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An efficient three-dimensional mesh quality optimization method based on gradient-enhanced probabilistic model

COMPUTER PHYSICS COMMUNICATIONS 2025年 312卷

作者： Deng, Longwei Yin, Junhui He, Qing Cao, Xinyu Zhang, Chaoyang Cui, Junhao Li, Bin Univ Elect Sci & Technol China Sch Elect Sci & Engn Natl Key Lab Sci & Technol Vacuum Elect Key Lab Large Scale Electromagnet Ind SoftwareMin Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518000 Peoples R China

As a cornerstone of numerical simulations, mesh generation establishes the initial discrete model required for simulations, and the quality of the mesh significantly impacts the accuracy of the analysis results. However, the initial mesh elements generated by automated mesh generators often do not meet the stringent requirements of numerical simulations due to their poor mesh quality. This paper proposes an efficient three-dimensional tetrahedral mesh quality optimization method based on gradient-enhanced probabilistic model. The proposed method includes a preprocessing step that first solves for the steepest descent direction and optimal step length of nodes, allowing for the rapid optimization of early node movement and placement, subsequently completing the initial relocation of nodes. By establishing a probabilistic model for determining the optimal node positions and creating a memoryless stochastic process, the method ensures good convergence speed and accuracy as the node positions approach their optimal solutions. Therefore, the proposed method not only accelerates the overall optimization efficiency but also enhances mesh quality, achieving a balanced improvement between smoothing efficiency and mesh quality. This paper validates the proposed method on both three-dimensional tetrahedral meshes and surface meshes, and develops a parallel version, demonstrating the method's broad applicability and strong optimization capability. Through ablation study and comparisons with classic methods, it is shown that the proposed method outperforms traditional methods in both optimization efficiency and mesh quality. The GitHub repository link is: https://***/suyi-92/***. And the input files can be found at: https://***/drive/folders/1ziiWzmorx82NiVJPxWI0yoBrPpk_Lzrg?usp=sharing

关键词： Tetrahedral elements Mesh quality probabilistic model Optimization-based smoothing Stochastic process

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Neurophysiological data augmentation for EEG-fNIRS multimodal features based on a denoising diffusion probabilistic model

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2025年 261卷 108594-108594页

作者： Chen, Li Yin, Zhong Gu, Xuelin Zhang, Xiaowen Cao, Xueshan Zhang, Chaojing Li, Xiaoou Shanghai Univ Med & Hlth Sci Coll Med Instruments Shanghai 201318 Peoples R China Shanghai Yangpu Mental Hlth Ctr Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Shanghai Qingdong Drug Rehabil Ctr Shanghai 201701 Peoples R China

Background and objective: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models. Methods: In this study, we proposed an EEG-fNIRS data augmentation framework based on the combination of denoising diffusion probabilistic model (DDPM) and adding Gaussian noise (EFDA-CDG), for enhancing the performance of hybrid BCI systems. Firstly, we unified the temporal and spatial dimensions of EEG and fNIRS by manually extracting features and spatial mapping interpolation to create EEG-fNIRS joint distribution samples. Then, the DDPM generative model was combined with the traditional method of adding Gaussian noise to provide richer training data for the classifier. Finally, we constructed a classification module that applies EEG feature attention and fNIRS terrain attention to improve classification accuracy. Results: In order to evaluate the effectiveness of EFDA-CDG framework, experiments were conducted and fully validated on three publicly available databases and one self-collected database. In the context of a participantdependent training approach, our method achieves accuracy rates of 82.02% for motor imagery, 91.93% for mental arithmetic, and 90.54% for n-back tasks on public databases. Additionally, our method boasts an accuracy rate of 97.82% for drug addiction discrimination task on the self-collected database. Conclusions: EFDA-CDG framework successfully facilitates data augmentation, thereby enhancing the performance of EEG-fNIRS hybrid BCI systems.

关键词： Electroencephalogram Functional near infrared spectroscopy Multimodal brain computer interface Denoising diffusion probabilistic model Data augmentation

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probabilistic model for the growth of thesauri

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ACTA APPLICANDAE MATHEMATICAE 2001年第3期67卷 237-252页

作者： Hazewinkel, M Rudzkis, R CWI NL-1090 GB Amsterdam Netherlands Inst Math & Informat LT-2600 Vilnius Lithuania

The paper deals with a mathematical model which describes how the collection of key phrases (and key words) evolves as a field of science develops. The experimental material is based on statistical observations on the sets of key phrases which have been assigned to papers in representative major journals in the field in question. Asymptotic properties of the model are considered, as well as estimators for the parameters of the model with particular emphasis on estimators that can indicate at what stage a collection of key phrases can be assumed as complete for the field in question at a given moment in time.

