Source Model Research Articles

A Curriculum-Style Self-Training Approach for Source-Free Semantic Segmentation.

Source-free domain adaptation has developed rapidly in recent years, where the well-trained source model is adapted to the target domain instead of the source data, offering the potential for privacy concerns and intellectual property protection. However, a number of feature alignment techniques in prior domain adaptation methods are not feasible in this challenging problem setting. Thereby, we resort to probing inherent domain-invariant feature learning and propose a curriculum-style self-training approach for source-free domain adaptive semantic segmentation. In particular, we introduce a curriculum-style entropy minimization method to explore the implicit knowledge from the source model, which fits the trained source model to the target data using certain information from easy-to-hard predictions. We then train the segmentation network by the proposed complementary curriculum-style self-training, which utilizes the negative and positive pseudo labels following the curriculum-learning manner. Although negative pseudo-labels with high uncertainty cannot be identified with the correct labels, they can definitely indicate absent classes. Moreover, we employ an information propagation scheme to further reduce the intra-domain discrepancy within the target domain, which could act as a standard post-processing method for the domain adaptation field. Furthermore, we extend the proposed method to a more challenging black-box source model scenario where only the source model's predictions are available. Extensive experiments validate that our method yields state-of-the-art performance on source-free semantic segmentation tasks for both synthetic-to-real and adverse conditions datasets.

Incremental model transformations with triple graph grammars for multi-version models and multi-version pattern matching

AbstractLike conventional software projects, projects in model-driven software engineering require adequate management of multiple versions of development artifacts, importantly allowing living with temporary inconsistencies. In previous work, we have introduced multi-version models for model-driven software engineering, which allow checking well-formedness and finding merge conflicts for multiple versions of the same model at once. However, situations where different models are linked via automatic model transformations also have to be handled for multi-version models. In this paper, we propose a technique for jointly handling the transformation of multiple versions of a source model into corresponding versions of a target model. This enables the use of a more compact representation that may afford improved execution time of both the transformation and further analysis. Our approach is based on the well-known formalism of triple graph grammars and the aforementioned encoding of model version histories called multi-version models. In addition to batch transformation of an entire history, the technique covers incremental synchronization of changes in the framework of multi-version models. Our solution is complemented by a dedicated pattern matching technique for multi-version models. We show the correctness of our approach with respect to the standard semantics of triple graph grammars and conduct an empirical evaluation to investigate the performance of our technique regarding execution time and memory consumption. Our results indicate that the proposed solution affords lower memory consumption and may improve execution time for batch transformation of large version histories, but can also come with computational overhead in unfavorable cases.

Equivalent Heat Source Model of Thermal Relay Contact Based on Surface Roughness of Silver–Magnesium–Nickel Contact

In a sealed electromagnetic relay, the change in the surface roughness mainly depends on the collision wear between the contact and the moving reed and the ablation effect of the arc on the contact surface based on the strong correlation between the contact resistance and the surface roughness of the Ag-Mg-Ni contact. With a change in contact resistance, the contact temperature increase in a hermetically sealed electromagnetic relay (HSER) is greatly affected. Under extreme overload conditions, the contact surface is severely ablated by the arc, and the roughness increases rapidly with the number of cycles, which greatly affects the contact resistance of the contact surface and the reliability of the relay. A thermal model of a relay contact system based on the surface roughness of Ag-Mg-Ni contacts was established in this paper by analyzing the effect of an arc on the surface roughness of Ag-Mg-Ni contacts under heavy overload conditions. The arc image of the Ag-Mg-Ni contact was recorded using a double-axis arc photographing platform, and the moving track of the arc center under overload conditions was drawn. This paper explored the patterns of arc center movement on the contact surface and the effects of the arc on the surface roughness of the contacts by analyzing the probabilities of the arc center appearing in various locations. A mathematical model correlating the number of contact cycles with contact resistance was established. Subsequently, a finite element simulation model for the equivalent heat source of the contact was developed. The theoretical model error was less than 10%. The accuracy of the equivalent heat source model was verified by comparing the measured data with the simulation results.

