Modeling Strategy Research Articles

Powder bed fusion with electron beam: The interplay of sintering, porosity, and coordination number in modelling the powder thermal conductivity through a novel tortuosity formulation

For the powder bed fusion with electron beam (PBF-EB) additive manufacturing, properties such as the thermal conductivity of the material surrounding the melting area are critical. Thermal conductivity is influenced by the extremely high temperature reached in a short time and distributed in the building area. This fast temperature growth produces sintering phenomena and the creation of a neck between the particles. Because of this sintering, measuring the thermal conductivity at the process conditions is challenging.This paper proposes an analytical formulation for estimating the effective powder bed thermal conductivity at the PBF-EB conditions, introducing a novel modelling strategy for the tortuosity factor. In a changing net of sintered powder particles, the proposed model for the tortuosity factor considers the neck evolution and the complexity of the heat transfer due to the several heat paths possible through the particle net. To show the effectiveness of the proposed model, the thermal conductivity is evaluated for three 3D structures characterised by an increasing number of powder particles and heat path complexity: a simple cubic, a body centred cubic and a portion of a powder bed. It is shown that thermal conductivity strongly depends on the arrangement of the particles in 3D space, the structure density and the complexity of the heat diffusion path (tortuosity). Also, the numerical results from the proposed model show good agreement when compared with finite element analysis and experimental literature data.

Research on modeling and control strategy of zero-carbon hybrid power system based on the ammonia-hydrogen engine

In the context of carbon neutrality, high efficiency, low carbon, and zero environmental impact have become an essential development direction of internal combustion engines (ICEs). Hydrogen energy has the advantage of having a high calorific value. It also has zero carbon emissions and is considered one of the significant alternative fuels for ICEs. Moreover, ammonia has a high octane number and has an anti-knock effect, which facilitates the mitigation of knock in hydrogen ICEs and potentially mitigates the negative impact of knock on engine volume power. Scholars have carried out some explorations on ammonia-hydrogen ICEs (AHICE), which have been applied in marine power systems. It is anticipated that AHICE will become one of the most significant development directions in the field of vehicle power systems in the future. However, there are still some problems with using AHICEs in passenger cars. The proposed work presented a zero-carbon hybrid power system based on an AHICE. Specifically, the external characteristics curve and power output boundary were obtained by bench experiments of the ICE under wide open throttle (WOT) conditions and diverse engine speeds and λ. Indeed, a hybrid system model consisting of an AHICE and a power battery was built where the AHICE was used to respond to the demanded power and the power battery was used to provide additional power and store the electrical energy converted from braking. Incidentally, an engine control strategy was developed to expand the knock limit and power boundary by dynamically adjusting the ammonia-hydrogen volume ratio. Finally, a hybrid power system simulation model was established based on MATLAB/Simulink. The CLTC-P and WLTC simulation results demonstrated that the zero-carbon hybrid system which comprised AHICE and power battery can work in the high efficiency area. The AHICE demonstrates lower fuel consumption while satisfying power requirements. This work provides a promising route to zero-carbon hybrid technology for the passenger car industry facing the challenge of carbon neutrality.

Validation of ADNEX and IOTA two-step strategy and estimation of risk of complications during follow-up of adnexal masses in low-risk population.