关键词： thesaurus index growth of thesauri probabilistic model asymptotic property statistical estimator growth model

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probabilistic model-based multi-integration formulas for quantifying a generalized minimal cut sequence

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2015年第1期229卷 73-82页

作者： Ge, Daochuan Zhang, Ruoxing Chou, Qiang Yang, Yanhua Shanghai Jiao Tong Univ Sch Nucl Sci & Engn Shanghai 200240 Peoples R China State Nucl Power Technol Corp Software Dev Ctr Beijing Peoples R China

Many sequence- and function-dependent failure behaviors may exist in fault-tolerant systems, in which the failure of the systems depends not only on the combination of basic events but also on their failure sequences. Such systems can be modeled by dynamic fault tree with Priority AND gate, Spare gate, Sequence Enforcing gate and Functional Dependence gate. The existing analytical methods for analyzing these dynamic gates are mainly Markov-based or inclusion-exclusion-based approaches. Compared with Markov approaches, the inclusion-exclusion-based method presents notable advantages. However, to the author's knowledge, no references by far have presented general multi-integration formulas applicable for solving a generalized minimal cut sequence, which is an unavoidable problem in inclusion-exclusion-based method. In this article, new multi-integration formulas are proposed based on traditional probabilistic models, which are applicable for solving a generalized minimal cut sequence as well as any dynamic gate. The rigorous derivation processes are given. The application and advantages of our proposed approach are demonstrated through a case study and the results indicate our proposed approach is reasonable.

关键词： Dynamic fault tree generalized minimal cut sequence probabilistic model quantitative analysis multi-integration formulas

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probabilistic model of iodine release for steam generator tube primary-to-secondary leakage induced by main steam-line break

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NUCLEAR TECHNOLOGY 1998年第1期121卷 70-80页

作者： Jackson, PS Williams, PJ Tetra Engn Grp Inc Weatogue CT 06089 USA Entergy Operat Inc Arkansas Nucl 1 Russellville AR 72801 USA

Most commercial pressurized water reactors with alloy 600 steam generator tubes are susceptible to stress-induced corrosion at locations such as the tube sheet transition, the tube-to-tube support structure interface, U-bend regions with high localized stresses, and to a lesser extent, free-span locations between supports where deposits or manufacturing defects have caused accelerated local attack. Under postulated main steam-line break (MSLB) accident conditions (and in rare instances during normal operation), some leakage of reactor coolant inventory through these cracks occurs. The result is an iodine source term to the environment. A simplified probabilistic iodine release model has been developed that is different from previous conservative deterministic models, which were developed for the routine steam generator tube rupture analysis, which is performed as part of a plant's safety analysis. The model described herein was developed to calculate the probability that the iodine release for MSLB-induced steam generator leakage will result in thyroid and whole body doses that do nor exceed the criteria in 10CFR100 for the projected condition of the plant's steam generator tubes after a specified period of full-power operation. This simplified probabilistic model treats the intrinsic statistical nature of the projected population of degraded tubes, the probability of leakage for multiple degradation mechanisms, and the probability distributions for iodine release for a preexisting spike and a coincident spike. Results from applying this methodology to data from a plant with substantial steam generator degradation indicate that steam generators with multiple degradation mechanisms can be operated safely for normal operating cycles. Safely, in this case, means without a significant probability of exceeding thyroid and whole body dose criteria under normal operation and postulated accident conditions.

关键词： degraded steam generators iodine release probabilistic model

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probabilistic model to forecast earthquakes in the Zemmouri (Algeria) seismoactive area on the basis of moment magnitude scale distribution functions

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ACTA GEOPHYSICA 2013年第1期61卷 60-83页

作者： Baddari, Kamel Makdeche, Said Bellalem, Fouzi Phys Lab Earth UMBB Boumerdes Algeria Dept Math UMBB Lab LIMOSE UMBB Boumerdes Algeria Res Ctr Astron Astrophys & Geophys CRAAG Algiers Algeria

Based on the moment magnitude scale, a probabilistic model was developed to predict the occurrences of strong earthquakes in the seismoactive area of Zemmouri, Algeria. Firstly, the distributions of earthquake magnitudes M (i) were described using the distribution function F (0)(m), which adjusts the magnitudes considered as independent random variables. Secondly, the obtained result, i.e., the distribution function F (0)(m) of the variables M (i) was used to deduce the distribution functions G(x) and H(y) of the variables Y (i) = Log M (0,i) and Z (i) = M (0,i) , where (Y (i) ) (i) and (Z (i) ) (i) are independent. Thirdly, some forecast for moments of the future earthquakes in the studied area is given.

关键词： probabilistic model Zemmouri seismoactive area seismic magnitude scale prediction

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probabilistic model for corrosion degradation of tanker and bulk carrier

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MARINE STRUCTURES 2018年 61卷 309-325页

作者： Lampe, Joerg Hamann, Rainer DNV GL SE Hamburg Germany

In this paper a process is presented for predicting corrosion at ship ageing structural elements by considering inspection results. The degradation process is described by a detailed corrosion model for each plate and stiffener depending on their category. A category is defined by a specific compartment, location and structural type. This basic model can be further refined by means of influencing factors and is individually adjusted using measurements or information from inspection or survey data, if available. Furthermore, the proposed corrosion model does include a probabilistic representation of uncertainty by a Weibull distribution.