Origin of Intercrystalline Brine Formation in the Balun Mahai Basin, Qaidam: Constraints from Geochemistry and H-O-Sr Isotopes

The Balun Mahai Basin (BLMH), located in the northern Qaidam Basin (QB), is endowed with substantial brine resources; however, the genetic mechanisms and potential of these brine resources remain inadequately understood. This study investigated the intercrystalline brine (inter-brine) in BLMH, performing a comprehensive geochemical analysis of elemental compositions and H-O-Sr isotopes. It evaluated the water source, solute origin, evolutionary process, and genetic model associated with this brine. Moreover, a mass balance equation based on the 87Sr/86Sr isotopic ratio was developed to quantitatively evaluate the contributions of Ca-Cl water and river water to the inter-brine in the study area. The results suggest that the hydrochemical type of inter-brine in the north part of BLMH is Cl-SO4-type and in the south part is Ca-Cl-type. The solutes in brine are mainly derived from the dissolution of minerals such as halite, sylvite, and gypsum. The hydrochemical process of brine is controlled by evaporation concentration, water–rock interaction, and ion exchange interaction. Hydrogen and oxygen isotopes suggest that the inter-brine originates from atmospheric precipitation or ice melt water and has experienced intense evaporation concentration and water–rock interaction. The strontium isotopes suggest that the inter-brine was affected by the recharge and mixing of Ca-Cl water and river water, which controlled the spatial distribution and formation of brine hydrochemical types. The analysis of ionic ratios suggest that the inter-brine is derived from salt dissolution and filtration, characterized by poor sealing and short sealing time in the salt-bearing formation. The differences in hydrochemical types and spatial distribution between the north and the south are fundamentally related to the replenishment and mixing of these two sources, which can be summarized as mixed origin model of “dissolution and filtration replenishment + deep replenishment” in BLMH.

Evaluation of the Domino Effect Caused by Pool Fires in a Tank Farm

Major hazard installations (MHIs), such as oil refineries, petrochemical plants, and terminals, use large-capacity storage tanks for storing crude oil and by-products. Pool fire is one of the most common types of storage tank fire incidents. This technical article aims to investigate the domino effect resulting from a pool fire in a tank farm consisting of eight large floating-roof storage tanks, with a focus on four specific tanks. A crude oil storage tank was selected as the primary tank (source tank). Point source and plume solid models were used to estimate the thermal radiation. It has been noted that the thermal radiation from the source tank to the adjacent tank in the same dike exceeds the threshold heat radiation level and might result in a domino effect; however, the thermal radiation from the source tank alone does not reach the threshold level for the tanks in the other dike. Additionally, it was found that the thermal radiation from both the primary and secondary tanks just reaches the threshold level for the farther-away tank in the other dike. The domino effect occurs provided that the firefighting system is not activated and the emergency response team does not intervene within ten minutes.

Analytical Approach of three-dimensional Flawed Multilayered Structure for EC Inspection

This article is devoted to the development of two current source models for the analysis of 3D electromagnetic phenomena in a conductive part of spherical geometry based on the second-order magnetic vector potential method for eddy current nondestructive testing (ECNDT). The first model, for a homogeneous single-layer sphere, allows the analytical calculation of all electromagnetic quantities in the charge. The second allows to treat in addition to the three-dimensional aspect of the problem, the heterogeneity of the charge (with several layers of different conductivities). An investigations using a multifrequency system have shown better results in detection of small cracks. The observations take about the calculation of normalized impedance based on dissipate energy and Joule losses in conductive material.

Investigation of the Effect of Integrated Offset, GPS, and InSAR Data in the Stochastic Source Modeling of the 2002 Denali Earthquake

This study investigates the effect of geological field measurement (offset), global positioning system (GPS), and interferometric synthetic aperture radar (InSAR) data on the estimation of the co-seismic earthquake displacements of the 2002 Denali earthquake. The analysis is conducted using stochastic source modeling. Uncertainties associated with each dataset limit their effectiveness in source model selection and raise questions about the adequate number of datasets and their type for reliable source estimation. To address these questions, stochastic source models with heterogeneous earthquake slip distributions are synthesized using the von Kármán wavenumber spectrum and statistical scaling relationships. The surface displacements of the generated stochastic sources are obtained using the Okada method. The surface displacements are compared with the available datasets (i.e., offset, GPS, and InSAR) individually and in an integrated form. The results indicate that the performance of stochastic source generation can be significantly improved in the case of using GPS data and in the integrated case. Overall, based on the case study of the 2002 Denali earthquake, the combined use of all available datasets increases the robustness of the stochastic source modeling method in characterizing surface displacement. However, GPS data contribute more than InSAR and offset data in producing reliable source models.