To evaluate the ability of the Assessment of Different NEoplasias in the adneXa (ADNEX) model and the International Ovarian Tumour Analysis (IOTA) two-step strategy to predict malignancy in adnexal masses detected in an outpatient low-risk setting, and to estimate the risk of complications in masses with benign ultrasound morphology managed using clinical and ultrasound follow-up. This single-center study was performed at Hospital Universitari Dexeus, Barcelona, Spain, using interim data from the ongoing prospective observational IOTA Phase-5 (IOTA5) study. The primary aim of the IOTA5 study is to describe the cumulative incidence of complications during follow-up of adnexal masses classified as benign on ultrasound examination. Consecutive patients with an adnexal mass detected between June 2012 and September 2016 in a private center offering screening for gynecological cancer were included and followed up until February 2020. Tumors were classified as benign or malignant based on histology (if patients underwent surgery) or the outcome of clinical and ultrasound follow-up at 12 (range, 10-14) months. Multiple imputation was used when outcomes were uncertain. The ability of the ADNEX model without CA125 and of the IOTA two-step strategy to distinguish benign from malignant masses was evaluated retrospectively using the prospectively collected data. We assessed performance with regard to discrimination (area under the receiver-operating-characteristics curve (AUC)), calibration, classification (sensitivity and specificity) and clinical utility (Net Benefit). In the group of patients with a mass judged to be benign who were selected for conservative management, we evaluated the occurrence of spontaneous resolution or any mass complication during the first 5 years of follow-up by assessing the cumulative incidence of malignancy, torsion, cyst rupture and minor mass complications (inflammation, infection or adhesions) and the time to occurrence of an event. A total of 2654 patients were recruited to the study. After application of exclusion criteria, 2039 patients with a newly detected mass were included for the model validation. Of those, 1684 (83%) masses were benign, 49 (2%) masses were malignant and, for 306 (15%) masses, the outcome was uncertain and therefore imputed. The AUC was 0.95 (95% CI, 0.89-0.98) for ADNEX without CA125 and 0.94 (95% CI, 0.88-0.97) for the two-step strategy. Calibration performance could not be meaningfully interpreted because the small number of malignancies resulted in very wide confidence intervals. The two-step strategy had better clinical utility than did the ADNEX model at malignancy risk thresholds < 3%. There were 1472 (72%) patients whose mass was judged to be benign based on pattern recognition by an experienced ultrasound examiner and were managed with clinical and ultrasound follow-up. In this group, the 5-year cumulative incidence was 66% (95% CI, 63-69%) for spontaneous resolution of the mass, 0% (95% CI, 0-0.2%) for torsion, 0.1% (95% CI, < 0.1-0.4%) for cyst rupture, 0.2% (95% CI, 0.1-0.6%) for a borderline tumor and 0.2% (95% CI, 0.1-0.6%) for invasive malignancy. The ADNEX model and IOTA two-step strategy performed well to distinguish benign from malignant adnexal masses detected in a low-risk population. Conservative management is safe for masses with a benign ultrasound appearance in this population. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Developing an annual global Sub-National scale economic data from 1992 to 2021 using nighttime lights and deep learning

The Gross Domestic Product (GDP) per capita is one of the most widely used socioeconomic indicators, serving as an integral component for climate change impact analysis. However, a national scale assessment may induce considerable bias because it conceals any internal variations within a country. The lack of a long-term sub-national scale GDP data is a substantive hinderance. Leveraging the close relationship between nighttime lights and GDP, we address this gap by developing a novel methodological framework in two steps. First, under the modeling philosophy of spatial statistics, we developed a novel approach based on deep and machine learning techniques to establish a complex mapping between two inconsistent nighttime lights (NTL) datasets: the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP) and the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (VIIRS). The models achieve accuracies ranging from 0.945 to 0.980 (correlation coefficients). By taking the estimations ensemble of the two techniques, the time series of DMSP data was extended to 2021. Next, a novel modeling strategy based on multi-layer perceptron was developed to derive the non-linear relationship between NTL and GDP per capita at sub-national scale to alleviate scale effects at this granularity, while explicitly capturing regional heterogeneity effect. The trained models achieve average accuracies of 0.967, 0.959, and 0.959 on the training, validation, and test sets, respectively. We evaluate the developed dataset at the global, national, and sub-national scales from various perspective, and the results offer solid evidence on the reliability of the estimated economic data. By linking to historical global climate change data, we quantify global economic losses attributed to extreme heat to demonstrate how the estimated GDP data can be useful in the climate change impact analysis.

