Multi-resolution Model Research Articles

The effect of different climate and air quality policies in China on in situ ozone production in Beijing

Abstract. In recent years, clean air policies have led to reductions in air pollution across China. Alongside this, emerging carbon neutrality (CN) policies that aim to address the impacts of climate change may also deliver air quality (AQ) co-benefits or climate penalties. Different CN policies will lead to different changes in volatile organic compound (VOC), NOx and particulate matter (PM) emissions, which will in turn impact the photochemical production of secondary pollutants such as ozone (O3). It is currently unclear how different combinations of AQ and CN policies may impact in situ O3 production across China in the future. A detailed chemical box model incorporating the Master Chemical Mechanism was developed to investigate the impact of combined AQ and CN policies on O3 formation in Beijing. The Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC) and the Dynamic Projection model for Emissions in China (DPEC) were used to estimate future pollutant mixing ratios, relative to ambient observations of 35 VOCs, NOx, CO and aerosol surface area (ASA) during the APHH-Beijing 2017 summer campaign. The most ambitious policy scenario, “Ambitious Pollution 1.5D Goals”, led to the largest reduction in O3 production by 2060 but was not the most impactful scenario for reducing O3 production between 2030–2045. Larger reductions were observed under the “Ambitious Pollution Neutral Goals” policy, which focuses on achieving net zero by 2060. O3 production was found to be most sensitive to changes in the OLE2 group of VOCs (alkenes where kOH>7×104 ppm−1 min−1; a 5 % increase in OLE2 increased simulated O3 production by 1.12 %). However, reducing less reactive but higher concentration species in Beijing (such as short-chain alkanes) led to larger reductions in O3 production under all scenarios. O3 production was not sensitive to changes in ASA, with a 69 % decrease in ASA leading to a change of <1 % in O3. However, doubling biogenic VOCs in the model further increased O3 production in 2060 under all future scenarios by up to 18 %, indicating that the influence of future climate-induced changes in biogenic emissions may have a significant impact on in situ O3 formation in Beijing. This study highlights that the emission trajectories of certain specific VOCs are highly influential in determining possible future O3 air quality effects that may arise from increasing ambient temperatures and decarbonisation in Beijing.

Numerical implementation of an extended multi-resolution SPH-PD coupling model for fluid-structure interaction problem with impacting failure

Considering the fluid-structure impacting failure problems with large size, this paper presents an extended multi-resolution smoothed particle hydrodynamics (SPH)-peridynamics (PD) coupling model. The influence domain and the interpolation smoothing length are innovatively introduced to reconstruct governing equations for interactions between particles of diverse resolutions, ensuring system-wide momentum conservation. Then, an adaptive multi-level cell neighborhood search (AMCNS) algorithm is presented, designed to reduce the time of neighborhood search in multi-resolution simulations. Several 2D and 3D validation tests demonstrate the accuracy of the multi-resolution SPH-PD model in describing solid deformation and fluid impact pressure. The multi-resolution SPH-PD model significantly enhances computational efficiency and can depict the process of structural failure under conditions where a wide difference in scale between waves and structures.

Graph-learning approach to combine multiresolution seismic velocity models

SUMMARY The resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations. Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probabilistic graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Further, by considering physical information (such as ray path density), we introduce physics-informed probabilistic graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. Transferring (segmented) distribution information from the HR regions enhances the details in the LR regions by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The model efficacy is underscored by the deviation between observed and calculated traveltimes along the boundaries between HR and LR regions, 38 per cent less than obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation.

Uncertainty estimation and evaluation of deformation image registration based convolutional neural networks

