of reliability, the ring-PUF can be ranked as follows: k-out-of-1 PUF, Loop PUF, ring oscillator PUF, transient effect ring oscillator PUF, and duty cycle PUF. This paper offers extensive technical guidance and theoretical support for ring PUF designers.

Accelerating reliability-based topology optimization via gradient online learning and prediction

In reliability-based topology optimization (RBTO), both quantification and maximization of structure reliability considering uncertainty of various quantities are essential. In this study, A novel gradient online learning and prediction (GoLap) method is proposed to accelerate the double-looped RBTO and reduce total computational time by using a one-hidden-layer neural networks (NN) and gradient and non-gradient based reliability quantification. The total computational time saving is achieved by (i) skipping the finite element analysis (FEA) runs and sensitivity analyses and (ii) updating the design and random variables using the gradients predicted by the NN model in selected inner and outer loops. 2D and 3D numerical examples, including an aircraft engine pylon, are presented to demonstrate the total computational time saving of up to 84.4%, and the prediction accuracy of the reliability of optimum structures for the GoLap implemented RBTO methods.

Reliability and Standardization for SiC Power Devices

The demand is rapidly increasing for SiC MOSFETs and diodes for power electronic conversion semiconductor (PECS) applications such as electrified vehicle charging and traction, energy storage systems and industrial power supplies. These applications employ a high quantity of large-area die per system while demanding high system-level reliability under aggressive electrical and environmental operating conditions. In addition, SiC devices exhibit some failure mechanisms that are less severe than, or non-existent, in Si devices. This situation demands thorough and novel device reliability characterization and quantification. It is also driving the development of industry consortia standards and guidelines at a much faster rate, and relatively earlier in the technology maturation phase, than occurred in the Si industry. In this paper, I will review some of the key published reliability performance data, stress procedure methodologies used, and implications for key applications. I will also compare and contrast the existing guideline and standard documents and suggest directions that are being explored for future documents. I will also discuss how future guidelines and standards are being developed to cover the SiC-specific failure mechanisms for representative mission profiles for some key applications, particularly electrified vehicles.

Evolutionary computation-based reliability quantification and its application in big data analysis on semiconductor manufacturing

Big data analysis of wafer maps in semiconductor manufacturing is essential for process reliability assessment, it is an important means of fault diagnosis in manufacturing. Although many excellent wafer map defect pattern identification (WMDPI) methods have been proposed, such as deep convolutional neural network (DCNN) based methods, there is no method that can make completely correct decisions and avoid false detections. Decision reliability is critical to the training evolution process of DCNN. In this paper, we propose a reliability quantification algorithm for wafer big data analysis. We estimate the parameter evolution uncertainty of DCNN in the WMDPI process by mixed uncertainty and thus quantify the decision reliability. In particular, reliability assessment is achieved by quantifying hybrid uncertainties, including epistemic uncertainty and aleatoric uncertainty. For epistemic uncertainty, this paper uses Monte Carlo dropout and explores the effects of drop rate, action location, and uncertainty criteria on reliability quantification through an empirical study. For aleatoric uncertainty, this paper proposes a calculation method based on Monte Carlo augmentation. The experimental results show that the identification accuracy of unknown defect patterns is 97.04% when hybrid uncertainty is considered; the identification accuracy of known defect patterns can be improved by up to 7.89%. The proposed DCNN can effectively avoid false and missed detections in the process of wafer big data analysis.

Batch Deterministic and Stochastic Petri nets Modeling for Reliability Quantification for Safety Critical Systems of Nuclear Power Plants

Safety-critical systems (SCS) are essential in maintaining and controlling the nuclear power plant (NPP) facilities, providing feedback on the plant's conditions, and safeguarding it from adverse consequences i.e., SCS plays a vital role in NPP. Thus, failure of such systems can lead to massive financial losses, human resource damage, and environmental degradation. It is, therefore, important that these systems should have a high level of reliability and accuracy. This research introduces a novel method for designing and assessing the reliability of SCS by employing batch deterministic & stochastic Petri nets (BDSPNs) and Markov chain. Our method of reliability evaluation achieved 99.9905% accuracy, proving its effectiveness. This paper illustrates the proposed approach to NPP's Shutdown System (SDS).