Diffuse radio sky models using large-scale shapelets

Sky models used in radio interferometric data-processing primarily consist of compact and discrete radio sources. When there is a need to model large-scale diffuse structure such as the Galaxy, specialized source models are sought after for the sake of simplicity and computational efficiency. We propose the use of shapelet basis functions for modeling the large-scale diffuse structure in various radio interferometric data-processing pipelines. The conventional source model construction using shapelet basis functions is restricted to using images of smaller size due to limitations in computational resources such as memory. We propose a novel shapelet decomposition method to lift this restriction, enabling the use of images of millions of pixels (as well as a wide spectral bandwidth) for building models of large-scale diffuse structure. Furthermore, the application of direction-dependent errors onto diffuse sky models is an expensive operation that is often performed as a convolution. We propose using some specific properties of shapelet basis functions to apply these direction-dependent errors as a product of the model coefficients, which avoids the need for convolution. We provide results based on simulations and real observations. In order to measure the efficacy of our proposed method in modeling large-scale diffuse structure, we considered the direction-dependent calibration of simulated as well as real LOFAR observations that have a significant number of diffuse large-scale structure. The results show that by including large-scale shapelet models of the diffuse sky, we are able to overcome a major problem of existing calibration techniques, which do not model this large-scale diffuse structure, that is, the suppression of this large-scale diffuse structure because the model is incomplete.

Strategies for comparison of modern probabilistic seismic hazard models and insights from the Germany and France border region

Abstract. The latest generation of national and regional probabilistic seismic hazard assessments (PSHAs) in Europe presents stakeholders with multiple representations of the hazard in many regions. This raises the question of why and by how much seismic hazard estimates between two or more models differ, not only where models overlap geographically but also where new models update existing ones. As modern PSHA incorporates increasingly complex analysis of epistemic uncertainty, the resulting hazard is represented not as a single value or spectrum but rather as probability distribution. Focusing on recent PSHA models for France and Germany, alongside the 2020 European Seismic Hazard Model, we explore the differences in model components and highlight the challenges and strategy for harmonising the different models into a common PSHA calculation software. We then quantify the differences in the source model and seismic hazard probability distributions using metrics based on information theory, illustrating their application to the Upper Rhine Graben region. Our analyses reveal the spatial variation in and complexity of model differences when viewed as probability distributions and highlight the need for more detailed transparency and replicability of the models when used as a basis for decision-making and engineering design.

Diagnostics-LLaVA

The recent advancements in the area of Large language models (LLMs) has opened horizons for conversational assistant-based intelligent models capable of interpreting images, and providing textual response, also known as Visual language models (VLMs). These models can assist equipment operators and maintenance technicians in the complex Prognostics and Health Management (PHM) tasks such as diagnostics of faults, root cause analysis and repair recommendation. Significant open source contributions in the area of VLMs have been made. However, models trained on general data fail to perform well in complex tasks in specialized domains such as diagnostics and repair of industrial equipment. Therefore, in this paper we discuss our work on development of Diagnostics-LLaVA, a VLM suitable for interpreting images of specific industrial equipment and provide better response than existing open source models in PHM tasks such as fault diagnostics and repair recommendation. We introduce Diagnostics-LLaVA based on the architecture of LLaVA, and created one instance of the Diagnostics-LLaVA for the automotive repair domain, referred to as Automotive-LLaVA. We demonstrate that our proposed Automotive-LLaVA model performs better than the state-of-the-art open source visual language models such as mPlugOWL and LLaVA in both qualitative and quantitative experiments.