Objective. Fast and accurate deformable image registration (DIR), including DIR uncertainty estimation, is essential for safe and reliable clinical deployment. While recent deep learning models have shown promise in predicting DIR with its uncertainty, challenges persist in proper uncertainty evaluation and hyperparameter optimization for these methods. This work aims to develop and evaluate a model that can perform fast DIR and predict its uncertainty in seconds. Approach. This study introduces a novel probabilistic multi-resolution image registration model utilizing convolutional neural networks to estimate a multivariate normal distributed dense displacement field (DDF) in a multimodal image registration problem. To assess the quality of the DDF distribution predicted by the model, we propose a new metric based on the Kullback–Leibler divergence. The performance of our approach was evaluated against three other DIR algorithms (VoxelMorph, Monte Carlo dropout, and Monte Carlo B-spline) capable of predicting uncertainty. The evaluation of the models included not only the quality of the deformation but also the reliability of the estimated uncertainty. Our application investigated the registration of a treatment planning computed tomography (CT) to follow-up cone beam CT for daily adaptive radiotherapy. Main results. The hyperparameter tuning of the models showed a trade-off between the estimated uncertainty’s reliability and the deformation’s accuracy. In the optimal trade-off, our model excelled in contour propagation and uncertainty estimation (p <0.05) compared to existing uncertainty estimation models. We obtained an average dice similarity coefficient of 0.89 and a KL-divergence of 0.15. Significance. By addressing challenges in DIR uncertainty estimation and evaluation, our work showed that both the DIR and its uncertainty can be reliably predicted, paving the way for safe deployment in a clinical environment.

A multi-resolution ensemble model of three decision-tree-based algorithms to predict daily NO2 concentration in France 2005–2022

Understanding and managing the health effects of Nitrogen Dioxide (NO2) requires high resolution spatiotemporal exposure maps. Here, we developed a multi-stage multi-resolution ensemble model that predicts daily NO2 concentration across continental France from 2005 to 2022. Innovations of this work include the computation of daily predictions at a 200 m resolution in large urban areas and the use of a spatio-temporal blocking procedure to avoid data leakage and ensure fair performance estimation. Predictions were obtained after three cascading stages of modeling: (1) predicting NO2 total column density from Ozone Monitoring Instrument satellite; (2) predicting daily NO2 concentrations at a 1 km spatial resolution using a large set of potential predictors such as predictions obtained from stage 1, land-cover and road traffic data; and (3) predicting residuals from stage 2 models at a 200 m resolution in large urban areas. The latter two stages used a generalized additive model to ensemble predictions of three decision-tree algorithms (random forest, extreme gradient boosting and categorical boosting). Cross-validated performances of our ensemble models were overall very good, with a ten-fold cross-validated R2 for the 1 km model of 0.83, and of 0.69 for the 200 m model. All three basis learners participated in the ensemble predictions to various degrees depending on time and space. In sum, our multi-stage approach was able to predict daily NO2 concentrations with a relatively low error. Ensembling the predictions maximizes the chance of obtaining accurate values if one basis learner fails in a specific area or at a particular time, by relying on the other learners. To the best of our knowledge, this is the first study aiming to predict NO2 concentrations in France with such a high spatiotemporal resolution, large spatial extent, and long temporal coverage. Exposure estimates are available to investigate NO2 health effects in epidemiological studies.

The simulation of droplet impact on liquid film evaporation in horizontal falling film evaporator based on smoothed particle hydrodynamics

PurposeIn this paper, the complex flow evaporation process of droplet impact on the liquid film in a horizontal falling film evaporator is numerically studied based on smoothed particle hydrodynamics (SPH) method. The purpose of this paper is to present the mechanism of the water treatment problem of the falling film evaporation for the high salinity mine water in Xinjiang region of China.Design/methodology/approachTo effectively characterize the phase transition problem, the particle splitting and merging techniques are introduced. And the particle absorbing layer is proposed to improve the nonphysical aggregation phenomenon caused by the continuous splitting of gas phase particles. The multiresolution model and the artificial viscosity are adopted.FindingsThe SPH model is validated qualitatively with experiment results and then applied to the evaporation of the droplet impact on the liquid film. It is shown that the larger single droplet initial velocity and the smaller single droplet initial temperature difference between the droplet and liquid film improve the liquid film evaporation. The heat transfer effect of a single droplet is preferable to that of multiple droplets.Originality/valueA multiphase SPH model for evaporation after the droplet impact on the liquid film is developed and validated. The effects of different factors on liquid film evaporation, including single droplet initial velocity, single droplet initial temperature and multiple droplets are investigated.