Bayesian neural networks enabled snapshot polarized light scattering spectroscopy with uncertainty quantification

Polarized light-scattering spectroscopy (PLSS) is a promising noninvasive early cancer detection technique via inversing the nuclear size of epithelial cells from collected single scattered light. Conventionally, the nuclear size is inferred by using model-driven least square fitting (LSF) methods, which is time-consuming and challenging for real-time cancer diagnosis. Although data-driven deep-learning-based techniques can be employed to speed up the inverse process, the labeled data are usually limited and the reliability quantification of prediction is lacking. Herein, by synthesizing a large quantity of single scattered spectra with physical priori, a Bayesian-deep-learning (BDL)-based PLSS framework is presented for the first time as far as we know, and is expected to provide model uncertainty of network prediction for the reliability quantification. Further, to explore the applicability of the BDL uncertain estimation, the neural networks for early cancer diagnosis are designed as a convolutional neural network (CNN) classification problem and a fully connected neural network (FNN) regression problem, respectively. They are verified using the single scattered spectra of polystyrene microsphere tissue models and human colorectal lesion tissue obtained by our proposed snapshot PLSS system. The results show that both BDL networks obtain similar nuclear sizes to the ground truth and their inferring speeds are far faster than LSF methods. Further, it is interesting to note that only the CNN classification network can provide effective uncertainty for reliability quantification. The work presents a new paradigm for the real-time diagnosis of early cancer and provides a reference for the design of snapshot PLSS endoscopy.

Aircraft conceptual design using metaheuristic-based reliability optimisation

The reliability optimisation methodology is developed to solve a conceptual design problem of a fixed-wing Unmanned Aerial Vehicle (UAV). The reliability quantification is based on the most probable point (MPP) concept, leading to a double-loop optimisation problem. The design problem is formulated as an outer-loop multiobjective optimisation for the main design problem and an inner-loop optimisation for estimating a reliability index (β) of a design solution. The goal of the outer-loop optimisation is to minimise the aircraft take-off weight, and simultaneously maximise β. The aerodynamic and stability properties of an aircraft solution are calculated by a vortex lattice method (VLM), while various types of empirical weight methods are obtained. The design problem is set up to have uncertainties in calculating aircraft empty weight and aerodynamic coefficients and derivatives. For the inner loop optimisation, the MPP is used for approximating the reliability index and probability of failure (pf). Multiobjective Meta-heuristic with Iterative Parameter Distribution Estimation (MMIPDE) and Success-History based Adaptive Differential Evolution (SHADE) are used for solving the outer- and inner-loop optimisations respectively. Four parameters setting strategies for running metaheuristics are proposed for use with the proposed metaheuristic-based reliability optimisation. The comparative results reveal that the best dynamic parameter setting from this study can reduce runtime by 22.5% compared to the traditional metaheuristic run while maintaining competitive results.

Dynamic spectrum access based on prior knowledge enabled reinforcement learning with double actions in complex electromagnetic environment

The spectrum access problem of cognitive users in the fast-changing dynamic interference spectrum environment is addressed in this paper. The prior knowledge for the dynamic spectrum access is modeled and a reliability quantification scheme is presented to guide the use of the prior knowledge in the learning process. Furthermore, a spectrum access scheme based on the prior knowledge enabled RL (PKRL) is designed, which effectively improved the learning efficiency and provided a solution for users to better adapt to the fast-changing and high-density electromagnetic environment. Compared with the existing methods, the proposed algorithm can adjust the access channel online according to historical information and improve the efficiency of the algorithm to obtain the optimal access policy. Simulation results show that, the convergence speed of the learning is improved by about 66% with the invariant average throughput.

Flatwise to Upright Build Orientations under Three-Point Bending Test of Nylon 12 (PA12) Additively Manufactured by SLS.

This paper describes in detail a series of static tests conducted in a three-point bend configuration on three build orientations (i.e., 0°, 45°, and 90°) of additively manufactured Nylon 12 (PA12) specimens produced with a powder refresh ratio of 50%, using a benchtop industrial SLS platform, Formlabs Fuse 1. The bending strength and flexural elasticity moduli are determined following ISO 173 specifications and by employing a more complex equation that considers the influence of large deflections as per ISO 14125 indications. Statistical variability of experimental data is considered and compared to the results from the literature. Through a fractographic SEM study, the damage morphologies of tested specimens are analyzed and associated with the recorded load-deflection curves for an accurate perception of build orientation-dependent anisotropy in bending properties of AM PA12 SLS specimens. A surprising result of this investigation is that the specimens built with 45° orientation showed superior modulus elasticity in flexure but a low bending strength compared to flatwise oriented specimens. In addition, a Weibull reliability quantification of bending strength is adapted to pinpoint the effects of internal 3D printing flaws (contained within a characteristic highly-stressed volume of material) over the failure probability of the three build orientations in question.