The source characteristics and ground motion parameters measure with structural damages of Mw6.4, 2018 Hualien earthquake Taiwan from GPS data

On February 6, 2018, a moderate earthquake with a magnitude of ML6.2, known as the 0206 earthquake, struck Hualien in eastern Taiwan, resulting in significant destruction, 17 fatalities, and over 300 injuries. This research investigates the kinematic source model of the earthquake by inverting coseismic Global Positioning System (GPS) displacement recordings from the area surrounding the epicenter. The inverted source model indicates that most asperities above the hypocenter are minor, with large slips occurring at depths shallower than 10 km, which contributed to the severe damage in Hualien City. Additionally, we calculated the Coulomb stress changes at depths ranging from 6 km to 14 km, revealing that most aftershocks within a month occurred within a Coulomb stress range of >0.1 bar, extending from shallow to deep and from southwest to northeast of the epicenter. Notably, despite high Coulomb stress, few aftershocks were generated in the western part of the epicenter, suggesting a seismic gap that may lead to a more significant earthquake in the future. Two years later, two moderate earthquakes (ML=5) occurred at depths of 17.53 and 12.1 km in the region where Coulomb stress had increased, validating our predictions. Furthermore, by applying the derived seismic source model and a stochastic semi-empirical technique to assess five parameters related to building damage, we found that all damaged buildings were within the danger alert level, with some located along the Meilun Fault, indicating that the destruction was due to the combined effects of the 0206 earthquake and the fault's induced movement.

Evaluation of the Predictive Capability of CMA Climate Prediction System Model for Summer Surface Heat Source on the Tibetan Plateau

Surface heat source (SHS) is a crucial factor affecting local weather systems. Particularly SHS on the Tibetan Plateau (TP) significantly influences East Asian atmospheric circulation and global climate. Accurate prediction of summer SHS on the TP is of urgent demand for economic development and local climate change. To evaluate the performance of SHS on the TP, the observed SHS data from the eleven sites on the TP verified against CRA40-land (CRA) is evidenced significantly better than ERA5-land (ERA5), another widely used reanalysis. The predictive capability of the CMA Climate Prediction System Model (CMA-CPS) for SHS on the TP was assessed using multiple scoring methods, including the anomaly correlation coefficient and temporal correlation coefficient, among others. Furthermore, relative variability and trend analysis were conducted. Finally, based on these assessments, the causes of the biases were preliminarily discussed. The CMA-CPS demonstrates a reasonable ability to predict the spatial distribution patterns of SHS, sensible heat (SH), and latent heat (LH) on the TP in summer. Specifically, the prediction results of SHS and LH exhibit an “east-high and west-low” distribution, while the distribution of the predicted SH is opposite. Nevertheless, the predicted values are generally lower than CRA, particularly in interannual variations and trends. Among the predictions, LH exhibits the highest temporal correlation coefficients, consistently above 0.6, followed by SHS, while SH predictions are less accurate. The spatial distribution and skill scores indicate that LH on the TP contributes more significantly to SHS than SH in summer. Furthermore, discrepancies in the predictions of surface temperature gradients, ground wind speed, and humidity on the TP may partly explain the biases in SHS and their components.

Acoustic excitation source modeling for flow-induced noise of hermetic compressor using FSI analysis

Acoustic excitation source modeling for flow-induced noise of hermetic compressor using FSI analysis

The Multi‐Segment Complexity of the 2024 MW ${M}_{W}$ 7.5 Noto Peninsula Earthquake Governs Tsunami Generation

AbstractThe 1 January 2024, moment magnitude 7.5 Noto Peninsula earthquake ruptured in complex ways, challenging analysis of its tsunami generation. We present tsunami models informed by a 6‐subevent centroid moment tensor (CMT) model obtained through Bayesian inversion of teleseismic and strong motion data. We identify two distinct bilateral rupture episodes. Initial, onshore rupture toward the southwest is followed by delayed re‐nucleation at the hypocenter, likely aided by fault weakening, causing significant seafloor uplift to the northeast. We construct a complex multi‐fault uplift model, validated against geodetic observations, that aligns with known fault system geometries and is critical in modeling the observed tsunami. The simulations can explain tsunami wave amplitude, timing, and polarity of the leading wave, which are crucial for tsunami early warning. Upon comparison with alternative source models and analysis of 2000 multi‐CMT ensemble solutions, we highlight the importance of incorporating complex source effects for realistic tsunami simulations.