A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases

BackgroundDescribing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection.MethodsThis study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China.ResultsWhen the spatio-temporal variation was small (time delay coefficient: 0.1–0.2, spatial sparsity:0.1–0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2–0.3, spatial sparsity: 0.6–0.9).ConclusionThis study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators—the time delay coefficient and spatial sparsity—into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

A novel global modelling strategy integrated dynamic kernel canonical variate analysis for the air handling unit fault detection via considering the two-directional dynamics

As an indispensable component of air-conditioning system, the air handling unit (AHU) always exhibits strong nonlinearity and two-directional dynamics (time-wise and batch-wise dynamics). However, existing monitoring strategies cannot adequately figure out the AHU's two-directional dynamics from the nonlinear data. To boost the AHU's fault detection performance, a novel global modelling scheme integrated dynamic kernel canonical variate analysis (GDKCVA) is developed in this paper. The first contribution is to collect datasets from various days to develop a three-way training dataset and then normalize it along the batch-wise to suppress the batch-wise dynamics and variables' correlations. The second contribution is to establish the dynamic kernel CVA by integrating autoregressive moving average exogenous framework to more effectively confront the time-wise dynamics and variables' nonlinear relationships. The kernel matrices of augmented batch dataset are determined utilizing the kernel trick that transforms nonlinear data into high-dimensional linear space by leveraging kernel function. The third contribution is to propose a novel global modelling strategy to further eliminate the batch-wise dynamics by computing the total average kernel matrices from all the normal batch datasets. Finally, experiments and comparisons on the ASHRAE RP-1312 datasets are carried out to assess the suggested GDKCVA's fault detection capability. Compared with the kernel CVA (KCVA), the three-way training dataset based CVA (TCVA) and the three-way training dataset based KCVA (TKCVA), the proposed GDKCVA has the highest fault detection rates and its average fault detection rates of three monitoring statistics for eleven fault patterns reach 99.38 %, 99.15 % and 100 %, respectively.

Comparative Analysis of CNN Algorithms for Mushroom Classification with Proposed Lightweight CNN Model

The classification of mushroom species presents significant ecologic and health-related challenges; advancement in classification techniques is required to gain reliable identifications. This study aims to explain a methodology that was devised and evaluated in the development of a novel, lightweight Convolutional Neural Network (CNN) designed specifically for the task of mushroom classification. The paper provides a custom CNN model that is computationally cost-effective and capable of high-precision classification, fit for real-time usage. Hence, the proposed model was evaluated on this dataset of curated mushroom images with traditional classifiers and state-of-the-art CNN architectures, such as EfficientNet-B7, ResNet50, InceptionV3, and MobileNetV2. The custom model is depth-wise separations engineered in such a way that while they reduce the computational load, they don't compromise the effectiveness of the model. The custom model achieved a test score of 0.68, which is moderate compared to more established models such as EfficientNet-B7 or ResNet50. This approach helps the model function effectively even on platforms having low computational resources. A comprehensive evaluation reveals that a custom CNN has reasonable accuracy in the identification of different mushroom species vis-à-vis existing models, but also significantly lightens the classification process.

Modeling and Rotation Control Strategy for Space Planar Flexible Robotic Arm Based on Fuzzy Adjustment and Disturbance Observer

In precise space operation tasks, the impact of disturbing torques on the space flexible robotic arm (SFRA) cannot be ignored. Besides, the slender structure of the SFRA is very likely to generate vibration of the robotic arm. These are all potential hidden dangers in space safety. To quantify the potential risk, an accurate dynamics model of the SFRA considering the disturbing torque is built by Lagrange principle and the assumed modal method (AMM). Moreover, the effects of the disturbing torque, modal order and nonlinear terms on the deformation accuracy of the SFRA are compared. It is observed that the simplified dynamics model with neglecting the nonlinear terms (NNTs) has a high model accuracy and be easily solved. Therefore, the NNTs simplified model is chosen for deriving the transfer function of the SFRA. The parameters of the PI controller are adjusted in real time based on fuzzy rules to reduce the tracking error in the SFRA. In addition, the disturbance observer is designed to observe and compensate of the disturbance torque in the SFRA. The control method of adjusting controller parameters with fuzzy rules based on the disturbance observer greatly improves the rotational control accuracy of the SFRA. Finally, the validity of aforementioned control strategy is confirmed by simulation analysis and experimental results.

Exploration of Curvature and Stiffness Dual-Regulated Breast Cancer Cell Motility by a Motor-Clutch Model and Cell Traction Force Characterization.