Anatomy-guided multi-resolution image reconstruction in PET

Objective. In this paper, we propose positron emission tomography image reconstruction using a multi-resolution triangular mesh. The mesh can be adapted based on patient specific anatomical information that can be in the form of a computed tomography or magnetic resonance imaging image in the hybrid imaging systems. The triangular mesh can be adapted to high resolution in localized anatomical regions of interest (ROI) and made coarser in other regions, leading to an imaging model with high resolution in the ROI with clearly reduced number of degrees of freedom compared to a conventional uniformly dense imaging model. Approach. We compare maximum likelihood expectation maximization reconstructions with the multi-resolution model to reconstructions using a uniformly dense mesh, a sparse mesh and regular rectangular pixel mesh. Two simulated cases are used in the comparison, with the first one using the NEMA image quality phantom and the second the XCAT human phantom. Main results. When compared to the results with the uniform imaging models, the locally refined multi-resolution mesh retains the accuracy of the dense mesh reconstruction in the ROI while being faster to compute than the reconstructions with the uniformly dense mesh. The locally dense multi-resolution model leads also to more accurate reconstruction than the pixel-based mesh or the sparse triangular mesh. Significance. The findings suggest that triangular multi-resolution mesh, which can be made patient and application specific, is a potential alternative for pixel-based reconstruction.

MurSS: A Multi-Resolution Selective Segmentation Model for Breast Cancer.

Accurately segmenting cancer lesions is essential for effective personalized treatment and enhanced patient outcomes. We propose a multi-resolution selective segmentation (MurSS) model to accurately segment breast cancer lesions from hematoxylin and eosin (H&E) stained whole-slide images (WSIs). We used The Cancer Genome Atlas breast invasive carcinoma (BRCA) public dataset for training and validation. We used the Korea University Medical Center, Guro Hospital, BRCA dataset for the final test evaluation. MurSS utilizes both low- and high-resolution patches to leverage multi-resolution features using adaptive instance normalization. This enhances segmentation performance while employing a selective segmentation method to automatically reject ambiguous tissue regions, ensuring stable training. MurSS rejects 5% of WSI regions and achieves a pixel-level accuracy of 96.88% (95% confidence interval (CI): 95.97-97.62%) and mean Intersection over Union of 0.7283 (95% CI: 0.6865-0.7640). In our study, MurSS exhibits superior performance over other deep learning models, showcasing its ability to reject ambiguous areas identified by expert annotations while using multi-resolution inputs.

Identification of NOx emissions and source characteristics by TROPOMI observations – A case study in north-central Henan, China

With the development of industries, air pollution in north-central Henan is becoming increasingly severe. The TROPOspheric Monitoring Instrument (TROPOMI) provides nitrogen dioxide (NO2) column densities with high spatial resolution. Based on TROPOMI, in this study, the nitrogen oxides (NOx) emissions in north-central Henan are derived and the emission hotspots are identified with the flux divergence method (FDM) from May to September 2021. The results indicate that Zhengzhou has the highest NOx emissions in north-central Henan. The most prominent hotspots are in Guancheng Huizu District (Zhengzhou) and Yindu District (Anyang), with emissions of 448.4 g/s and 300.3 g/s, respectively. The Gaussian Mixture Model (GMM) is applied to quantify the characteristics of emission hotspots, including the diameter, eccentricity, and tilt angle, among which the tilt angle provides a novel metric for identifying the spatial distribution of pollution sources. Furthermore, the results are compared with the CAMS global anthropogenic emissions (CAMS-GLOB-ANT) and Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC), and they are generally in good agreement. However, some point sources, such as power plants, may be missed by both inventories. It is also found that for emission hotspots near transportation hubs, CAMS-GLOB-ANT may not have fully considered the actual traffic flow, leading to an underestimation of transportation emissions. These findings provide key information for the accurate implementation of pollution prevention and control measures, as well as references for future optimization of emission inventories. Consequently, deriving NOx emissions from space, quantifying the characteristics of emission hotspots, and combining them with bottom-up inventories can provide valuable insights for targeted emission control.