CPSC: Conformal Prediction With Shrunken Centroids for Efficient Prediction Reliability Quantification and Data Augmentation, a Case in Alternative Herbal Medicine Classification With Electronic Nose

In machine learning applications, the reliability of predictions is significant for assisted decision and risk control. As an effective framework to quantify the prediction reliability, conformal prediction (CP) was commonly developed with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor, known as CPKNN. However, the conventional CPKNN suffers from high variance and bias, and long computational time as the feature dimensionality increases. To address these limitations, a new CP framework-CP with shrunken centroids (CPSC) is proposed. It regularizes the class centroids to attenuate the irrelevant features and shrink the sample space for predictions and reliability quantification. We have also compared the CPSC with CP based on SVM, LightGBM, RF on the herbal medicine dataset, with the electronic nose as a case and assessed them in two tasks: 1) off-line prediction: the training set was fixed, and the accuracy on the testing set was evaluated and 2) online prediction with data augmentation: CPKNN and CPSC filtered unlabeled data to augment the training data based on the prediction reliability, and the final accuracy of the testing set was compared. The result shows that CPSC significantly outperformed CPKNN in both two tasks: 1) CPSC reached a significantly higher accuracy with lower computation cost, and with the same credibility output, CPSC generally achieved higher accuracy and 2) the data augmentation process with CPSC robustly manifested a statistically significant improvement in prediction accuracy with different reliability thresholds, and the augmented data were more balanced in classes. This novel CPSC provides higher prediction accuracy and better reliability quantification, which can assist in reliable decision support.

Quantitative Reliability Evaluation of Silicon Carbide-Based Inverters for Multiphase Electric Drives for Electric Vehicles

Abstract Reliability of power converter in the electric drivetrain of a vehicle can be a criterion for the comparison of various converter topologies, cooling system designs and control strategies. Therefore, reliability prediction is important for the design and control of vehicles. This paper presents an approach for quantitative evaluation of the reliability of converters for multiphase motor drives for electric vehicles (EVs) after considering the driving cycle. This paper provides a good background of reliability quantification so that it is easy to extend the presented approach to other applications. The models of subsystems have been selected to have excellent computational efficiency with good accuracy which is necessary for simulating long driving cycles. A simple vehicle model is used to obtain the traction motor torque demand for various points of the driving cycle. A multiphase interior permanent magnet synchronous motor has been used for traction motor. The operating voltage and currents of the motor are found using maximum torque per ampere (MTPA) control of IPMSM. The analytical loss calculation has been used to find the losses of switching devices of the converter. A thermal model of silicon carbide (SiC) MOSFET has been used to calculate junction temperature from the losses. The model developed here gives the failure rate and mean time between failures (MTBF) of switching devices of the inverter, which can be used to determine the failure rate. The model has been used to find the transition probabilities of a Markov model which can be used to quantify the reliability of converters of multiphase electric drives.

Reliability Quantification of Railway Electrification Mast Structure Considering Buckling

This paper aims to quantify and assess the reliability of mast structures as a part of ensuring structure safety. The mast structure is a basic aspect of the overhead line electrification equipment (OHLE) used in railway systems. This structure is very important as the failure of structure leads to the failure of an electric system that supplies the power to the train. To ensure structural safety and reliability, this paper thus analyses the reliability index of the mast, stay tube, and bracket tube structures. According to Eurocode, buckling resistance under compression of these parts were calculated based on specific material properties, and the load condition of these structures is based on Australian Railcorp document TMC331. In this paper, the strength load combination with the wind loading on the wire at 45° on the track is considered in particular as being the worst load combination for structures to bear, and the random variables used to affect reliability probabilistic analysis. Various parameters including self-weight, wind load, dimension parameters, materials, geometrical properties are taken into consideration. Statistical models of these parameters are taken from previous studies. The reliability index value was calculated via quantification of structure reliability using the first-order reliability method (FORM). Finally, a sensitivity analysis is used to evaluate the impacts of yield strength, length, cross-section, density, and load combination on reliability. The obtained results show that increasing length of structure can potentially reduce the reliability of mast structure to buckling resistance while the density of material also plays a major role in the reliability index. The findings will provide the structural safety criteria of the railway mast structure and improve the standard design to mitigate the risks and unplanned maintenance due to the uncertainties.

Reliability of MEMS in Shock Environments: 2000-2020.

The reliability of MEMS in shock environments is a complex area which involves structural dynamics, fracture mechanics, and system reliability theory etc. With growth in the use of MEMS in automotive, IoT, aerospace and other harsh environments, there is a need for an in-depth understanding of the reliability of MEMS in shock environments. Despite the contributions of many articles that have overviewed the reliability of MEMS panoramically, a review paper that specifically focuses on the reliability research of MEMS in shock environments is, to date, absent. This paper reviews studies which examine the reliability of MEMS in shock environments from 2000 to 2020 in six sub-areas, which are: (i) response model of microstructure, (ii) shock experimental progresses, (iii) shock resistant microstructures, (iv) reliability quantification models of microstructure, (v) electronics-system-level reliability, and (vi) the coupling phenomenon of shock with other factors. This paper fills the gap around overviews of MEMS reliability in shock environments. Through the framework of these six sub-areas, we propose some directions potentially worthy of attention for future research.