The migration of breast cancer cells is the main cause of death and significantly regulated by physical factors of the extracellular matrix (ECM). To be specific, the curvature and stiffness of the ECM were discovered to effectively guide cell migration in velocity and direction. However, it is not clear what the extent of effect is when these dual-physical factors regulate cell migration. Moreover, the mechanobiology mechanism of breast cancer cell migration in the molecular level and analysis of cell traction force (CTF) are also important, but there is a lack of systematic investigation. Therefore, we employed a microfluidic platform to construct hydrogel microspheres with an independently adjustable curvature and stiffness as a three-dimensional substrate for breast cancer cell migration. We found that the cell migration velocity was negatively correlated to curvature and positively correlated to stiffness. In addition, curvature was investigated to influence the focal adhesion expression as well as the assignment of F-actin at the molecular level. Further, with the help of a motor-clutch mathematical model and hydrogel microsphere stress sensors, it was concluded that cells perceived physical factors (curvature and stiffness) to cause changes in CTF, which ultimately regulated cell motility. In summary, we employed a theoretical model (motor-clutch) and experimental strategy (stress sensors) to understand the mechanism of curvature and stiffness regulating breast cancer cell motility. These results provide evidence of force driven cancer cell migration by ECM physical factors and explain the mechanism from the perspective of mechanobiology.

AC loss calculation of no-electrical-insulation HTS magnets using a field-circuit coupling method

Abstract No-electrical-insulation (NEI) magnets are gradually exhibiting significant appeal due to their robust thermal stability and elevated mechanical strength. However, when exposed to AC conditions, these magnets will suffer more significant AC losses in dynamic electromagnetic devices, such as motors and maglev systems. Presently, the numerical methods for predicting the electromagnetic and loss behavior of large-scale NEI magnets entail high computation costs due to the substantial degrees of freedom or complicated modeling strategies. Thus, we propose a fully finite element method, referred to as the field-circuit coupling method, to efficiently assess the overall behavior of NEI magnets while preserving adequate accuracy. This method couples the T-A formula and the single-turn equivalent circuit through a global voltage, to avoid the costly and complicated inductance calculations, and to simultaneously consider the induced current. By further integrating the homogenization method, the calculation speed can be increased up to ten times. Additionally, we study the critical current, and the electromagnetic and loss behavior of the NEI magnets based on the proposed model. We identify some measurement methods that offer more precise estimations of the critical current and the turn-to-turn contact resistance of NEI magnets. Meanwhile, the results indicate the severe impact of high AC fields on the losses, and emphasize the importance of a reliable shielding structure for operational safety. Finally, the influence of turn-to-turn contact resistivity on the loss behavior is also investigated, which can provide valuable insights for the design of NEI magnets in dynamic electromagnetic devices.

Industrial image anomaly detection based on multi Gaussian discriminant model and robust core set

Abstract To address the issue of false positive (FP) detections in image anomaly detection caused by the loss of low-frequency features when dealing with high-dimensional feature distributions, we propose the multi-layer Gaussian discriminant anomaly detection model (MGAD). This model utilizes distance metrics based on multiple normal distributions to perform anomaly detection. By mining multi-layer feature combinations from normal samples and incorporating a Gaussian mixture model strategy for pixel-by-pixel probability density estimation, a weighting mechanism is designed to emphasize the role of low-frequency features in Gaussian space. This approach effectively models data collections that do not follow a single normal distribution as a mixture of several Gaussian distributions, thereby reducing false detections. Additionally, we propose a method for calculating the minimum Mahalanobis distance based on the estimation of the minimum covariance determinant. By identifying a subset with the smallest covariance matrix determinant, this method enhances the robust estimation of the data’s central position and spread, thereby reducing the impact of outliers. On the MVTec-AD dataset, MGAD demonstrates outstanding performance with an anomaly detection area under the receiver operating characteristic curve (AUROC) of 98.8%, the anomaly localization AUROC of 98.2%, and the per-class true negative rate for normal samples of 93.1%. Compared with the state-of-the-art models, MGAD improves the detection accuracy for normal samples by 3.6%, demonstrating the best performance among all models. These results highlight the model’s excellent capability in anomaly recognition and reduction of FPs.