Effects of source space resolution, randomization and averaging in focal EEG source localization: Estimation of forward and inverse errors

In this article, we study randomized multiresolution forward modeling and source localization in electroencephalography (EEG). Our goal is to determine numerically the effects of variable source space resolution on both forward modeling and source localization perspectives and to examine how it affects the detection of the sources at different depths inside the brain. Our concentration is in the main principles of multiresolution methods: random source space generation and the reconstruction obtained via averaging over multiple maximum a posteriori (MAP) estimations obtained at a finite set of different sub source spaces, and their suggested enhancement in distinguishability of weak components. The motivation of averaging is to reduce the discretization and optimization error and marginalize other variable factors. Due to the unguided randomized source space generation, multiple samples of the same size source spaces are needed. For this very reason, a large number of source spaces of multiple source counts are used to obtain statistically feasible results in this paper. This study verifies the described postulates of the multiresolution technique by showing averaging to be crucial in order to obtain a robust projection to a lower resolution, where the lead field matrix is overdetermined. From the viewpoint of source localization, the multiresolution approach was found to improve the reconstruction accuracy obtained with each resolution level below the highest and observed to be necessary to achieve an accuracy comparable to that of forward modeling.

A parallel multi-resolution Smoothed Particle Hydrodynamics model with local time stepping

Smoothed Particle Hydrodynamics (SPH) model has exhibited remarkable efficacy in addressing strongly nonlinear large deformation problems. However, the expensive computational cost could limit its application. To address this, we propose a novel parallel multi-resolution SPH model with a local time step strategy. This model integrates the multi-resolution model with a Message Passing Interface-based parallelization framework, wherein subdomains discretize the computational domain at varying resolutions. Meanwhile, a local fourth-order Runge-Kutta time integration scheme is introduced, enabling different subdomains to use different time steps. Subdomains of varying resolutions are devoid of overlapping regions, and changes in particle resolution at interfaces are achieved through a dynamic strategy involving particle merging and splitting. Besides that, we conducted a comparative analysis of different SPH discretization formats concerning their impact on the accuracy of multi-resolution particle discretization. Finally, several numerical tests were conducted to validate the accuracy and efficiency of the present multi-resolution SPH model.

Abstract 3514: A deep learning model for detection and characterization of tertiary lymphoid structures in H&amp;E-stained images from pancreatic ductal adenocarcinoma

Abstract Tertiary lymphoid structures (TLSs) are ectopic aggregates of a number of immune cells in nonlymphoid tissues under chronically inflamed environments and cancer. Emergence evidence suggested that TLSs were found in diverse cancers and TLSs have been referred as an independently predictive biomarker for immunotherapy response, especially immune checkpoint inhibitors. Moreover, the density and mature status of TLSs often positively associated with the favorable outcomes in cancers. Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related mortality with low survival rate in the whole world. Here, we developed a multi-resolution machine learning model for detection of TLSs from hema-toxylin and eosin (H&amp;E) stained digital pathology slides as a potential clinical biomarker in PDAC. We selected 120 H&amp;E slides (patients) with a total of 1,896 TLSs manually annotated as immature or mature TLSs by two pathologists. We also used immunohistochemistry (IHC) for CD20 and CD23 markers as a reference standard for H&amp;E-based manual annotations. Our model mainly included detection and segmentation of individual TLSs. First, we used a transformer convolutional neural network to build a classification task to detect the presence of immature or mature TLSs. Then the instance segmentation model was applied to quantify the number of lymphocytes per unit square to validate the presence and maturation of TLSs. F1-score, calculated by the harmonic mean of precision (positive predictive value) and recall (sensitivity), was finally used to quantitatively evaluate the performance of model for each H&amp;E slide. In validation dataset with 250 H&amp;E slides (patients), we selected 50 slides to calculate F1-score, suggesting that our machine learning model achieved high F1-scores for detection and classification of immature TLSs (mean = 0.87, range from 0.79 to 0.95), and mature TLSs (mean = 0.92, range from 0.86 to 0.97). We further stratified 370 PDAC patients into three groups with mature TLSs (23%), immature TLSs (16%), and no TLSs (61%), respectively. Combined the clinical information, survival analysis revealed that patients with mature TLS had better survival outcomes than those without TLS (HR = 0.63; 95% CI, 0.44 - 0.90; P = 0.010). We developed a machine learning model that can accurately detect and classify TLSs in PDAC, and also demonstrated the prognosis value of predictive TLSs in H&amp;E slides by automatedly machine learning model. These data highlight the promise of machine learning model for automated identification and quantification of the TLS on H&amp;E slides in PDAC pathology. Citation Format: Chaoxian Zhao, Jidong Jia, Mingxi Lv, Jiayi Feng, Yijun Wang, Yingbin Liu. A deep learning model for detection and characterization of tertiary lymphoid structures in H&amp;E-stained images from pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3514.