关键词： Corrosion model probabilistic model Measurement based Long-term estimation

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probabilistic model for 3D interactive segmentation

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COMPUTER VISION AND IMAGE UNDERSTANDING 2016年 151卷 47-60页

作者： Hershkovich, Tsachi Shalmon, Tamar Shitrit, Ohad Halay, Nir Menze, Bjoern H. Dolgopyat, Irit Kahn, Itamar Shelef, Ilan Raviv, Tammy Riklin Ben Gurion Univ Negev Elect & Comp Engn Dept Beer Sheva Israel Ben Gurion Univ Negev Zlotowski Ctr Neurosci Beer Sheva Israel Soroka Med Ctr Dept Radiol Beer Sheva Israel Technion Rappaport Fac Med Haifa Israel Tech Univ Munich Dept Comp Sci Munich Germany

Fully-automated segmentation algorithms offer fast, objective, and reproducible results for large data collections. However, these techniques cannot handle tasks that require contextual knowledge not readily available in the images alone. Thus, the supervision of an expert is necessary. We present a generative model for image segmentation, based on a Bayesian inference. Not only does our approach support an intuitive and convenient user interaction subject to the bottom-up constraints introduced by the image intensities, it also circumvents the main limitations of a human observer-3D visualization and modality fusion. The user "dialogue" with the segmentation algorithm via several mouse clicks in regions of disagreement, is formulated as a continuous probability map, that represents the user's certainty to whether the current segmentation should be modified. Considering this probability map as the voxel-vise Bernoulli priors on the image labels allows spatial encoding of the user-provided input. The method is exemplified for the segmentation of cerebral hemorrhages (CH) in human brain CT scans;ventricles in degenerative mice brain MRIs, and tumors in multi-modal human brain MRIs and is shown to outperform three interactive, state-of-the-art segmentation methods in terms of accuracy, efficiency and user-workload. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Image segmentation User interaction probabilistic model MR & CT Brain imaging Modality fusion

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probabilistic model of the minimum effective cross-section area of non-uniform corroded steel bars

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CONSTRUCTION AND BUILDING MATERIALS 2019年 216卷 227-238页

作者： Gao, Xiangling Pan, Yutong Ren, Xiaodan Tongji Univ Dept Struct Engn Shanghai 200092 Peoples R China

In general, the corroded reinforced concrete members is induced by the natural environment in existing buildings or by the two artificial accelerated corrosion such as electrochemical method and artificial simulated environment. The deterioration of yield strength of corroded steel bars is mainly caused by the decrease of the effective cross-section area, the randomness and the variability of the corrosion. The position of the yield point occurs at the minimum effective cross-section area. Through the statistical analysis of 1706 groups of experimental data on yield strength of corroded steel bars, the relationship between the minimum effective cross-section area of non-uniform corroded steel bars and the average corrosion ratio under three corrosion conditions has been established. After that, a probabilistic model of minimum effective cross-section area of corroded steel bars has been proposed and, meanwhile, a correction coefficient is suggested to adjust the deviation of the yield strength between the average corrosion ratios of artificial accelerated corrosion and natural environment corrosion. Based on the presented probabilistic model, a reinforced concrete beam member under natural environment was analyzed. It is found that the flexure-shear competition happened during its service life, that is, the failure mode may change because of the non-uniform corrosion. Therefore, the non-uniform corrosion of the corroded RC members should be taken into account seriously in structural design. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Steel bar Corrosion probabilistic model Effective cross-section area Beam member Flexure-shear competition

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probabilistic model of ammonia and toxicity status for urban lake

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE 2001年第5期127卷 337-347页

作者： Gelda, RK Effler, SW O'Donnell, SM Upstate Freshwater Inst Syracuse NY 13214 USA

The development of a probabilistic modeling framework to conduct a priori simulations (forecasts) of the concentration of total ammonia (T-NH3) and status with respect to toxicity standards and criteria is documented for ammonia-polluted Onondaga Lake, Syracuse, N.Y. This framework utilizes a previously tested mass balance nitrogen model, long-term (27 year) simulations, and the Monte Carlo technique. Variability is accommodated, based on long-term records, for several parameters and forcing conditions, including (1) tributary runoff, (2) in-lake vertical mixing, (3) in-lake rate of nitrification, (4) T-NH3 concentration in the effluent of a wastewater-treatment plant, (5) lake pH, and (6) lake temperature. The probabilistic framework per-formed well in simulating the broad variations in T-NH3 concentrations observed in the lake over a 6-year period (1989-1994). The major sources of the observed interannual variability in the lake's T-NH3 pool are demonstrated to be natural variations in runoff and the irregular occurrence of "nitrification events" during fall mixing. Predictions are presented in the form of frequency distributions of T-NH3 concentrations and violations/exceedances of the standard/criterion, consistent with the format of U.S. Environmental Protection Agency toxicity criteria. Application of the probabilistic model is recommended for the total maximum daily load analysis that will establish the T-NH3 limit for the wastewater-treatment plant effluent.

关键词： lakes Status Treatment plants Runoff total maximum daily load probabilistic model Waste Water ammonia Frequency distribution Toxicity

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