AI-driven data fusion modeling for enhanced prediction of mixed-mode I/III fracture toughness

This research evaluates the use of artificial intelligence to enhance the accuracy of predictions for mixed-mode I/III fracture toughness in polymethyl methacrylate . Traditionally, assessing fracture toughness relies heavily on destructive testing methods, specifically using edge-notch disc bend specimens subjected to three-point bending tests. These established methods are not only expensive and time-consuming but also frequently limited by the availability of data. To address these challenges, this study introduces a data fusion source modeling approach. This approach integrates primary fracture toughness test data with secondary predictive data derived from the maximum tangential stress criterion and the local strain energy density criterion. By employing adaptive boosting and general regression neural network algorithms, the models developed in this research demonstrate a marked improvement in predictive performance compared to traditional primary source models. Additionally, a feature importance analysis using Shapley Additive exPlanations values reveals that the mode mixity parameter, specimen thickness, and radius are critical factors influencing fracture toughness. The study highlights that while mode mixity emerges as the most significant factor, a reduction in specimen thickness generally leads to decreased fracture toughness, whereas an increase in radius has a more complex, often negative, effect. The results of this study indicate that AI-powered models using data fusion can overcome limitations related to data scarcity, enabling more accurate predictions in fracture mechanics. Furthermore, this approach provides a pathway for utilizing AI in other engineering domains where data sets are limited.

Real-time modeling of transient crustal deformation through the quantification of uncertainty deduced from GNSS data

We propose a new method for real-time uncertainty monitoring of earthquake and volcano source models using data from the global navigation satellite system (GNSS) and explore its application concerning observation station placement. The Geospatial Information Authority of Japan operates two main types of GNSS earth observation network system (GEONET) coordinates for crustal deformation monitoring on different time scales: post-processing analysis values and real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD uses the Markov Chain Monte Carlo (MCMC) method termed real-time automatic uncertainty estimation of a coseismic single rectangular model using GNSS data (RUNE) for single rectangular fault model estimation to handle uncertainty. Thus far, no GNSS monitoring system can automatically detect transient crustal deformation events, such as volcanic activity and earthquake swarms, on timescales of a day or less. We extended RUNE and developed a core program for a new monitoring system for earthquake and volcanic source models and their uncertainties. Our program achieved automatic and stable MCMC utilization for rectangular fault, dike, Mogi, and spheroid models by increasing the computational speed, improving search efficiency, and adjusting hyperparameters. The program automatically determines the standard deviation of the likelihood function assuming a normal distribution with weights for each observation station. The calculation time was within 15 s for 1 × 106 samples on a standard 1U server. We assessed the reliability of the developed method using synthetic and observed GNSS data from the 2015 Sakurajima volcanic event. The results were consistent with the assumed model and previous studies and indicated an advantage in automatically quantifying uncertainty in a short computation time. Based on MCMC samples, we developed a new visualization algorithm to indicate areas on a map in which the number of observation stations should be expanded. We assessed the reliability using data from the 2023 Noto Peninsula earthquake [Mj 6.5]. The results indicate that the algorithm is helpful in studying the placement of stations. The above model extensions and their application are essential to achieve a rapid quantitative understanding of disaster events near urban areas and for utilizing this information in emergency response activities.Graphical

A Dynamic Volumetric Heat Source Model for Laser Additive Manufacturing

Melt pool scale models of laser powder bed fusion (LPBF) offer insights into the process-structure-property relationships in additive manufacturing (AM). These models often neglect physical phenomena such as vapor cavity formation and fluid mechanics to reduce computational demands. Instead, volumetric heat source models are used to represent the effects that these phenomena have on the predicted melt pool dimensions. Generally, the dimensions and effective absorption of the volumetric heat source are calibrated to reproduce melt pool dimensions observed in metallographic cross sections taken from single-track experiments on bare plate. However, the transient nature of LPBF often deviates the melt pool dimensions from the assumed steady-state conditions of single-track experiments, motivating the need for a volumetric heat source model that more generally considers the dynamic relationship between melt pool shape and laser-material interactions. Here, we introduce a two-parameter volumetric heat source model that integrates several existing models into a generalized mathematical expression, providing independent control over the radial heat distribution via the parameter k and the volumetric shape of the heat source via the parameter m. This parameterization enables the calibration of melt pool shape predictions through simultaneous adjustment of these parameters, while keeping the radial heat source dimensions consistent with the experimental spot size (D4σ) and constraining the heat source depth and absorption to physically derived expressions for cavities. Consequently, the proposed volumetric heat source model adapts to changes in the local melt pool conditions due to scanning strategy and part geometry by dynamically adjusting the heat source depth and absorption. We demonstrate the capabilities of the proposed model through comparisons with a collection of experiments from the Additive Manufacturing Benchmark (AMBench).