An improved time advance method for distributed multi-resolution modelling with interactive deviation characterization

Multi-resolution modelling (MRM) has been increasingly applied in engineering practice. While in a distributed simulation environments, time advance method for MRM is less studied. When the system is running in a relatively low-resolution status, it usually adopts basic time advance modes to save simulation resources. However, these basic modes usually suffer from inaccurate response of interactions and are separate from the resolution switching process. In this paper, we propose an improved time advance method for distributed multi-resolution models. We introduce an interaction table to enable dynamic management of interactions, and decouple the interaction request process from the interaction response process, resulting in a more fine-grained interactive mode. Moreover, the interactive deviation index is introduced to quantitatively characterize the cumulative errors from delayed interactions. This index has been incorporated into resolution switching conditions to provide a more flexible and intuitive way for multi-resolution modelling. Proposed method has been verified on both theoretical cases and practical applications, and can achieve high simulation accuracy with less time consumption.

Field Application of a Novel Multiresolution Multiwell Unconventional Reservoir Simulation: History Matching and Parameter Identification

Summary This study focuses on developing an efficient workflow by integrating a multiresolution simulation model and a multiobjective evolutionary algorithm (MOEA) for application to multiwell unconventional reservoirs. In this approach, hydraulic fractures are represented using a dual porosity, dual permeability system facilitated by an embedded discrete fracture model (EDFM). A novel fast-marching simulation method is used to cut down on computational expenses by an order of magnitude, greatly accelerating the history-matching process. A variety of integrated monitoring technologies were implemented to map out the hydraulic fracture network. Insights into hydraulic fracture locations were gleaned from warm-back analysis of distributed temperature sensing data, and these locations were then assimilated into the simulation model as embedded discrete fractures. For the simulation, a fast-marching-based multiresolution model was used to partition the reservoir into local and shared domains guided by the diffusive-time-of-flight (DTOF) principle. The local domain maintained the original 3D grids near the wells while transforming the remaining area into 1D grids to accelerate the simulation process. Before history matching, a thorough sensitivity analysis was conducted to pinpoint the most impactful parameters. Subsequently, the model was fine-tuned using production data through an MOEA. The most sensitive parameters in history matching were identified as fracture geometry and conductivity, fluid saturations, and rock compressibility in the stimulated reservoir volume (SRV) areas. After history matching, there was a noteworthy reduction in the uncertainty of these tuning parameters. The calibrated parameters are valuable to evaluate the effectiveness of the well completion design. Overall, this work emphasizes the innovative combination of techniques applied, the efficiency gains in the history-matching process, and the scalability of the approach to other oilfield applications.

Multi-Resolution LSTM-Based Prediction Model for Remaining Useful Life of Aero-Engine

Multi-Resolution LSTM-Based Prediction Model for Remaining Useful Life of Aero-Engine

Multi-resolution deep learning characterizes tertiary lymphoid structures and their prognostic relevance in solid tumors

BackgroundTertiary lymphoid structures (TLSs) are dense accumulations of lymphocytes in inflamed peripheral tissues, including cancer, and are associated with improved survival and response to immunotherapy in various solid tumors. Histological TLS quantification has been proposed as a novel predictive and prognostic biomarker, but lack of standardized methods of TLS characterization hampers assessment of TLS densities across different patients, diseases, and clinical centers.MethodsWe introduce an approach based on HookNet-TLS, a multi-resolution deep learning model, for automated and unbiased TLS quantification and identification of germinal centers in routine hematoxylin and eosin stained digital pathology slides. We developed HookNet-TLS using n = 1019 manually annotated TCGA slides from clear cell renal cell carcinoma, muscle-invasive bladder cancer, and lung squamous cell carcinoma.ResultsHere we show that HookNet-TLS automates TLS quantification across multiple cancer types achieving human-level performance and demonstrates prognostic associations similar to visual assessment.ConclusionsHookNet-TLS has the potential to be used as a tool for objective quantification of TLS in routine H&E digital pathology slides. We make HookNet-TLS publicly available to promote its use in research.