Role of landslide sampling strategies in susceptibility modelling: types, comparison and mechanism

Role of landslide sampling strategies in susceptibility modelling: types, comparison and mechanism

Getting the intermolecular forces correct: introducing the ASTA strategy for a water model.

Having a force field for water providing good bulk properties is paramount for modern studies of most biological systems. Some of the most common three-site force fields are TIP3, SPC/ε or OPC3, providing a decent range of bulk properties. That does not mean though, that they have realistic inter-atomic forces. These force fields have been parameterized with a top-down approach, meaning, by fitting the force field parameters to the experimental bulk properties. This approach has been the governing strategy also for many variants of four- and more-site models. We test a bottom-up approach, in which the force field is parameterized by optimizing the non-bonded inter-atomic forces. Our philosophy is that correct inter-atomic forces lead to correct geometrical and dynamical properties. The first system we try to optimize with the accurately system tailored atomic (ASTA) approach is water, but we aim to eventually probe other systems in the future as well. We applied our ASTA strategy to find a good set of parameters providing accurate bulk properties for the simple three-site force field forms, and also for AMOEBA, a more detailed and polarizable force field. Even though our bottom-up approach did not provide satisfactory results for the simple three-site force fields (with fixed charges), for the case of the AMOEBA force field it led to a modification of the original strategy, giving very good intra- and inter-molecular forces, as compared to accurate quantum chemically calculated reference forces. At the same time, important bulk properties, in this study restricted to the density and diffusion, were accurately reproduced with respect to the experimental values.

Spatial guided image captioning: Guiding attention with object's spatial interaction

AbstractNowadays relational position embedding is widely used in many large multi‐modal models. It begins with relational captioning (a branch of image captioning) and contains two procedures: geometric modelling and prior attention. However, there are some problems that remain unsolved in the conventional procedures. This paper reviews the shortcomings of geometric modelling and prior attention. Then, a new framework called relational guided transformer (RGT) is proposed to verify the authors' conclusion from the origin of relational position embedding—relational captioning. Specifically, RGT has two simple but effective improvements in geometric modelling and prior attention: (1) A machine‐learned geometric modelling strategy called multi‐task geometric modelling (MTG) is used under multi‐task learning, replacing the original hand‐made geometric feature. (2) The effectiveness of multiple kinds of prior attention is discussed and preserved in a better form, which is called spatial guided attention (SGA) to integrate the geometric prior knowledge. Extensive experiments on MSCOCO and Flickr30k have been performed to investigate the effectiveness of each module and prove our argument. The superiority of the model comparing to the authors' baseline has also been proven on the offline evaluation with the “Karpathy” test split of both datasets.

Effect of Environmental and Operating Conditions on Partial Discharge Activity in Electrical Machine Insulation: A Comprehensive Review

Electrical machines for transportation applications are subjected to harsh environmental conditions during their operations. Partial discharge (PD), which is one of the main reasons for insulation failure, is greatly affected by ambient conditions (i.e., temperature, pressure, and humidity). Countless efforts are made for a comprehensive understanding of the physics of PD under variable environmental factors. This paper aims to review recent works addressing temperature, pressure, and humidity impact on PD activity. The main content of the paper is organized into three sections dealing with each environmental factor. In every section, relevant publications are reviewed considering the type of samples tested, voltage waveform applied, mutual effects, and the most common PD modeling strategies used. The applicability of the PD measurements for PD risk assessment is also discussed. Based on the review, the current progress in understanding the environmental effects on the PD inception mechanism and PD characteristics is presented and discussed in detail, and future research trends in this field are outlined.

A simulation strategy for fatigue modeling of delamination in composite structures under multiple loading conditions considering loading history and [formula omitted]-curve effects

This work evaluates the ability of cohesive zone modeling-based approaches to predict delamination in composite materials that develop large process zones under complex loading conditions. The R-curve effects subjected to static and fatigue loading under multiple loading modes, considering the loading history, are analyzed. To this end, the delamination predictions of a state-of-the-art CZM-based simulation strategy are evaluated by blind simulation of a validation benchmark test. The validation test promotes a non-self-similar delamination scenario, including a process zone that evolves under different loading mode conditions with a non-straight leading delamination front. Good delamination prediction accuracy is achieved. In addition, insights into the relationship between the features of the simulation strategy and the physics of the delamination process are discussed. With regard to the limitations of the simulation strategy, particular attention should be paid to modeling the contribution of an evolving process zone based on the loading mode history.