Incorporating meander to account for the impact of low winds in area source modeling; AERMOD as a case study

ABSTRACT A variety of sources of pollutant emissions can be represented as area sources. These include manure lagoons, landfills, wastewater treatment ponds, and highways. A group of point sources can also be treated as an area source. The impact of an area source is usually computed by representing the area source as a set of line sources perpendicular to the wind direction. As for point sources, the Gaussian horizontal concentration distribution used to compute the contributions of the line sources is likely to overestimate ground-level concentrations when the wind speed is comparable to the standard deviation of the horizontal velocity fluctuations. A variety of methods are used to mitigate this overestimation under these conditions, referred to as meander. As an example of one these approaches, we examine that of AERMOD, EPA’s regulatory model. AERMOD includes meander in modeling the impact of point and volume sources, but has not yet incorporated it into AERMOD’s area source algorithm. This paper describes an approach to include meander in AERMOD’s area source algorithm and demonstrates its impact on concentrations associated with area sources. Implications: Inclusion of wind direction meander in modeling dispersion when the wind speed is low is important in ensuring that AERMOD does not overestimate concentrations under these conditions. In view of the importance of area sources of pollution, the results presented in this paper represent a potential enhancement of AERMOD’s ability to estimate the upper end of the concentration distribution, which forms the basis of the regulatory acceptance of the model.

Relativistic diffusion model for hadron production in p -Pb collisions at energies available at the CERN Large Hadron Collider

We investigate charged-hadron production in relativistic heavy-ion collisions of asymmetric systems within a nonequilibrium-statistical framework. Calculated centrality-dependent pseudorapidity distributions for p-Pb collisions at sNN=5.02 and 8.16 TeV are compared with data from the Large Hadron Collider. Our approach combines a relativistic diffusion model with formulations based on quantum chromodynamics while utilizing numerical solutions of a Fokker-Planck equation to account for the shift and broadening of the fragmentation sources for particle-production with respect to the stopping (net-baryon) rapidity distributions. To represent the centrality dependence of charged-hadron production in asymmetric systems over a broad region of pseudorapidities, the consideration and precise modeling of the fragmentation sources, along with the central gluon-gluon source, is found to be essential. Specifically, this results in an inversion of the particle-production amplitude from backward to forward dominance when transitioning from central to peripheral collisions, in agreement with recent ATLAS and ALICE p-Pb data at sNN=5.02TeV. Published by the American Physical Society 2024

Modeling Cs-137 Migration in Groundwater due to Low Probability Event of Leaching from Near Surface Disposal Facility for Low Level Solid Radioactive Waste

In this paper, we present impact of Cesium-137 (Cs-137) disposal in a Near Surface Disposal Facility (NSDF) with respect to groundwater migration and its consumption for drinking. We employ the Multiple Area Source Model for simulating the fate of radionuclide in groundwater to evaluate its potential contamination due to extremely low probability event of leaching from the NSDF. The assessment conservatively assumes direct interaction between disposal trenches and the underlying aquifer neglecting the barrier provided by the unsaturated zone. Temporal profiles of concentration of Cs-137 at different down flow distances in groundwater are obtained over a period extending several years post-disposal. The results indicate that even under such a worst-case conservative scenario and for an annual disposal rate of 3 TBq/y for a period of 50y, the maximum Cs-137 concentration in groundwater remains significantly below the safe drinking water threshold of 10 Bq/L recommended by the World Health Organization. It is found that amount of Cs-137 generally disposed in NSDF is significantly lower than the allowable disposal limit corresponding to concentration defined for safe drinking water limit.