Multiresolution Models of DC Traction Power Supply Systems With Reversible Substations

Reversible substations (RSs) permitting bidirectional power flows can recover the regenerative braking energy of trains in DC traction power supply systems (TPSSs), increasing the energy efficiency of railway systems. To predict their effects on system dynamics and energy savings, the paper develops multiresolution models (MRMs) to simulate the RS roles with different fidelities. A high‐resolution model for the transient simulation replicates a particular topology where a three‐level voltage source inverter is connected to the secondary winding of an existing 12‐pulse rectifier transformer and regulated to keep a constant DC voltage in the inverting mode. Furthermore, it can model the transient effects of pantograph‐to‐line arcing by inserting arc voltage profiles at the train’s input stage. To increase the computation speed in the long‐term energy flow simulation, a low‐resolution model simplifies the rectifiers into a series connection of a diode and a controlled voltage source depicting their nonlinear output characteristics and then places a DC voltage source in parallel to form a reverse path for braking power recovery. In addition, nonlinear conversion efficiencies are introduced to calculate energy flows across substations. The MRMs are tested based on a 1.5 kV DC TPSS and discussed alongside system dynamics under normal operation or pantograph arcing and the consistencies between different models. The RS using bidirectional voltage source converters only is additionally modelled to compare the technical performance of the two topologies in terms of system dynamics and energy efficiencies.

Construction and realization of simulation training environment based on AI-assisted simulation of real scenarios of dispatching and commanding operations

Abstract Dispatch command business is an essential and important part of modern society's production and life. With the development of science and technology, the use of AI-assisted construction of simulation training environments is of enormous significance to the development and improvement of the dispatching command business. The study facilitates the acquisition and preprocessing of data from the three-dimensional scene used in the scheduling command simulation training, refines the details of the simulation environment using a multi-resolution hierarchical detail model, and enables the creation of a simulation training environment for the scheduling command using three-dimensional modeling and optimization technology. In this paper, the dispatching system trained in a simulation environment is better than other dispatching systems in terms of dispatching accuracy, starting difficulty, and feedback effectiveness. The satisfaction rate for scheduling demand on different subway lines is significantly better than other scheduling algorithms. As the sampling area expands, the FID and NRMSE values of the generated real map decrease, but the PSNR and NRMSE values increase. This paper generates a dispatching command simulation training environment with a fidelity level of “excellent” and a simulation quality that exhibits a high degree of realism and credibility.

Application research of interior space design elements based on image processing

In order to quantitatively analyze the application performance of elements in interior space design, a detection method of texture visual features of elements distribution images in interior space design based on image processing and texture edge contour features detection is proposed. A multi-resolution visual information collection model for detecting the texture visual characteristics of element distribution images in indoor space design is constructed, and a visual characteristic identification model for element application configuration in indoor space design is established. Multi-texture grid block area combination design method is adopted to realize the fusion of element application configuration visual images in indoor space design. Through the edge feature detection method of texture grid area, Establish a visual parameter identification model of element application configuration in interior space design, extract the visual information feature quantity of element application configuration in interior space design, adopt Harris corner feature matching method, realize distributed detection of texture edge contour features of element application configuration visual features in interior space design, and fuse detection of high-resolution texture information through combined filtering and collaborative filtering. The collection and optimization of visual features of element application configuration in interior space design are realized, and the distributed detection algorithm of texture edge contour features is adopted to improve the convergence level of detection of texture visual features of element distribution images in interior space design, and the application of elements in interior space design based on image processing is realized. The simulation results show that this method has good resolution and strong visual expression ability in extracting the texture visual features of element distribution images in interior space design, which improves the quantitative analysis ability of elements in interior space design.

Multi-Resolution Model Compression for Deep Neural Networks: A Variational Bayesian Approach

Multi-Resolution Model Compression for Deep Neural Networks: A Variational Bayesian Approach