Effective knockdown-replace gene therapy in a novel mouse model of DNM1 developmental and epileptic encephalopathy

Effective gene therapy for gain-of-function or dominant-negative disease mutations may require eliminating expression of the mutant copy together with wildtype replacement. We evaluated such a “knockdown-replace” strategy in a mouse model of DNM1 disease, a debilitating and intractable neurodevelopmental epilepsy. To challenge the approach robustly, we expressed a patient-based variant in GABAergic neurons - which resulted in growth delay and lethal seizures evident by postnatal week three - and delivered to newborn pups an AAV9-based vector encoding a ubiquitously-expressed, Dnm1-specific interfering RNA (RNAi) bivalently in tail-to-tail configuration with a neuron-specific, RNAi-resistant, codon-optimized Dnm1 cDNA. Pups receiving RNAi or cDNA alone fared no better than untreated, whereas the vast majority of mutants receiving modest doses survived with almost full growth recovery. Synaptic recordings of cortical neurons derived from treated pups revealed that significant alterations in transmission from inhibitory to excitatory neurons were rectified by bivalent vector application. To examine the mutant transcriptome and impact of treatment, we used RNAseq and functional annotation clustering. Mutants displayed abnormal expression of >1000 genes in highly significant and relevant functional clusters, clusters that were abrogated by treatment. Together these results suggest knockdown-replace as a potentially effective strategy for treating DNM1 and related genetic neurodevelopmental disease.

(Invited) Computational Models of Proton-Coupled Electron Transfer in Energy Conversion and Storage

Proton-coupled electron transfer (PCET) is a fundamental class of chemical reactions that is central to many electrochemical energy conversion and storage processes. In this talk, I will discuss recent theoretical model development for interfacial and intermolecular PCET reactions. First, I will describe a rate constant model for the reaction between reduced anatase TiO2 surfaces and a 4-MeO-TEMPO nitroxyl radical. The rate constants are modeled using vibronically nonadiabatic PCET theory, where the transferring proton and electron are treated quantum mechanically. Hybrid functional periodic density functional theory (DFT) calculations are used to derive model parameters such as driving forces, reorganization energies, and proton vibrational wavefunctions. This modeling strategy highlights the role of inner-sphere bond reorganization and excited vibronic states on the PCET reactivity of metal oxides. Next, I will show how similar computational approaches can be used to predict whether electrochemical PCET is likely to proceed through concerted electron–proton transfer or sequential electron transfer and proton transfer steps. Using PCET reactions between redox-active quinones and functionalized imidazole bases as an example, mechanistic predictions are informed by DFT-calculated potential energy surfaces. These PCET modeling strategies have broad applicability to kinetic studies of electrochemical energy conversion and storage processes.

A Comprehensive Comparison of Stable and Unstable Area Sampling Strategies in Large-Scale Landslide Susceptibility Models Using Machine Learning Methods

A Comprehensive Comparison of Stable and Unstable Area Sampling Strategies in Large-Scale Landslide Susceptibility Models Using Machine Learning Methods

Research on Equipment Reliability Modeling and Periodic Maintenance Strategies in Dynamic Environment

Equipment along the railway has different degradation paths and reliability in different environments. It is of great significance to study the reliability and maintenance strategy of equipment in dynamic environment to ensure the safety of train operation. In this paper, a method of equipment reliability modeling and periodic maintenance decision-making in dynamic environment is proposed. A reliability model considering the degradation and impact of environmental changes and considering the characteristics of the equipment is established. A new concept of maintenance cycle is defined, and a decision model of equipment periodic maintenance is established. Finally, through numerical simulation, it is concluded that under severe environment, the maintenance time is shortened by 29508h and 31639h respectively, and the maintenance cost is increased by 41.4% and 28.2% respectively. Furthermore, the change trend and adjustment scheme of the strategy under different operating environments are analyzed, which verifies the correctness and effectiveness of the model in this paper.